Reliability of Medicare Hospital Discharge Records : Report of a Study [1 ed.] 9780309577830
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Copyright © 1977. National Academies Press. All rights reserved.
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i
RELIABILITY OF MEDICARE HOSPITAL DISCHARGE RECORDS
INSTITUTE OF MEDICINE
Final Report November 1977 Supported by U.S. Department of Health, Education, and Welfare Contract No. SSA 600-76-0159 ERRATUM
See page ix -- last sentence should read:
The committee hopes that this report may be reviewed as one step toward the eventual creation of such a system.
National Academy of Sciences Washington, D.C.
Copyright © 1977. National Academies Press. All rights reserved.
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ii
NOTICE The project that is the subject of this report was approved by the Governing Board of the National Research Council, whose members are drawn from the Councils of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. The members of the Committee responsible for the report were chosen for their special competences and with regard for appropriate balance. This report has been reviewed by a group other than the authors according to procedures approved by a Report Review Committee consisting of members of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. Supported by U.S. Department of Health, Education, and Welfare Contract No. 600-76-0159. The Institute of Medicine was chartered in 1970 by the National Academy of Sciences to enlist distinguished members of appropriate professions in the examination of policy matters pertaining to the health of the public. In this, the Institute acts under both the Academy's 1863 Congressional charter responsibility to be an advisor to the Federal Government, and its own initiative in identifying issues of medical care, research, and education. Publication IOM-77-05
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iii
NATIONAL ACADEMY OF SCIENCES 2101 CONSTITUTION AVENUE WASHINGTON, D. C. 20418 INSTITUTE OF MEDICINE November 28, 1977 Clifton R. Gaus, Sc.D. Acting Associate Administrator for Policy, Planning, and Research Room 5082 DHEW Switzer Building 330 C Street, S.W. Washington, D.C. 20201 Dear Dr. Gaus:
I am pleased to present to the Health Care Financing Administration the final report of the study of the reliability of Medicare hospital discharge records conducted by the Institute of Medicine, National Academy of Sciences, under contract no. 600-76-0159. The study was intended to assess the reliability of information describing hospital utilization by Medicare beneficiaries which is obtained as a by-product of the Medicare administrative recordkeeping system. The purpose was to determine the usefulness of such information for evaluating the impact of Professional Standards Review Organizations; however, the results have broader implications for the collection and use of national health statistics. We will be happy to discuss the report in greater detail with you or members of your staff. Sincerely yours,
David A. Hamburg, M.D. President Institute of Medicine
Enclosure
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iv
Copyright © 1977. National Academies Press. All rights reserved.
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Institute of Medicine Division of Health Care Services RELIABILITY OF MEDICARE HOSPITAL DISCHARGE RECORDS TECHNICAL ADVISORS Robert J. HAGGERTY, M.D., (Chairman), Roger I. Lee Professor of Public Health, Harvard School of Public Health, Boston, Massachusetts Faye BROWN, R.R.A., Director, Medical Information Service, St. Helena Hospital and Health Center, Deer Park, California Jacob J. FELDMAN, Ph.D., Associate Director for Analysis, National Center for Health Statistics, Hyattsville, Maryland Donald C. RIEDEL, Ph.D., Chairman of the Center for the Study of Health Services, Yale University, New Haven, Connecticut Joseph STEINBERG, President, Survey Design, Inc., Silver Spring, Maryland STAFF Linda K. DEMLO, Ph.D., Division and Study Director Paul M. CAMPBELL, M.S. Sarah Spaght BROWN, M.P.H. Sandra H. MATTHEWS FIELD TEAM Judy HILL, R.R.A., Lynchburg, Virginia Maureen COLLINS, R.R.A., Buffalo, New York Barbara PUDER, R.R.A., St. Louis, Missouri Irene RENDEN, R.R.A., Columbia, Missouri
Copyright © 1977. National Academies Press. All rights reserved.
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vi
Copyright © 1977. National Academies Press. All rights reserved.
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CONTENTS
vii
CONTENTS
FOREWORD
ix
INTRODUCTION
1
STUDY METHODS
5
ANALYSIS
23
SUMMARY AND RECOMMENDATIONS
61
APPENDICES A.
Bibliography
71
B.
Sample Letter to Hospital Administrators
73
C.
Diagnostic Codes Included as Components of the Sample of Medicare Records
75
D.
IOM Re-abstracting Form, General Instructions for Field Team, and Specific Instructions for IOM Re-abstracting Form
85
E.
Informal Checklist
105
F.
Reliability of Field Work
107
G.
Percent of Abstracts with no Discrepancy for each Diagnosis Included as a Component of the Sample of Medicare Records
115
H.
Net and Gross Difference Rates in Designation of Principal Diagnosis (Based on Four-Digit Comparisons of Specific Diagnoses)
121
General Comparison of Assessments of the Reliability of Medicare Records Maintained by the Health Care Financing Administration and Hospital Discharge Abstracts Compiled by Private Abstract Services
125
I.
Copyright © 1977. National Academies Press. All rights reserved.
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CONTENTS
J.
viii
Comparison of the Reliability of HCFA Medicare Records and Private Abstract Service Medicare Abstracts for Selected Diagnoses Common to both Studies 127
K. Standard Errors and Confidence Intervals for Statistics Based on Medicare Data 129
L. Standard Errors and Confidence Intervals for Statistics Based on Data from Private Abstracting Services 132
M. Selected Examples of Principal Procedures not Listed in CPT, as Determined by the IOM Field Team Members 134
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FOREWORD
ix
FOREWORD
This report presents the findings of an assessment of the reliability of information describing hospital utilization by Medicare beneficiaries and obtained as a by-product of the Medicare administrative record keeping system. The information is generated primarily from claims submitted by hospitals to fiscal intermediaries for reimbursement purposes. The study was a logical extension of an earlier examination of the reliability of hospital utilization data compiled by private abstracting services and based on abstracts of medical records. Although the initial request for both studies stemmed from the need to identify an existing data base to serve as a baseline for evaluating the Professional Standards Review Program, the analyses were conducted with a view toward the broader purposes to which utilization data might be applied. The Medicare data and information compiled by private abstracting services constitute two of the few national data bases with potential use for a variety of health services research, policy, and administrative needs, in addition to the PSRO evaluation, provided they are sufficiently reliable. Although some information items were highly reliable, the cumulative effect of both studies elicits serious reservations about the adequacy of existing hospital utilization data on diagnoses and procedures. Accordingly, the uncritical processing of diagnostic-specific utilization information must be questioned, since increasingly important decisions regarding the adequacy of health care, the allocation of resources, and, perhaps hospital reimbursement rates may be based on these data. The deficiencies in the accuracy of currently available diagnostic and procedural utilization data in no way lessens the need for a comprehensive and reliable national health information system. The committee hopes that this report may be reviewed as one Robert J. Haggerty, M.D. Chairman, Technical Advisory Committee
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FOREWORD x
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INTRODUCTION
1
Chapter 1 INTRODUCTION
This report assesses the reliability of selected information describing the utilization of hospital services by a sample of Medicare beneficiaries. The information is recorded in a data base of hospital records maintained by the Health Care Financing Administration (HCFA). [1] The analysis was to assist in identifying an existing data base for assessing the effects of Professional Standards Review Organizations (PSROs), since baseline data were not gathered before the PSRO program was implemented. [2] This study is a logical extension of an earlier examination of the reliability of hospital utilization data compiled by private abstracting services and based on abstracts of medical records. [3] The Medicare data and information compiled by private abstracting services constitute two of the few national data bases for monitoring utilization of health services. Both are potentially useful for a variety of health services research, policy, and administrative needs, in addition to the PSRO evaluation, provided they are sufficiently reliable.
1 The Health Care Financing Administration (HCFA) was created in March 1977. Components of the Social Security Administration (SSA) dealing with Medicare became part of HCFA. The data base of Medicare records referred to in this report is maintained by the Office of Policy, Planning and Research, HCFA. 2 The establishment of PSROs was authorized by Congress in 1972 through Public Law 92-603. PSROs are intended to assure that medical services financed by Medicare, Medicaid, and Maternal and Child Health Programs conform to appropriate professional standards, are medically necessary, and in the case of inpatient services, could not have been performed equally effectively on an outpatient basis or in an inpatient facility of a different type. 3 Institute of Medicine, Reliability of Hospital Discharge Abstracts (Washington, D. C.: National Academy of Sciences, February 1977).
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INTRODUCTION
2
The information examined in this study is accumulated as a by-product of the Medicare administrative record-keeping system, which contains three primary data files: the health insurance entitlement master file identifies each person eligible for health insurance benefits and contains basic demographic information; the provider file contains information on each participating facility authorized to receive reimbursement; and the hospital insurance utilization file contains basic hospital admission and discharge information derived from the claim form. For a twenty percent sample of all Medicare beneficiaries who are hospitalized, the information on diagnoses and surgical procedures entered on the claim form by the hospital billing office is coded by SSA.[4] For these beneficiaries, a statistical discharge record is created (hereafter referred to as the Medicare record), which consolidates information from all three files mentioned above. Six items from the Medicare records for this twenty percent sample constituted the basis for this analysis. They include date of hospital admission, date of discharge, sex, principal diagnosis, the presence of other diagnoses, and principal procedure. There are several characteristics of this data file that support its potential value for program evaluation. The information has been collected for the past ten years. It could be used, therefore, for before-and-after measures of Medicare utilization in areas with and without active PSROs. The ability to identify both patient's residence and place of treatment allows estimates of patient migration in and out of PSRO areas, so that better comparisons of differential admission rates may be made between PSROs. Finally, the unique and permanent identification of the beneficiaries permits the creation of a person-based data file. This, in turn, facilitates the aggregation of isolated hospital admissions and examination of an entire episode of illness, readmission patterns, and discharge to lower levels of care. The value of these analyses depends, in part, on the reliability of the data on diagnoses and procedures, which had not been examined before this study. SPECIFIC STUDY OBJECTIVES The objectives of this examination of Medicare records were: • To determine the frequency of discrepancy between selected data items included in the Medicare record and the corresponding hospital medical record; • Where discrepancies were detected, to determine the source of the error (usually, either the hospital or HCFA) and the reasons for which it occurred;
4 Beneficiaries are chosen for inclusion in the sample by the terminal digit of their health insurance claim number assigned by SSA when a beneficiary enrolls.
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INTRODUCTION
3
• To analyze the extent to which discrepancies affect utilization statistics aggregated at varying levels of diagnostic, hospital, and geographic groupings; and • To compare the reliability of Medicare records and abstracts processed by private abstracting services. The extent of agreement between the Medicare data and the hospital medical record was determined by comparing the results of an independent abstracting of the hospital record with the Medicare record. Information was also gathered to describe the hospital's procedures for completing the Medicare claim form and forwarding that information to the fiscal intermediary and, eventually, to HCFA. The multiple, but poorly understood paths through which the data flow suggested that this additional documentation was warranted and might be useful in explaining why errors occur. The study conclusions are dependent on the reliability of the field work. Therefore, a sub-sample of the medical records abstracted by the field team was independently re-abstracted to assist in determining the reliability of their work. The study did not examine the validity of information in the medical record--that is, the extent to which recorded information accurately reflects the patient's condition. Although this is an important question, it was outside the scope of the study.
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INTRODUCTION 4
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STUDY METHODS
5
Chapter 2 STUDY METHODS
In the Institute of Medicine's study of the reliability of the six items selected from the Medicare record, a field team independently abstracted selected patient records within participating study hospitals. The results of the independent abstracting were compared with Medicare data on the same hospitalizations obtained from the HCFA. (The Medicare information had been compiled by HCFA from claim forms submitted by the study hospitals to the fiscal intermediary for payment.) Discrepancies between the Institute of Medicine (IOM) abstract and the Medicare record were noted, and the patient records were re-examined in an attempt to understand the reasons for discrepancies. For most records with discrepancies, an attempt was made to locate the hospital copy of the appropriate claim form (Medicare form number 1453) and transfer the claims information to the abstract form. These data, supplemented by information on hospital procedures for processing Medicare claims, constituted the basis for the analysis. The sampling plan, research instruments, field work, data processing, and analytic techniques developed for the previous re-abstracting study are generally applicable here. Unique methodological aspects of the current study are discussed below. SAMPLING PLAN As before, a three-stage sampling plan was used. The plan included an initial national sample of hospitals suitable for use in the larger PSRO evaluation; a smaller subsample of hospitals included in this study; and within each study hospital, aa sample of Medicare records that were used in the abstracting. Initial National Sample of Hospitals The Institute of Medicine had previously drawn a national probability sample of short-term general hospitals using a two-way controlled selection
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STUDY METHODS
6
process.[1] The sample consisted of ten panels of hospitals with less than 1,000 beds and an additional group of hospitals with 1,000 or more beds, which were included in the sample with certainty. Each panel constitutes a national sample in its own right and can be combined with other panels, plus the certainty hospitals, to create a range of representations of the national hospital universe, depending on the sample size and level of precision desired. Two of the ten panels were randomly selected and combined with the certainty hospitals to serve as the sampling frame for the previous re-abstracting study. Therefore, eight panels were available for inclusion in this study. One panel was chosen at random and, together with a subsample of certainty hospitals, provided 303 hospitals that served as the sampling frame for this study. Subsample of Hospitals for Abstracting Within the sampling frame, a final sample of 125 hospitals was drawn, using an approximation to controlled selection. Although the initial goal of the study was to include seventy to seventy-five hospitals, oversampling was necessary because of the limited time for follow-up contacts and an expected high refusal rate. A letter to administrators of sampled hospitals requesting their participation is in Appendix B. Eighty-four of the 125 hospitals (65 percent) agreed to participate. This number was reduced to seventy-two, using the stratification variables, in order to stay within the budget. The hospitals that finally were asked to participate had similar characteristics and were distributed in proportion to those in the original sample of 125. One hospital in the final sample was excluded because it was not possible to retrieve the necessary medical records with the available identifying information. Therefore, a total of seventy-one hospitals were included in the study. The hospitals declining to participate did not appear to differ from the participants in any systematic manner. The basic hospital weights were adjusted to reflect the reduced sample size. Sample of Medicare Records The sample design for selecting records to be independently abstracted was guided by several considerations. Because the study was intended, at least in part, to determine the usefulness of Medicare records for evaluating the effects of PSROs, the diagnoses were selected to conform
1 R. Goodman and L. Kish, “Controlled Selection - A Technique in Probability Sampling,” Journal of the American Statistical Association, 45 (September 1950): 350-72; see also Irene Hess, Donald C. Riedel, and Thomas B. Fitzpatrick, Probability Sampling of Hospitals and Patients, 2nd ed. (Ann Arbor: Health Administration Press, 1975).
Copyright © 1977. National Academies Press. All rights reserved.
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STUDY METHODS
7
with those specified in the PSRO Evaluation Plan as appropriate for determining PSRO impact on utilization of hospital services. [2] This had the added advantage of facilitating comparisons between the results of this study with the previous re-abstracting study. However, some diagnoses from the previous study were not appropriate for a Medicare population and were excluded--for example, hypertrophy of tonsils and adenoids. Other diagnoses especially important for a Medicare population were included, even though they had not been used in the previous study--for example, diseases of the prostate. In addition to examining specific diagnoses, it is sometimes useful to aggregate information to reflect broader, homogeneous groupings of patients with generally similar conditions. In this regard, HCFA's Office of Policy, Planning and Research has analyzed utilization data using AUTOGRP categories as a research tool. [3] Therefore, an additional sampling consideration was the desirability of analyzing the reliability of data by AUTOGRP categories, as well as by specific diagnoses. A sampling plan was developed that concentrated on the fifteen AUTOGRP Diagnosis Related Groups (DRGs) for conditions most frequently occurring in the Medicare population. Most DRGs were divided into specific and residual sub-groups. Specific diagnostic sub-groups include diagnoses identified for special study because of their importance for the Medicare population and/or their inclusion in the previous study. They were sampled at a greater frequency than the residual diagnostic subgroups, which include everything in the DRGs except the specific diagnoses. As an example, cataract is a specific diagnosis from the DRG of “Diseases of the Eye” and was sampled with a higher frequency than the residual sub-group within that DRG. In some cases, the DRG contained only the specific diagnosis - for example, diabetes or cerebrovascular diseases - and there was no residual sub-group. Where there were residuals, they often included diagnoses referred to as “satellites” in the previous study. “Satellite” diagnoses are frequently and erroneously coded as principal in place of the specific diagnosis with which they are associated. This is another feature of the sample design that facilitated comparisons between this study and its predecessor.
2 U.S. Department of Health, Education, and Welfare, Office of the Assistant Secretary for Health, Office of Professional Standards Review, Program Evaluation Plan: Professional Standards Review Organizations, by Martin A. Baum et al. (22 September 1975), pp. 119-21. 3 The AUTOGRP system, developed at Yale University, classifies patients into homogeneous categories that are clinically and statistically meaningful and reflect similar patterns of hospital resource consumption. See Ronald Mill, Robert B. Fetter, Donald C. Riedel, and Richard Averill, “AUTOGRP: An Interactive Computer System for the Analysis of Health Care Data,” Medical Care 14 (July 1976): 603-615.
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STUDY METHODS
8
In order to give some chance of inclusion in the sample to all diagnoses, a sixteenth DRG was created that encompassed all diagnoses not included in any of the other fifteen DRGs. This sixteenth category, plus the residual sub-groups in the other DRGs, permit the calculation of net and gross difference rates, as was done in the previous study. The total sample represents the universe of all diagnoses included in the ICDA-8 classification system, as adapted by HCFA.[4] Figure 1 shows the diagnostic groups included in the sample and their ICDA-8 code numbers. A more comprehensive listing of code numbers is found in Appendix C. A computerized sampling procedure was used to select the abstracts for inclusion in the study. The universe of abstracts eligible for selection in each hospital was known and used to develop the rates with which each diagnostic group within each hospital was sampled. All abstracts were for Medicare beneficiaries age 65 and over, who were discharged from the study hospitals during calendar year 1974 and included in the twenty percent sample maintained by HCFA. The year 1974 was used because this is the baseline year for assessing the effects of PSROs. This procedure was expected to yield a total of 4,908 abstracts from seventy-one hospitals. In some instances, however, the medical record was not available in the medical record department, and no substitutions were made. As a result, 4,745 medical records were actually independently abstracted by the IOM field team.
4 Eighth Revision of International Classification of Diseases, Adapted for Use by the Social Security Administration, U.S. Department of Health, Education, and Welfare, Social Security Administration, Pubn. No. 23-72, undated.
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STUDY METHODS
9
Figure 1. Components of Sample of Medicare Records Diagnosis related group (DRG)
Sub-groups
ICDA-8 code numbers*
Specific diagnosis
Chronic ischemic heart disease**
412.0;412.9
Residual diagnosis
Subacute ischemic heart disease (satellite)
411.0;411.9
Angina pectoris
413.0;413.9
Asymptomatic ischemic heart disease
414.0;414.9
Specific diagnosis
Cerebrovascular diseases**
430.0-438.9
Residual diagnosis
none
1. Ischemic heart disease(s) except acute myocardial infarction (AMI)
2. Cerebrovascular Diseases
3. Fractures Specific diagnosis
Fracture, neck of femur**
820.0-820.9
Residual diagnosis
Fracture of other and unspecified parts of femur (satellite)
821.0-821.9
Fracture of skull, spine, and trunk
800.0-809.9
*Discrete and non-continuous codes are listed individually. Where a diagnosis has a lengthy list of continuous codes, however, only the first and last codes are given, separated by a dash. A complete listing of codes for each diagnosis is found in Appendix C. The reader may wish to refer to the Appendix before considering the influence of coding discrepancies in the analysis. **Indicates specific or “target” diagnosis used in prior re-abstracting study.
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STUDY METHODS
Diagnosis related group (DRG)
10
Sub-groups
ICDA-8 code numbers
Fracture of upper limb
810.0-819.9
Fractures of lower limb; excludes fractures, neck of femur
822.0-829.9
Specific diagnosis
Cataract**
374.0-374.9
Residual diagnosis
Inflammatory diseases of the eye
360.0-369.9
Other diseases and conditions of the eye; excludes cataract and blindness
370.0-373.9; 375.0-378.9
Specific diagnosis
Acute myocardial infarction**
410.0-410.9
Residual diagnosis
none
4. Diseases of the eye
5. Acute myocardial infarction
6. Hernia of abdominal cavity Specific diagnosis
Inguinal hernia without mention of obstruction*
550
Residual diagnosis
Inguinal hernia with obstruction (satellite)
552
Other hernia of abdominal cavity without mention of obstruction
551.0-551.9
Other hernia of abdominal cavity with mention of obstruction
553.0-553.9
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STUDY METHODS
11
7. Diabetes mellitus Specific diagnosis
Diabetes** mellitus
Residual diagnosis
none
250.0-250.9
8. Diseases of the prostate Specific diagnosis
Hyperplasia of prostate
600
Residual diagnosis
Prostatitis and other diseases of the prostate
601;602
Specific diagnosis
Bronchopneumonia organism not specified and pneumonia organism and type not specified
485;486
Residual diagnosis
Pneumonia, type and organism specified
480;481; 482.0-482.9; 483;484
Specific diagnosis
Cholelithiasis and cholecystitis**
574.0-574.9; 575
Residual diagnosis
Other diseases of the gall bladder and bile duct
576.0-576.9
Intestinal obstruction without mention of hernia
560.0-560.9
9. Pneumonia
10. Diseases of the gall bladder and bile duct
11. Miscellaneous diseases of the intestine and peritoneum Specific diagnosis
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STUDY METHODS
12
Diagnosis related group (DRG)
Subgroups
ICDA-8 code numbers
Residual diagnosis
Peritonitis, peritoneal adhesions, and other diseases of intestine and peritoneum
567.0-567.9; 568;569.0-569.9
Specific diagnosis
Congestive heart failure and left ventricular failure
427.0;427.1
Residual diagnosis
Acute heart failure, undefined
782.4
Specific diagnosis
Diverticulosis of intestine
562.0;562.1
Residual diagnosis
Noninfectious gastroenteritis and colitis, except ulcerative
561
Chronic enteritis and ulcerative colitis
563.0-563.9
Functional disorders of intestine
564.0-564.9
Specific diagnosis
Bronchitis
466;490; 491
Residual diagnosis
none
12. Heart failure
13. Enteritis, diverticula, and functional disorders of intestine
14. Bronchitis
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STUDY METHODS
13
Diagnosis related group (DRG)
Sub- groups
ICDA-8 code numbers
Specific diagnosis
Malignant neoplasm of bronchus and lung
162.1
Residual diagnosis
Primary malignant neoplasm of respiratory system except of bronchus and lung
160.0-160.9; 161.0-161.9; 162.0; 163.0-163.9
15. Malignant neoplasm of the respiratory system
16. All else Specific diagnosis
all else
Residual diagnosis
none
Retrieval of Medicare Claim Forms As noted earlier, the field team was instructed to consult the hospital's copy of the Medicare claim form when a discrepancy was found between the IOM abstract and the Medicare record that was not attributable to the field work. (Mistakes by the field team were expected to result in a determination that the Medicare record was correct, so there would be no need to consult the claim form.) Retrieving claim forms was difficult because of the lack of uniformity in filing procedures. Some are filed according to the date on which the account is closed; others are physically removed from the hospital. Some hospitals had not retained the form. Since there were 2,878 IOM abstracts with at least one discrepancy not attributable to the field work, the same number of claim forms should have been available and examined. However, only 2,377, or 82.6 percent of those needed, could be retrieved. Ten hospitals were unable to produce any Medicare claim forms. They were located primarily in Standard Metropolitan Statistical
*Discrete and non-continuous codes are listed individually. Where a diagnosis has a lengthy list of continuous codes, however, only the first and last codes are given, separated by a dash. A complete listing of codes for each diagnosis is found in Appendix C. The reader may wish to refer to the Appendix before considering the influence of coding discrepancies in the analysis. **Indicates specific or “target” diagnosis used in prior re-abstracting study.
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STUDY METHODS
14
Areas, as was the general study population. However, they included fewer voluntary hospitals, more governmental hospitals, and a disproportionately lower share of hospitals with 200 to 500 beds than the rest of the study population. Because of the limited availability of claims forms, these data were analyzed as a separate data set. RESEARCH INSTRUMENTS Two research instruments were developed: the abstracting form was intended to record information obtained in the process of abstracting selected medical records (see Appendix D); the informal checklist was intended to describe the flow of claims information within hospitals and to ascertain the extent to which hospital definitions conform to those used by the field team and by HCFA (see Appendix E). Abstracting Form Information items to be abstracted included date of hospital admission, date of discharge, sex, admitting diagnosis, principal and other diagnoses, and principal and other procedures. Additional items appear on the claim form, such as date of surgery and charges for laboratory and other hospital services. Because of the complexities and costs of adding more items to the study, however, the information abstracted was restricted to that mentioned above. Age and/or date of birth was not abstracted because it is validated by HCFA when a person enrolls for benefits. All abstracting was done in accord with item definitions developed for the Uniform Hospital Discharge Data Set (included in Appendix D), since PSROs use UHDDS definitions. Furthermore, consensus has been reached within the Department of Health, Education, and Welfare to require use of UHDDS in federal reporting systems. Diagnostic coding was based on ICDA-8 as modified by HCFA.[5] Procedural coding was based on four-digit Surgical Current Procedural Terminology (CPT), first edition, as modified by HCFA.[6]
5 Eighth Revision of International Classification of Diseases, Adapated for Use by the Social Security Administration, U.S. Department of Health Education, and Welfare, Social Security Administration, Pubn. No. 23-72, undated. 6 Numerical Surgical Current Procedural Terminology (CPT) List, U.S. Department of Health, Education, and Welfare, Social Security Administration, Office of Program Policy and Planning, ORS, Pubn. No. 013-75 (9/75). Although the title specifically refers to surgical procedures, the contents include procedures which are not surgical--for example, transfusion--and which also appear on the Medicare claim form. Throughout this report the term “procedure” is used, rather than “surgical procedure,” since it is clear that this broader concept is appropriate.
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STUDY METHODS
15
Although all diagnoses and procedures for a given hospitalization were recorded on the abstract form, only the admitting and principal diagnoses and principal procedure were coded. Strict guidelines were provided for designating an admitting diagnosis, relying on only the limited information available at the time of admission or soon thereafter. This information was used to explore the possibility that the primary diagnosis, contained in the Medicare record, may be in reality an admitting diagnosis, rather than a more definitive principal diagnosis, because of the urgency to submit a claim form for reimbursement. The presence of additional diagnoses was also noted--again, in accord with specific guidelines. For example, a “history of” a particular diagnosis would not be included unless it were clinically significant for the hospitalization under review. A bone fracture with surgery or other treatment would be included, whereas a slight degree of osteoarthritis noted only as an observation and for which a patient was not treated, would not be included. The notation of significant additional diagnoses was intended to determine whether they are appropriately recorded on the Medicare record. (All diagnoses may be recorded on the Medicare claim form. The Medicare record, however, includes a code for the primary diagnosis only; the presence of additional diagnoses is noted, but they are not coded.) It was hypothesized that the reliability of data for patients with additional diagnoses might be lower than for those with a single diagnosis only. There were several steps involved in the abstracting process, each intended to increase understanding of the reasons for discrepancies and the relative degree of error that would be involved if such data were used for evaluative purposes. After the field team had independently abstracted all information items from the hospital medical record, the results of that process were compared with corresponding information on the Medicare record. Where the two disagreed, the medical record was re-examined and an attempt was made to determine the correct data source, as well as the reason for the discrepancy. In all cases, these determinations were made before consulting the claim form. The options available to denote reasons for discrepancy varied, depending on the data item, as outlined below. Reasons for discrepancy - date of hospital admission, discharge, and sex: • Clerical - discrepancies attributable to mistakes of a coding clerk, such as obvious transposition of numbers in the year of admission. • Completeness - discrepancies resulting from an inadequate review of the medical record. For example, an item may be missing from the admitting sheet, but clearly stated in the discharge summary. Reasons for discrepancy - admitting vs. principal diagnoses (both determined by field team):
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STUDY METHODS
16
•
Completeness - discrepancies between the admitting and principal diagnoses resulting from an incomplete review of those portions of the medical record designated for use in determining admitting diagnosis. • Coding refinement - discrepancies resulting from the availability of additional details when the principal diagnosis was determined. Although the general diagnostic category does not change, the final determination is more refined. For example, the admitting diagnosis may be pneumonia (486.0), although the principal diagnosis is pneumococcal pneumonia (481.0). • Investigation - discrepancies occurring when an admitting diagnosis (such as headache) is based on preliminary findings or symptoms, although the principal diagnosis (hypoglycemia) is based on more complete medical investigation. • Other - all reasons not included in any of the above. For such cases, the field team member was instructed briefly to explain the reason for the difference. Reasons for discrepancy - principal diagnosis and procedure: These reasons for discrepancy were grouped into two broad categories: ordering and coding. The possibility of an ordering discrepancy had to be eliminated before considering a coding discrepancy. In general, ordering discrepancies stem from uncertainty about whether a diagnosis or procedure should be regarded as “principal” or “other,” in accord with UHDDS definitions. The following specific types of ordering discrepancies were considered: • Ordering: Medicare definition - discrepancies reflecting differences between the UHDDS definition and that required for the Medicare claim form, which should occur only if a hospital consciously and consistently used the Medicare definition in completing the claim forms in 1974. For example, a patient is admitted for an open fracture reduction and while on the operating table suffers an acute myocardial infarction which requires a three-month hospitalization. Using the UHDDS definition, fracture reduction would be chosen as the principal diagnosis, because fracture is the diagnosis explaining the cause of admission. If the definition in the “Medicare Hospital Manual” for principal diagnosis is used, however, the diagnosis might be AMI. The manual states: “the primary diagnosis is the diagnosis or the illness or condition which was the primary reason for the patient's hospitalization.” It might be noted that this Medicare definition is appropriate primarily for patients included in the twenty percent sample. For other Medicare beneficiaries, the “intermediary may authorize the hospital to use the ‘working' diagnosis rather than the final diagnosis if this
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STUDY METHODS
• •
•
• •
17
would reduce delays in the submission of billing forms.”[6] In hospitals with this authorization, billing office personnel would have to explicitly determine whether a particular patient was in the twenty percent sample and report the diagnosis accordingly. Therefore, this reason for discrepancy was not expected to be frequently used. Ordering: Hospital list - discrepancies stemming from a routine hospital practice (in 1974) of choosing the first listed diagnosis or procedure on the face sheet as principal. Ordering: Completeness - discrepancies caused by selecting a narrative for principal diagnosis based on an incomplete review of the medical record. For example, the principal diagnoses recorded by IOM and Medicare refer to different diseases, each of which the patient had during the hospital episode under question. However, if the chart had been searched more thoroughly, it would have been clear that one, rather than the other, was the proper principal diagnosis in accord with UHDDS definitions. Ordering: Judgment - discrepancies representing an honest difference of opinion in determining which of several diagnoses is principal. One example of this might be a record for a patient with diabetes and glaucoma and insufficient documentation to decide whether either diagnosis would conform to the UHDDS definition for principal diagnosis. Similarly, a record may indicate carcinoma of several sites and may not be sufficiently documented to permit a determination of a principal diagnosis using the UHDDS definition. Ordering: Other - all reasons not included in any of the above. The field team was instructed to write a note explaining the necessity to use this reason. Ordering: Dependent - this option applied only to discrepancies on principal procedure and was used if an earlier discrepancy in the selection of principal diagnosis resulted in a corresponding or dependent discrepancy in the selection of principal procedure.
After the ordering options were eliminated as possible reasons for discrepancies between the Medicare record and the IOM abstract, coding options could be considered. In some respects, the term “coding” may be a slight misnomer for this type of discrepancy because the code should be assigned by HCFA personnel, who have access only to information on the claim form. A more accurate description of this type of discrepancy might refer to the specificity of detail in the narrative on which the code is later based, but this is rather cumbersome. In some hospitals a code is assigned in place of or in addition to the narrative, in which
6 Medicare Hospital Manual, U.S. Department of Health, Education, and Welfare, HIM Pubn. 10 (6-66), Reprint, August 1975, p. 81.1-82.2.
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STUDY METHODS
18
case the earlier use of “coding” discrepancies is quite appropriate. Occasionally fiscal intermediaries also assign codes. Furthermore, the distinction between ordering and coding discrepancies facilitates comparison with the prior study. Therefore, for abstracts where there was a general agreement on what the principal diagnosis or procedure should be, but not the resultant code, the following reasons for discrepancy were available: • Coding: Clerical - discrepancies caused by transposing diagnostic code numbers or using non-existent codes. • Coding: Completeness - discrepancies caused by including incomplete narrative information for assigning a code, which resulted from an incomplete review of the medical record. For example, the presence of a nine as a fourth digit, indicating the diagnosis is not otherwise specified, when a more careful review of the chart would have resulted in a more specific fourth digit code. • Coding: Procedural - discrepancies caused by routine and systematic misuse or misunderstanding of the coding system. For example, reliance on the diagnostic index without reference to tabular listings or failure to heed inclusion and/or exclusion advice from the tabular listing. • Coding: Importance - this option applies only to the coding of procedures and was used to explain discrepancies caused by differences of opinion over how significant a procedure must be to warrant coding. This is particularly important in hospitals which routinely code all procedures without regard to the UHDDS definition. • Coding: Judgment - discrepancies caused by absence of complete word-for-word correspondence between the recording of the diagnosis or procedure in the medical record and/or claim and the wording in the coding manuals, which requires relying on judgment. For example, a diagnosis may be recorded as “recurrent.” It is unclear from the record whether “acute” or “chronic” is the more appropriate qualifier, and these are the only two options available in the coding manual. • Coding: Other - All reasons not included in any of the above. This option was expected to be used to explain differences when the field team coded a procedure, but Medicare coders did not because the information on the claim forms was illegible or inappropriate. A note of explanation was required if this option was selected.
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STUDY METHODS
19
Reasons for discrepancy - presence of additional diagnoses: • Completeness - discrepancies resulting from an incomplete review of the medical record in hospitals where additional diagnoses are expected to be noted on Medicare claims forms. • Hospital definition - discrepancies resulting from a hospital policy whereby only one diagnosis, presumably the principal one, is routinely entered on the Medicare claim form. By definition, no additional diagnoses would appear on the Medicare record. • Importance - discrepancies resulting from IOM guidelines for determining additional diagnoses. For example, osteoarthritis may be listed as a diagnosis in the medical record and the Medicare claim form might indicate the presence of an additional diagnosis. However, if osteoarthritis were noted in the record only as an observation, then the field team would not regard it as an additional diagnosis. IOM guidelines define an additional diagnosis as one whose presence affects treatment methods or utilization of services. (See Specific Instructions in Appendix D). For each discrepancy, the field team was asked to code only one explanatory reason. Frequently a subjective assessment was required in order to determine which reason might be influential. It is particularly difficult to know whether coding errors should be traced to the hospital or to HCFA. Therefore, the reliability of these responses may be less than for the remainder of the data. Nevertheless, the potential value of the information was judged to outweigh its partially subjective nature. After all records had been abstracted and the reasons for discrepancy determined, information from the claim form was abstracted. The diagnoses and procedures were entered on the abstracting form in the order in which they were listed on the claim form. The first-listed diagnosis and related procedure was then coded by the field team since this procedure is usually followed by Medicare coders. To expedite the field work, comparisons between the claims data and the Medicare record were performed by computer at the National Academy of Sciences (NAS), rather than by the field team. When the narrative summary of diagnoses and procedures is transferred from the claim form into a numerical code by HCFA coders for inclusion in the twenty percent data base, occasionally special coding guidelines are used. The field team did not use these guidelines, because of the potential lessening of reliability that might result if they altered their usual coding practices. This raises the possibility that discrepancies between the field team's work and the Medicare record may stem from HCFA procedures, rather than from inaccurate data submitted by the hospital. Therefore, claims data from all abstracts with discrepancies were coded by senior Registered Record Administrators (RRAs)
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STUDY METHODS
20
employed by HCFA to determine whether the HCFA codes had been correctly applied. In the analysis, special attention was given to those cases in which the IOM abstract and hospital claim agreed, but the Medicare record was different. This enabled an estimation of the extent to which HCFA coding accounted for the discrepancy. Information Checklist The checklist was developed to describe the flow of information from the medical record department to the hospital billing office and fiscal intermediary (see Appendix E). It was not administered as a formal questionnaire. Instead, it provided an informal guide for the field team to help them understand hospital procedures and the reasons for which discrepancies might occur. This information was needed for several reasons. During initial meetings with local hospitals and Medicare officials to determine the feasibility of conducting this study, it became apparent that the paths by which claims information eventually enters the HCFA computer vary considerably. In some hospitals, when a patient is discharged the medical record department sends the billing office a discharge list, including information on diagnoses and procedures needed to complete the claim form. Elsewhere, a copy of the face sheet of the medical record or discharge summary may be sent to the billing office, where the necessary information is retrieved. If the principal diagnosis is determined by a billing clerk, short and easily spelled diagnoses may receive preference. HCFA requires that information on diagnosis and procedure be provided in a narrative (not coded) form. In one hospital visited, diagnostic information was transformed to H-ICDA codes by the medical record department and sent to the billing office, which then translated that information back to a narrative for forwarding to the intermediary. In other instances, the hospital provided coded information to the intermediary, which then converted the codes to narrative for transmittal to HCFA. Occasionally, HCFA may receive claim forms for the twenty percent sample that are already coded. Nevertheless, Medicare coders are instructed to disregard the codes and enter their own. Although the fact of variation was known, the permutations along that path and the influence on data reliability were not known. It therefore became desirable to try to document the variability and use that information in analyzing the reliability of the Medicare records. Thus, information was obtained to reflect the source from which the billing office obtained information on diagnoses and procedures and the training levels of persons retrieving that information, the number of days after discharge that diagnostic information was transmitted to the billing office for entry on the claim form, whether later, more definitive diagnostic information was forwarded to the billing office and intermediary, whether the billing office received narrative or coded information and, if coded, whether it was translated back into a narrative.
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STUDY METHODS
21
Information on the definition of principal diagnosis and procedure used by each hospital during 1974 was also obtained. This was needed in order to determine whether the hospital consciously followed Medicare's definition, which may have led to discrepancies, or whether the hospital had developed a unique definition that differed from both UHDDS and Medicare definitions, which again may have resulted in discrepancies. FIELD WORK The field work was conducted by four Registered Record Administrators (RRAs), recruited because of their extensive experience in diagnostic coding, research, and administration. They were specially trained for this study by a consultant under the general guidance of the IOM staff. Two of the field team members and the technical consultant participated in the previous re-abstracting study and were able to draw upon that experience in refining the methods for this study. The consultant reviewed HCFA's adaptation of the ICDA-8 classification scheme, which consisted primarily of an expansion of fourth digit codes, and instructed the field team in its use. Before the field work began, the supervisor of the medical record department in each study hospital was asked to locate and have available the selected medical records. When the field team member arrived, she first informally discussed the checklist items with the supervisor to acquaint herself with any unusual hospital procedures that might influence the abstracts, and immediately began abstracting. The completed abstracts were compared with the Medicare records (contained in a sealed envelope to avoid conditioning the abstracting) and any differences between the two were noted. If a discrepancy between the Medicare record and IOM abstract was observed, the patient's medical record was re-examined to determine what information appeared to be correct and what factors might account for the discrepancy. For those discrepancies not attributable to the field team member, the claim forms were reviewed. A roster of all records under study had previously been sent to the director of the billing department, so that the claims were available, if needed. Details on the re-abstracting process are found in Appendix D. The field team's ability fully to understand the reasons for discrepancy was somewhat constrained by the break in continuity between the examination of the medical record and review of the claim form. Ideally, after abstracting the medical record one would compare the IOM abstract, the Medicare record, and the claim form by placing them side-by-side and referring to the medical record in hopes of understanding discrepancies. During the feasibility phase of the study it became apparent that this would not be possible. Such a comparison would have required copying or physically removing either the medical record or the claim form from its customary location, which created confidentality problems.
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STUDY METHODS
22
Furthermore, the difficulty in obtaining claim forms often led to delay or return visits, which might have required accessing the medical record twice and, in any case, would have created logistical problems. Therefore, the method used was regarded as the best compromise. To check the reliability of the field work, a subsample of abstracts was independently assessed by the consultant who trained the field team. Comparisons were made between these results and those initially compiled by the field team. In conducting this work, the consultant did not know which member of the field team had done the initial abstracting, or whether discrepancies were initially detected. The assessment process and its results are described in Appendix F. CONFIDENTIALITY To retrieve the medical records and claim forms within study hospitals, it was necessary for HCFA to provide the IOM with the health insurance claim number, name and birth date of the beneficiary, and date of hospital admission and discharge for each hospital episode reviewed. To review the accuracy of diagnostic and procedural coding, a partial review of the medical record was required. Thus, the protection of individual privacy became very important, and special measures were required to assure confidentiality. All information provided by HCFA was carefully secured. The computer tapes on which the data were compiled for analytic purposes were stored in the NAS computer center, which is accessible only to authorized persons. All information which might identify individuals was removed and destroyed as tape files were created. The report contains statistical summaries only, which do not permit the identification of patient, physician, or hospital. These provisions were carefully delineated in a notice in the Federal Register, dated October 14, 1976. DATA PROCESSING After completion of the field work, the abstracts and Medicare records were returned to IOM for data processing. Each abstract and informal checklist was scanned visually, keypunched and verified, and subjected to computer edits. After the accuracy of the raw data was assured, weights were added for use in the analysis. A single composite weight was assigned to each abstract which reflected its probability of inclusion at each step in the sampling process and was adjusted to account for the non-participation of hospitals and the unavailability of medical records. The weights were applied throughout the analysis to permit generalizing to the broader national universe of all 1974 hospital discharges for Medicare beneficiaries age 65 and over and eligible for inclusion in the Medicare utilization data file. Although analyses are based on weighted data, the unweighted sample sizes are reported in the tables presented in the following chapter.
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ANALYSIS
23
Chapter 3 ANALYSIS
The analysis was intended to assess the reliability of six abstracted information items chosen for study and investigate several factors which might affect data reliability, particularly for information on principal diagnosis and procedure. The effect of data reliability on hospital utilization statistics such as diagnostic specific admission rates and lengths of stay was also examined. TOTAL FREQUENCIES OF DISCREPANCIES Table 1 shows the frequency of discrepancies between the Medicare record and the Institute of Medicine (IOM) abstract for each data item selected for study. In general, the data were highly reliable for dates of hospital admission and discharge and the sex of a patient. Information was less reliable for data reflecting the principal diagnosis and principal procedure and whether additional diagnoses were present. [1] When there were discrepancies in these data, information on the IOM abstract was most frequently determined to be correct. Occasionally, the data provided by HCFA and the IOM field team were equally acceptable. This was particularly true for diagnostic data, where 4.6 percent of all sets of abstracts had a different principal diagnosis on each data source and “either” diagnosis was an acceptable choice. The lower level of reliability for diagnosis is of particular concern because such information may be used to reflect disease prevalence, as well as patterns of hospital care and utilization of medical services, and may play an important role in determining policy directives such as resource allocation for specific disease categories. Therefore, a more detailed analysis of the problems associated with the abstracting and coding of these data was performed.
1 A similar pattern of agreement was also found in the independent assessment of the field work (see Appendix F).
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ANALYSIS
24
Table 1. Discrepancy Between Medicare Record and IOM Abstract and the Correct Data Source for Selected Items (weighted percent) Correct data source where a discrepancy exists Selected items
No discrepancy
Medicare Record
IOM Abstract
Either
Neither
Total
Admission date
99.5
0.4
0.1
-
-
100.0%
Discharge date
99.3
0.4
0.3
-
-
100.0
Sex
99.4
0.4
0.2
-
-
100.0
Principal diagnosis (four-digit)
57.2
2.3
35.7
4.6
0.2
100.0
Presence of additional diagnosis
74.5
1.3
23.5
0.7
-
100.0
Principal Procedure
78.9
1.7
17.3
1.7
0.4
100.0
Unweighted N = 4745
The analysis was guided by several factors considered in the previous study and thought to influence reliability, including: • the potential inadequacies of current nomenclature, coding guidelines, and medical recording practices for definitively determining and coding a principal diagnosis or principal procedure and the resultant need of abstractors to exercise some judgment which may lessen reliability; • the degree of coding refinement (four-digit, three-digit, or broader diagnostic classifications such as AUTOGRP); • the contribution of individual diagnoses to the overall discrepancy rates; • the contribution of the actual coding and processing of claims information by HCFA personnel; and
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ANALYSIS
25
• the contribution of structural and functional factors within the hospital that may affect the reliability of abstracted information, including the many paths by which data from the medical records are eventually received by HCFA. The influences of these factors on data reliability were individually considered. The analysis of diagnostic information is presented before that pertaining to procedures. An examination of the content and coding of the Medicare claim form follows. The analysis concludes with discussions of the implications for the accuracy of utilization statistics and the relative influence of hospital characteristics on data reliability. ANALYSIS OF DIAGNOSTIC INFORMATION In analyzing diagnostic information, the reasons explaining discrepancies between the diagnoses coded by HCFA and the field team were first explored in hopes of eliciting general clues about potential reasons for differences. The concordance between admitting and principal diagnosis was examined next to determine whether hospitals may submit admitting diagnoses, rather than principal, to facilitate reimbursement for the Medicare claims. In both analyses all diagnoses were combined. Subsequent analyses were progressively less aggregated to examine the extent to which particular diagnostic groupings or individual diagnoses might contribute to overall accuracy at varying levels of coding refinement. Finally, the influence of co-morbidity was explored. The analyses of information on both diagnoses and procedures are based on comparisons between the Medicare record and the IOM abstract, assuming that data on the Medicare record accurately reflect information from the claim form submitted by the hospital. This assumption was also tested, and the results are presented later in this chapter. Reasons for Discrepancies To understand the lower reliability of principal diagnosis, the reasons selected by the field team to explain discrepancies were analyzed. Table 2, Table 3 and Table 4 show the reasons for discrepancies according to the correct data source for diagnoses compared at the fourth digit, third digit, and classified according to the AUTOGRP system. As noted in Chapter 2, the possibility of an ordering discrepancy (a discrepancy caused by uncertainty over whether a diagnosis should be considered as “principal” or “other”) was to be ruled out before attributing an error to coding practices.
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ANALYSIS
26
Table 2. Reason for Discrepancy in Principal Diagnostic Codes Compared to the Fourth Digit by Correct Data Source (weighted percent) Correct data source Reason for discrepancy
Medicare Record*
IOM Abstract
Either
Ordering-SSA definition
-
1.2
-
Ordering-hospital list
-
20.8
5.0
Ordering-completeness
4.4
21.4
-
Ordering-judgment
2.6
1.4
78.2
Ordering-other
7.8
3.8
1.6
Coding-clerical
29.2
3.5
-
Coding-completeness
12.7
19.4
-
Coding-procedure
37.9
13.7
-
Coding-judgment
2.8
0.2
14.5
Coding-other
2.6
14.6
0.7
Total
100.0%
100.0
100.0
(Percent of total number of abstracts)
(2.3)
(35.7)
(4.6)
Neither**
(0.2)
When the Medicare record was correct, coding discrepancies generally occurred more frequently than ordering discrepancies. This was found at all three levels of coding refinement. When the IOM abstract was correct, the frequency of ordering and coding discrepancies was relatively equal if all four digits were compared. If only three digits or AUTOGRP comparisons were made, coding discrepancies generally decreased and ordering discrepancies assumed greater importance. When “either” data source was correct, the discrepancies were invariably related to ordering problems.
*For some abstracts a reason for discrepancy was not checked by the field team when the Medicare record was correct. Reasons for discrepancies were assigned to those abstracts according to their frequency when they were assigned by the field team. **The analysis of cases for which “neither” was correct is not presented because the numbers are too small.
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ANALYSIS
27
Table 3. Reason for Discrepancy in Principal Diagnostic Codes Compared to the Third Digit by Correct Data Source (weighted percent) Reason for discrepancy
Correct data source Medicare Record*
IOM Abstract
Either
Ordering-SSA definition
-
1.4
-
Ordering-hospital list
-
23.5
5.3
Ordering-completeness
5.0
24.0
-
Ordering-judgment
3.2
1.6
79.5
Ordering-other
9.9
4.1
1.7
Coding-clerical
19.6
3.8
-
Coding-completeness
10.4
12.4
-
Coding-procedure
46.3
12.6
-
Coding-judgment
3.4
0.2
12.7
Coding-other
2.2
16.4
0.8
Total
100.0%
100.0
100.0
(Percent of total number of abstracts)
(1.9)
(31.6)
(4.4)
Neither**
(0.2)
When the IOM abstract was correct, most ordering problems were attributable to two common practices within hospitals--routinely using the first listed diagnosis on the face sheet as the principal diagnosis or determining a principal diagnosis based on an incomplete review of the medical record. The predominance of these reasons for discrepancies was independent of the level of coding refinement. Anecdotal data transmitted informally by medical record and billing department supervisors to the field team indicate a considerable amount of variation among hospitals with respect to the definition of principal diagnosis.
*For some abstracts a reason for discrepancy was not checked by the field team when the Medicare record was correct. Reasons for discrepancies were assigned to those abstracts according to their frequency when they were assigned by the field team. **The analysis of cases for which “neither” was correct is not presented because the numbers are too small.
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ANALYSIS
28
Table 4. Reason for Discrepancy in Principal Diagnostic Codes Compared using AUTOGRP Classifications by Correct Data Source (weighted percent) Reason for discrepancy
Correct data source Medicare Record*
IOM Abstract
Either
Ordering-SSA definition
-
1.8
-
Ordering-hospital list
-
32.3
5.4
Ordering-completeness
1.1
27.1
-
Ordering-judgment
-
1.7
81.6
Ordering-other
14.3
5.6
3.3
Coding-clerical
9.1
2.9
-
Coding-completeness
1.4
7.0
-
Coding-procedure
70.9
8.7
-
Coding-judgment
-
0.1
8.1
Coding-other
3.2
12.8
1.6
Total
100.0%
100.0
100.0
(Percent of total number of abstracts)
(0.7)
(17.5)
(2.3)
Neither**
(0.1)
The actual coding of a diagnosis was more of a problem when discrepancies were analyzed at the fourth digit, than if only the first three-digits were compared or if AUTOGRP was used. For coding discrepancies where the IOM abstract was correct, the reason usually given by the field team was “coding-completeness,” suggesting that a narrative was selected to describe the principal diagnosis without completely reviewing the medical record. This occurred most frequently at the four-digit comparison level, Often a code “nine” was used as the fourth-digit on the Medicare record to indicate “not otherwise specified,” when a more careful review of the
*For some abstracts a reason for discrepancy was not checked by the field team when the Medicare record was correct. Reasons for discrepancies were assigned to those abstracts according to their frequency when they were assigned by the field team. **The analysis of cases for which “neither” was correct is not presented because the numbers are too small.
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ANALYSIS
29
record would have yielded a more specific narrative and corresponding fourth-digit code. (For example, code 560.9 indicates intestinal obstruction without mention of hernia due to an unspecified cause, while code 560.1 indicates intestinal obstruction without mention of hernia due to paralytic ileus.) Another common reason for discrepancy was “coding procedure,” which occurred with relatively equal frequency at the three levels of diagnostic coding refinement. This reflects a routine and systematic misuse or mis-understanding of the coding system, such as relying on either the alphabetic or tabular index, rather than using both. The “coding other” reason for discrepancy also was used with relatively equal frequency regardless of the level of coding refinement. In 50.7 percent of these 207 cases, the diagnostic code listed by HCFA was 799.9, which indicates that the claim form did not contain acceptable diagnostic information, although the field team had coded a principal diagnosis. For most of the remaining cases in this category, the field team was unable to find any diagnostic information in the hospital record similar to that found on the Medicare record, so consideration of alternative discrepancy options was inappropriate. Discrepancies for which the diagnostic codes on “either” the Medicare record or the IOM abstract were equally acceptable account for 4.6 percent of the abstracts in the study when all diagnoses are combined and compared to four-digits. The most frequent reason for this decision was “ordering judgment,” indicating an honest difference of opinion in interpreting the medical record. When three-digit or the AUTOGRP comparisons were used, the percent of abstracts for which “either” source of data was correct was 4.4 percent and 2.3 percent, respectively, and again the most frequent reason for discrepancy was “ordering-judgment.” This may suggest that in some instances the guidelines for determining principal diagnosis are not adequately specified. It also raises the possibility that for some patients, it may be unrealistic to expect reliable determinations of “the” principal diagnosis. The number of cases for which “neither” data source was correct is sufficiently small that the associated reasons for discrepancies are not discussed. In general, three basic problems account for discrepancies between the diagnostic codes determined by HCFA and the IOM field team. When the IOM abstract was correct, two problems identified by the field team reflect instances where remedial action could possibly increase the level of reliability. First, a more complete review of the medical record might reduce the frequency of both ordering and coding discrepancies which stem from the use of incomplete information. Second, more explicitly stated hospital guidelines for recording and transmitting diagnostic information and determining principal diagnosis might help. If the diagnosis listed first on the face sheet is assumed to be “principal,” persons providing that information could be trained to assure that the assumption is correct. The third problem relates to abstracts where “either” diagnostic code is acceptable. In these cases, corrective action is difficult to identify, since the discrepancies stem from professional differences in interpreting a medical record. Although this
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ANALYSIS
30
accounts for only a small percent of the abstracts, it nonetheless is important, since it identifies an area in which the determination of a single, reliable, principal diagnosis may not be feasible. Admitting vs. Principal Diagnosis It has been hypothesized that hospitals' need for reimbursement may cause them to forward claims to fiscal intermediaries containing an admitting diagnosis, rather than a more carefully established principal diagnosis. This likelihood was strengthened by the finding in the preceding section that many discrepancies between the Medicare record and IOM abstract stemmed from an incomplete review of the medical record by hospital personnel responsible for determining the principal diagnosis. To explore this possibility, the field team determined an admitting diagnosis for each case, based only on information contained in the face sheet of the medical record, history and physical reports, and admitting or emergency room notes. This was compared with the principal diagnosis, based on a careful examination of the entire record. Table 5 indicates that for approximately sixty percent of the abstracts, the admitting diagnosis (determined retrospectively by the field team) is an accurate reflection of the principal diagnosis established after study to be chiefly responsible for causing the hospital admission. When the diagnoses were different, coding refinement did not appear to be influential. Rather, the admitting diagnosis usually reflected symptoms or preliminary findings; after additional testing and medical investigation a more precise and different principal diagnosis was determined. Table 5. Discrepancies Between the Institute of Medicine Admitting and Principal Diagnoses and Reasons for Discrepancy at Varying Levels of Coding Refinement (weighted percent) No discrepancy
Reason for discrepancy when one exists Completeness
Refinement
Investigation
Other
Total
Four digit
58.4
0.5
4.7
33.2
3.2
100.0%
Three digit
61.7
0.5
3.2
31.9
2.7
100.0
AUTOGRP classification
80.8
0.2
0.9
16.9
1.2
100.0
Because the more extensive medical investigation led to a considerable change in admitting diagnoses for about thirty-three percent of the cases, it appeared less likely that HCFA's principal diagnosis might in fact closely approximate an admitting diagnosis. This was confirmed when only about forty percent of HCFA's principal diagnoses agreed with the IOM's admitting diagnoses compared to four digits and about forty-six percent at three digits. When this analysis was limited to those
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ANALYSIS
31
discharges where there was a discrepancy between the principal diagnosis on the Medicare record and the IOM abstract and the abstract was correct, only about ten percent of the HCFA's principal diagnoses agreed with the IOM's admitting diagnoses compared to both three and four digits. Influence of Diagnostic Groupings The data presented in Table 1 show the frequency of discrepancies for all principal diagnoses combined and compared to the fourth-digit. Table 2, Table 3, and Table 4 reveal a decrease in coding errors when less specific diagnostic comparisons are used. In this section, the influence of differing levels of diagnostic groupings is explored in more detail. For most of the fifteen diagnostic groups under study, three-digit or AUTOGRP analyses may be acceptable for determining basic utilization statistics, such as admission rates. As described in Chapter 2, the AUTOGRP categories constituted the basis for drawing the sample of abstracts. Within each Diagnosis Related Group (DRG), specific diagnostic sub-groups were identified because of their importance for the Medicare population and/or their inclusion in the previous re-abstracting study. Residual diagnostic sub-groups included all diagnoses in the DRGs except the specific diagnoses. Therefore, the reliability of data was examined for the entire DRGs combined, the specific diagnoses, and the residual diagnoses, using AUTOGRP, three-digit, and four-digit comparisons. The accuracy of data was not influenced greatly by aggregating the diagnostic groups according to their reason for inclusion in the sample--specific or residual sub-categories (see Table 6). However, the level of reliability for all categories of diagnoses does vary according to the level of coding refinement, with increased reliability using AUTOGRP or comparing only three digits. For all diagnostic categories, the AUTOGRP comparisons were more reliable. The increase in reliability must be balanced against the loss of precision in the information, however. The percent of abstracts where the data on “either” the Medicare record or IOM abstract are equally acceptable decreases only slightly when AUTOGRP is used. Diagnostic Specific Discrepancies Table 7 shows the frequency of discrepancy and the correct data source for the individual specific diagnoses (the specific diagnostic sub-groups within the DRGs, many of which conform to the “target” diagnoses in the previous study). The diagnoses with higher levels of reliability include cataract, inguinal hernia without obstruction, hyperplasia of the prostate, diverticulosis of intestine, and bronchitis. The categories with less accurate data include chronic ischemic heart disease, cerebrovascular diseases, diabetes mellitus, intestinal obstruction without mention of hernia, and congestive heart failure. The percent of cases where “either” data source was correct is highest for chronic ischemic heart disease, diabetes mellitus, and bronchopneumonia and unspecified pneumonia.
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ANALYSIS
32
Table 6. Discrepancy Between the Medicare Record and the IOM Abstract at Differing Levels of Aggregating Diagnoses and the Correct Data Source Where a Discrepancy Exists (weighted percent) Level of Aggrega-tion of diagnoses
No discrepancy
Correct data source where a discrepancy exists Medicare record
IOM abstract
Either
Neither
Total
All diagnoses* AUTOGRP
71.7
1.2
23.3
3.5
0.3
100.0%
Three-digit
68.2
1.5
25.9
4.1
0.3
100.0
Four-digit
61.9
2.4
31.2
4.2
0.3
100.0
AUTOGRP
71.3
1.2
23.7
3.5
0.3
100.0
Three-digit
67.8
1.5
26.2
4.2
0.3
100.0
Four-digit
62.5
2.0
30.8
4.4
0.3
100.0
AUTOGRP
73.9
1.5
21.6
3.0
-
100.0
Three-digit
66.3
3.6
27.1
3.0
-
100.0
Four-digit
60.0
4.2
32.5
3.3
-
100.0
Specific sub-categories
Residual sub-categories
*Includes only those abstracts in the first fifteen DRG's listed in Chapter 2. The sixteenth category was created primarily to enhance the representativeness of the sample. It is not an actual DRG and had to be excluded from the AUTOGRP comparisons. It was excluded from the other comparisons as well in order to maintain a common denominator throughout the table. Therefore, the percents are different than in Table 1.
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ANALYSIS
33
Table 7. Weighted Frequency of Discrepancy Between the Medicare Record and IOM Abstract and the Correct Data Source Where a Discrepancy Exists (weighted percent) Principal diagnosis on Medicare record
Weighted percent of all abstracts that each diagnosis represents
Correct data source where a discrepancy exists Percent with no discrepancy
Record
Abstract
Either
Neither
Total
Chronic ischemic heart disease
9.8
36.8
4.0
50.3
7.6
1.3
100.0%
Cerebrovascular diseases
6.9
58.5
3.5
33.8
4.2
-
100.0
Fracture, neck of femur
2.0
70.5
3.0
26.5
-
-
100.0
Cataract
3.0
97.3
0.2
2.5
-
-
100.0
Acute myocardial infarction
2.4
67.3
1.0
28.8
2.9
-
100.0
Inguinal hernia without mention of obstruction
1.3
96.7
-
2.7
0.6
-
100.0
Diabetes mellitus
2.5
49.7
0.8
43.8
5.7
-
100.0
Hyperplasia of the prostate
2.1
87.1
0.4
8.0
4.5
-
100.0
Bronchopneumoniaorganism not specified and pneumonia-organism and type not specified
2.8
75.9
-
18.2
5.9
-
100.0
Cholelithiasis/cholecystitis
2.0
62.8
1.2
34.0
1.7
0.3
100.0
Intestinal obstruction without mention of hernia
0.9
58.0
2.1
36.2
3.7
-
100.0
Congestive heart failure and left ventricular failure
1.7
58.4
0.1
36.3
5.2
-
100.0
Diverticulosis of intestine
1.5
86.5
-
9.1
4.4
-
100.0
Bronchitis
0.7
89.8
-
8.8
1.4
-
100.0
Malignant neoplasm of bronchus and lung
1.2
79.9
-
17.7
2.4
-
100.0
All else
59.2
52.5
2.2
40.0
5.1
0.2
100.0
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ANALYSIS
34
Table 8 displays the percent of abstracts with no discrepancy by diagnosis at differing levels of coding refinement. The table is arranged so that the first column of figures shows the percent of abstracts with no discrepancy for the entire DRG, using AUTOGRP codes. The following columns show the percent with no discrepancy for the specific diagnostic sub-group within each DRG, compared with AUTOGRP codes, threedigits, and four-digits. All data in this table are confined to either entire DRGs or specific diagnoses within them. Related data for the residual diagnoses are in Appendix G. For almost all specific diagnostic sub-groups, three-digit comparisons are more reliable than four-digit. The changes are most pronounced for diagnoses that show a greater number of four-digit codes (see Figure 1 in Chapter 2), especially fracture neck of femur, intestinal obstruction without mention of hernia, and cholelithiasis/ cholecystitis. For some categories, the percents increase as one moves from three-digit to AUTOGRP-particularly for cerebrovascular diseases. For most of the specific diagnoses, however, the AUTOGRP comparison is the same as the three-digit match. Table 8. Abstracts With No Discrepancy Between the Medicare Record and IOM Abstract by Sub-categories of Diagnoses at Differing Levels of Coding Refinement (weighted percent) Principal diagnosis: Entire DRG
AUTOGRP classification
Principal diagnosis: Specific sub-category
AUTOGRP classification
Three-digit comparison
Four-digit comparison
Ischemic heart disease except AMI
40.1
Chronic ischemic heart disease
38.6
38.6
36.8
Cerebrovascular diseases
84.7
Cerebrovascular diseases
84.7
68.4
58.5
Fractures
87.7
Fracture, neck of femur
93.4
93.4
70.6
Diseases of the eye
95.0
Cataract
97.9
97.9
97.4
Acute myocardial infarction
76.1
Acute myocardial infarction
76.1
76.1
67.3
Hernia of abdominal cavity
89.8
Inguinal hernia without mention of obstruction
96.7
96.7
96.7
Diabetes mellitus
56.2
Diabetes mellitus
56.2
56.2
49.7
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ANALYSIS
35
Principal diagnosis: Entire DRG
AUTOGRP classification
Principal diagnosis: Specific sub-category
AUTOGRP classification
Three-digit comparison
Four-digit comparison
Diseases of the prostate
85.0
Hyperplasia of the prostate
87.1
87.1
87.1
Pneumonia
77.8
Bronchopneumoniaorganism not specified and pneumoniaorganism and type not specified
80.5
75.9
75.9
Diseases of the gall bladder and bile duct
79.9
Cholelithiasis/ cholecystitis
84.4
76.3
62.8
Miscellaneous diseases of intestine and peritoneum
58.6
Intestinal obstruction without mention of hernia
68.8
68.8
58.1
Heart failure
58.9
Congestive heart failure and left ventricular failure
60.6
61.0
58.5
Enteritis, diverticula and functional disorders of intestine
86.5
Diverticulosis of intestine
86.9
86.9
86.5
Bronchitis
95.7
Bronchitis
95.7
89.8
89.8
Malignant neoplasm of respiratory system
74.9
Malignant neoplasm of bronchus and lung
79.9
79.9
79.9
All else
*
All else
*
74.3
53.5
*AUTOGRP classification is not appropriate for this category.
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ANALYSIS
36
The reasons for discrepancy were re-examined to determine whether special problems were associated with particular diagnoses (see Table 9). Because of the small number of abstracts, the reasons were grouped into those associated with determining principal diagnosis (ordering) and those associated with assigning a code number after the principal diagnosis had been selected (coding). When the diagnosis on “either” abstract was equally acceptable, most problems were related to the ordering of diagnosis. Where the IOM abstract was determined to be correct, most diagnoses with high discrepancy rates had ordering problems, particularly chronic ischemic heart disease and diabetes which often haveassociated co-morbidity. Ordering problems were also associated with cataract and diverticulosis of the intestine, however, even though the data for these diagnoses were quite reliable. Diagnoses whose reliability improved with less specific coding tended to have coding discrepancies. Influence of Multiple Diagnoses Since Medicare patients frequently have multiple chronic conditions, this may complicate the determination of a principal diagnosis for any particular hospitalization. Therefore, the influence of the presence of additional diagnoses on the reliability of principal diagnosis was examined. The presence of additional diagnoses was determined by the field team using the guidelines listed in the Specific Instructions (see Appendix D). A reconciliation was conducted when the data sources were not in agreement. The data from this reconciliation have been used in the table presented below. Table 10 shows the influence of additional diagnoses on the reliability of principal diagnosis compared to four-digits. For most diagnoses that had a relatively low level of reliability in Table 8, the percent of abstracts with no discrepancy increased if the analysis was confined to those with no additional diagnoses. This was particularly evident for chronic ischemic heart disease, acute myocardial infarction, diabetes, congestive heart failure, and intestinal obstruction without mention of hernia. Co-morbidity also shows some effect on hyperplasia of the prostate, bronchopneumonia and pneumonia unspecified, and diverticulosis of the intestine. Since the presence of additional diagnoses influences the accuracy of data, adjustments in analysis might be made if the fact of co-moribidity were accurately noted on the Medicare claim form. As shown in Table 1, this occurs for only 74.5 percent of the discharges. The reasons for discrepancies between the field team and the Medicare record on this item show that in some instances the errors stem from procedures within the hospitals, which require that only one diagnosis be submitted to HCFA--the “hospital definition” response (see Table 11). However, for most discrepancies, more accurate information would have been obtained if a more complete review of the medical record had been conducted.
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ANALYSIS
37
Table 9. Reasons for Discrepancy by Principal Diagnosis and Correct Source of Data, Based on Four-Digit Comparisons (weighted percent) Principal diagnosis on Medicare record
Correct data source IOM abstract
Either
Ordering
Coding
Unweighted number of abstracts
Ordering
Coding
Unweighted number of abstracts
Chronic ischemic heart disease
81.0
19.0
178
100.0
-
22
Cerebrovascular diseases
46.8
53.2
115
66.7
33.3
14
Fracture, neck of femur
20.0
80.0
47
66.7
33.3
14
Cataract
55.5
44.5
8
-
-
-
Acute myocardial infarction
51.7
48.3
66
100.0
-
8
Inguinal hernia without mention of obstruction
40.0
60.0
8
-
100.0
1
Diabetes mellitus
85.0
15.0
97
66.7
33.3
9
Hyperplasia of the prostate
45.5
54.5
18
100.0
-
8
Bronchopneumoniaorganism not specified and pneumonia-organism and type not specified
43.5
56.5
41
65.0
35.0
9
Cholelithiasis/cholecystitis
39.0
61.0
55
100.0
-
3
Intestinal obstruction without mention of hernia
30.0
70.0
27
100.0
-
5
Congestive heart failure and left ventricular failure
53.5
46.5
56
100.0
-
10
Diverticulosis of intestine
87.5
12.5
13
100.0
-
6
Bronchitis
42.9
57.1
7
50.0
50.0
3
Malignant neoplasm of bronchus and lung
57.6
42.4
22
100.0
-
4
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ANALYSIS
38
Table 10. Frequency of Agreement Between the Medicare Record and IOM Abstract for Principal Diagnosis Coded to the Fourth Digit and Categorized by the Presence of Additional Diagnoses (weighted percent) Principal diagnosis on Medicare record
With additional diagnoses
Without additional diagnoses
Total Number of unweighted abstracts
Percent agreement
Total Number of unweighted abstracts
Percent agreement
Chronic ischemic heart disease
331
35.5
21
57.9
Cerebrovascular diseases
269
57.7
51
62.8
Fracture, neck of femur
136
71.1
70
69.1
Cataract
115
96.2
127
98.7
Acute myocardial infarction
185
63.1
42
84.9
Inguinal hernia without mention of obstruction
79
94.1
61
99.7
Diabetes mellitus
211
47.2
13
90.6
Hyperplasia of the prostate
157
84.0
72
95.9
Bronchopneumoniaorganism not specified and pneumonia-organism and type not specified
177
74.6
32
81.0
Cholelithiasis/cholecystitis
139
62.3
45
64.5
Intestinal obstruction without mention of hernia
70
50.6
27
86.6
Congestive heart failure and left ventricular failure
137
53.1
18
87.2
Diverticulosis of intestine
109
85.5
29
92.0
Bronchitis
56
93.8
10
72.8
Malignant neoplasm of bronchus and lung
103
81.7
35
74.5
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ANALYSIS
39
Table 11. Reason for Discrepancy for Presence of Additional Diagnoses by Correct Data Source (weighted percent) Reason for discrepancy
Correct data source Medicare Record
IOM Abstract
Either
Neither
Completeness
72.6
86.4
13.4
-
Hospital definition
3.0*
7.7
-
-
Importance
24.4
5.9
86.6
-
Total
100.0%
100.0
100.0
-
(Percent of total number of abstracts
(1.3)
(23.5)
(0.7)
(-)
In summary, the analysis of diagnostic information suggests that the Medicare record contains correct data for at least 59.5 percent of the cases when codes are compared to four digits and 63.8 percent with three-digit comparisons (see Table 12). These figures can be adjusted upwards, depending on assumptions about the few cases where the correct data source could not be determined. If one examines the discrepant abstracts for which a correct data source could be determined (either Medicare or IOM) and applies the appropriate percents to the cases for which a correct data source could not be determined, the following conclusions are reached: with fourdigit codes the Medicare records are correct in 59.8 percent of the cases; with three-digit comparisons the corresponding figures is 64.1 percent. Alternatively, one might assume that the Medicare and IOM data are equally likely to be correct. All the “Indeterminates” could then be added to the column in which the Medicare record was correct and the resulting percents would be 64.1 with four-digit comparisons and 68.2 with three-digit comparisons. In any case, individual diagnoses contribute to the overall levels of accuracy. Those with higher rates of discrepancies include chronic ischemic heart disease, cerebrovascular diseases, acute myocardial infarction, and diabetes. The discrepancies associated with these diagnoses tend to reflect difficulty in determining the principal diagnosis (an ordering discrepancy); frequently the patients had multiple diagnoses. There does not appear to be a systematic bias within the hospitals to submit an admitting diagnosis on the claim form in lieu of a more carefully established principal diagnosis.
*The “hospital definition” reason for discrepancy was inappropriatelyselected in this instance.
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ANALYSIS
40
Table 12. Summary Table: Accuracy of Principal Diagnoses on Medicare Records (weighted percent) Level of comparison
Correct*
Incorrect**
Indeterminate***
Total
Four-digit
59.5
35.9
4.6
100.0%
Three-digit
63.8
31.8
4.4
100.0
ANALYSIS OF PRINCIPAL PROCEDURES The analysis of information on principal procedures was similar to that for diagnoses. Table 1 showed that the Medicare record and IOM abstract agreed on principal procedure for 78.9 percent of the cases. The reasons for discrepancy are shown in Table 13. When discrepancies occurred, the IOM abstract was usually determined to be correct. Most problems related to the coding, rather than ordering, of procedures. The reason most frequently selected to explain discrepancies was “coding-completeness,” indicating that reliability might be improved if more care were taken in reviewing the medical record. Other reasons included: “coding-procedure,” describing a systematic misuse or misunderstanding of the coding system; and “coding-importance,” reflecting a difference of opinion about the importance of a procedure listed by the hospital and subsequently by Medicare. For example, the hospital billing office may have listed a transfusion, while the IOM field team may not have regarded this as a principal procedure in accord with UHDDS. Ordering problems occurred less frequently than coding errors. When they were noted and the IOM abstract was correct, the specific reason most frequently selected was “orderingcompleteness.” Thus, an incomplete review of the medical record accounted for about forty percent of all discrepancies when the IOM abstract was correct.
*This column contains all cases for which there were no discrepancies and those for which there was a discrepancy, but the Medicare record was determined to be correct (see Table 1). **This column contains all cases for which there were discrepancies and the IOM abstract or “neither” was determined to be correct (see Table 1). ***This column contains all cases for which there were discrepancies, but it was not possible to state with certainty which data source was correct and the cases were therefore assigned to the “either” category.
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ANALYSIS
41
Table 13. Reason for Discrepancy in Principal Procedure by Correct Data Source (weighted percent) Reason for discrepancy
Correct source data Medicare Record
IOM Abstract
Either
Ordering-SSA definition
5.1**
1.3
-
Ordering-hospital list
-
2.5
1.2
Ordering-completeness
8.4
9.9
-
Ordering-judgment
1.1
0.7
10.1
Ordering-other
-
1.2
0.4
Ordering-dependent
1.3
2.7
4.3
Coding-clerical
29.7
3.8
-
Coding-completeness
14.7
33.6
-
Coding-procedure
28.1
18.8
3.0
Coding-importance
1.2
11.5
56.1
Coding-judgment
-
0.2
21.9
Coding-other
10.4
13.8
3.0
Total
100.0%
100.0
100.0
(Percent of total number of abstracts)
(1.7)
(17.3)
(1.7)
Neither*
(0.4)
The examination of information on procedures is complicated by the un-certainty about which procedures should be included on the bill, in-adequacies of the CPT classification system, and the frequency with which HCFA receives bills containing either no procedural information or illegible information. An attempt was made to categorize the data base according to the status of procedural information and then to ex-amine the concordance between the field team's work and information on the Medicare record.
*The analysis of cases for which “neither” was correct is not presented because the numbers are too small. **The “ordering--SSA definition” reason for discrepancy was inappropri-ately selected in this instance.
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ANALYSIS
42
Table 14 shows that on most Medicare records (2,990 of 4,745, or about sixty-three percent), no procedure is coded--presumably because no procedure was performed and the hospital billing office did not sub-mit this information to the intermediary. The frequency of discrep-ancy is affected when this fact is taken into account. When the analysis is confined to those cases for which the Medicare record did not include a procedure code, there was agreement with the IOM abstract about ninety percent of the time. Alternatively, for about ten percent of those cases the field team thought that a principal pro-cedure should have been coded, but was not. The rate of agreement de-creased to fifty-seven percent when the analysis was confined to only those cases for which a procedure code appeared on the Medicare record. The reasons for discrepancies when there was a code on the Medicare record primarily reflected coding problems, rather than ordering. Al-though failure to follow established coding procedures in recording the narrative on which the code is based was important, the major reason for discrepancy again stemmed from an incomplete review of the medical re-cord. In addition, some discrepancies may occur because Medicare personnel attempt to code everything that appears in the principal pro-cedure position on the claim form. In some cases the field team may have felt that a particular procedure should be listed as principal in accord with UHDDS and wrote the procedure on the abstract form. But because there was no directly related CPT code in the manual, the field team would not code it. For the same cases, however, Medicare coders would “force” the procedure into a code (based on anatomical classifi-cation or a similar procedure for which CPT included a code) or con-struct what is referred to as an “X-code” or use a master code. Re-gardless, this would lead to discrepancies between the Medicare record and IOM abstract. Examples of procedures that were not coded by the field team because of limitations of CPT are found in Appendix M. Table 14. Discrepancy Between Medicare Record and IOM Abstract and the Correct Data Source for Selected Categories of Procedures as Coded by SSA (weighted percent) Categories
No discre-pancy
Medicare record
IOM abstract
Either
Neither
Total
All procedures (n = 4745)
78.9
1.7
17.3
1.7
0.4
100.0%
No procedure coded (n = 2990)
89.7
-
8.9
1.4
-
100.0
Procedure coded using CPT (n = 1754)
56.6
5.2
34.7
2.3
1.2
100.0
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ANALYSIS
43
In summary, for about sixty-three percent of the Medicare records, no principal procedure is coded, presumably because no procedure was performed and/or the hospital billing office did not submit this information. When principal procedure is coded, there was agreement between the IOM abstract and the Medicare record in about fifty-seven percent of the cases. The reasons for discrepancy usually stemmed from coding problems, rather than ordering, and the most frequent reason was the failure to adequately review the medical record before recording a narrative on which the code for principal procedure was based. ANALYSIS OF CLAIMS INFORMATION As noted earlier, the analyses completed to this point are based on comparisons between the Medicare record and the IOM abstract, assuming that data on the Medicare record accurately reflect information from the claim form submitted by the hospital to the fiscal intermediary and, eventually, to HCFA. It is possible that the information submitted on the claim form is not an accurate reflection of the patient's condition as determined by the field team using UHDDS definitions. In such cases, one would expect that the information on the Medicare record would not agree with that contained on the IOM abstract. On the other hand, the information submitted on the claim form may accurately reflect the patient's condition and agree with the IOM abstract, but still differ from that contained on the Medicare record--either because of a special Medicare coding guideline or because of a coding error. To help determine the relative influence of these possibilities on the quality of data, the extent of agreement between information coded on the IOM abstract and that contained on the hospital copy of the Medicare claim form was analyzed. Whenever the field team discovered a discrepancy between the Medicare record and the IOM abstract in which the former was not correct, the hospital's copy of the appropriate claim was consulted. Narrative information from the claim was copied on the abstract form and the first listed diagnosis or related procedure was coded, since this is the customary procedure for Medicare coders (there are exceptions, as will be noted later). Codes from the initial abstracting of the medical record and the item listed on the claim form (both assigned by the field team) were compared to determine whether or not the information submitted by the hospital on the claim form accurately reflected the patient's condition. The results of these comparisons are presented below, beginning with principal diagnosis and then principal procedure. Principal Diagnosis Table 15 shows that for about seventy percent of the cases for which discrepancies were found on principal diagnosis, the information submitted by the hospital billing office to the intermediary did not accurately reflect the patient's condition. Of those cases for which
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ANALYSIS
44
Table 15. Agreement Between Principal Diagnosis Coded from the Claim Form and the IOM Abstract for Discrepant Medicare Records (weighted percent) Agree
Disagree
Total
Four-digit (n = 1402)
29.6
70.4
100.0%
Three-digit (n = 1244)
29.4
70.6
100.0%
the information was incorrect, about seventy-five percent of the principal diagnoses on the Medicare record agreed with the first-listed diagnosis on the claim form. In other words, even though the information submitted by the hospital was incorrect, it was accurately coded by Medicare coders. For the remaining twenty-five percent, the Medicare record did not agree with the first-listed diagnosis on the claim form. The data gathered by the field team did not permit a direct assessment of the frequency with which discrepancies might stem from either the correct application of a special Medicare coding guideline that required coding something other than the first-listed item, or from an error. Therefore, information from all cases with discrepancies on principal diagnosis between the Medicare record and IOM abstract (where the field team had obtained a copy of the appropriate claim form) was submitted to senior HCFA RRAs to determine the accuracy of the HCFA coding function. They were asked to code the information and also to indicate whether they had simply coded the first-listed item or had applied a special coding guideline. It might be noted that this exercise approximates a reliability assessment. But because different, and more experienced coders performed the recoding, it is not a test of reliability (repeatability) in the customary sense. The results of the re-coding of diagnostic information are presented in Table 16 and Table 17. They indicate that there is variability in the Medicare coding function, but do not lead to firm conclusions about the reasons for variability. More specifically, with four-digit comparisons where the hospital claim form accurately reflected the patient's condition, the Medicare re-code agreed with the claim form in 41.8 percent of the cases. Since none of the initial Medicare codes reflected the claims information for these cases, this suggests that the initial Medicare record may have contained erroneous information introduced by Medicare coders. For the remaining 58.2 percent of the cases where the claims data were correct, the Medicare re-code did not agree with the first-listed diagnosis on the claim. As expected, special guidelines were applied in 51.3 percent of those cases. For the remaining 48.7 percent, the first-listed diagnosis was coded, but the discrepancy persisted.
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ANALYSIS
45
Table 16. Reliability of Medicare Coding of Four-Digit Principal Diagnosis for Cases Where There Were Discrepancies Between Medicare Record and IOM Abstract Percent of cases in which Medicare code agrees with first-listed diagnosis on claim form Initial coding Accuracy status and coding method
Agree
Re-coding for IOM study Disagree
Total
Agree
Disagree
Total
First-listed
54.8 (82.2)
45.2 (48.7)
100.0 (62.7)
Special convention
19.9 (17.8)
80.1 (51.3)
100.0 (37.3)
IOM abstract = claim firstlisted (claim reflects patient condition) n = 344
Total*
0%
0%
0%
41.8 (100.0)
58.2 (100.0)
100.0% (100.0%)
First-listed
74.5
25.5
100.0
82.7 (70.5)
17.3 (38.7)
100.0 (61.8)
Special convention
#
#
#
55.8 (29.5)
44.2 (61.3)
100.0 (38.2)
Total
74.5
25.5
100.0%
72.5 (100.0)
27.5 (100.0)
100.0% (100.0%)
IOM abstract # claim firstlisted (claim does not reflect patient condition) n = 1058
Greater consistency is found between the Medicare initial code and recode for those cases where the hospital claim did not accurately reflect the patient's condition. The overall levels of agreement between the Medicare code and the claim form were 74.5 percent for the original records and 72.5 percent for the re-codes. As expected, conventions were used for 61.3 percent of the cases in which the two did not agree. The residual 38.7 percent, for which the first-listed diagnosis was coded
*Since only cases with discrepancies were submitted for re-coding, the totals for the initial coding are automatically zero. #The absence of this information created the need for the re-coding exercise reported in the right-hand side of the table.
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ANALYSIS
46
but the discrepancy remained, may be regarded as error, assuming the IOM field team was correct. One may hypothesize that the generally higher consistency of Medicare coding of cases in which the claims information was incorrect suggests that a rather simplistic, but erroneous narrative was submitted by the hospital and was easily coded by the Medicare coders. On the contrary, the smaller number of cases for which discrepancies between Medicare and IOM were detected, but the claims information was correct, may have been more complicated cases for which the claims information was less straightforward. Similar comparisons for three digit diagnostic comparisons are presented in Table 17. When the claims data were accurate, the level of agreement between the Medicare re-code and claim form increases from 41.8 to 62.7 percent. Otherwise, the findings resemble those for four-digit comparisons. In general, a portion of the discrepancies between the Medicare record and the first-listed item on the claim form stems from the appropriate application of special Medicare coding guidelines. However, there is an unexplained residual that probably reflects some error. There is definitely variability among HCFA coders. Nevertheless, the major factor contributing to discrepancies appears to be the failure of hospitals to provide accurate billing information. Table 17. Reliability of Medicare Coding of Three-Digit Principal Diagnosis for Cases Where There Were Discrepancies Between Medicare Record and IQM Abstract Percent of cases in which Medicare code agrees with first-listed diagnosis on claim form Initial coding Accuracy status and coding method
Agree
Re-coding for IOM study Disagree
Total
Agree
Disagree
Total
First-listed
65.9 (84.2)
34.1 (42.0)
100.0 (49.0)
Special convention
42.0 (15.8)
58.0 (58.0)
100.0 (51.0)
62.7 (100.0)
37.3 (100.0)
100.0 (100.0
IOM abstract = claim firstlisted (claim reflects patient condition) n = 299
Total*
0%
0%
0%
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ANALYSIS
47
Percent of cases in which Medicare code agrees with first-listed diagnosis on claim form Initial coding Accuracy status and coding method
Re-coding for IOM study
Agree
Disagree
Total
Agree
Disagree
Total
First-listed
76.2
23.8
100.0%
84.3 (69.7)
15.7 (59.1)
100.0 (61.8)
Special convention
#
#
#
59.1 (30.3)
40.9 (40.9)
100.0 (38.2)
Total
76.2
23.8
100.0%
74.6 (100.0)
25.4 (100.0)
100.0% (100.0%)
IOM abstract # claim firstlisted (claim does not reflect patient condition) n = 945
Principal Procedure Table 18 shows that for about forty-three percent of the cases for which discrepancies were found on principal procedure, the information submitted by the hospital billing office to the intermediary did not accurately reflect the principal procedure performed during the hospital stay, as determined by the field team. This level of inaccuracy is increased to 55.2 percent when the analysis is limited to those cases where the Medicare record indicated by the use of a CPT code that a surgical procedure was performed. Of those cases for which the information was incorrect, about seventy-six percent of the principal procedures on the Medicare record agreed, with the data on the claim form coded by the field team. For the remaining twenty-four percent, the Medicare record did not agree with the claim form data, presumably because of the appropriate application of a special HCFA coding guideline or because of a mistake. As with diagnoses, the data gathered by the field team did not permit a direct assessment of the frequency with which discrepancies might stem from either the correct application of a Medicare coding guideline or
*Since only cases with discrepancies were submitted for re-coding, the totals for the initial coding are automatically zero. #The absence of this information created the need for the re-coding exercise reported in the right-hand side of the table.
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ANALYSIS
48
Table 18. Agreement Between Principal Procedure Coded from the Claim Form and the IOM Abstract for Discrepant Medicare Records (weighted percent) Agree
Disagree
Total
All procedures (n = 805)
56.7
43.3
100.0%
No procedure coded (n = 266)
80.3
19.7
100.0
Procedure coded using CPT (n = 539)
44.8
55.2
100.0
an error. To ascertain this, information from all cases with discrepancies on principal procedure between the Medicare record and the IOM abstract (where the field team had obtained a copy of the appropriate claim forms) was submitted to senior HCFA RRAs to determine the reliability of the HCFA coding function. The results of the re-coding of information on procedures (see Table 19) show a variability in the Medicare coding function similar to that found with diagnoses. For cases where the hospital claim form accurately reflected the procedure performed during the hospital stay, the Medicare re-code agreed with the claim form in 60.4 percent of the cases. Since none of the initial Medicare codes reflected claims information for the cases, this suggests that erroneous information on the initial Medicare record may have been introduced by the Medicare coders. For the remaining 39.6 percent of the cases where the hospital claim data were accurate, the Medicare recode did not agree with the first-listed procedure on the claim. Special coding guidelines were applied to only 39.2 percent of those cases. For the remaining 60.8 percent, the first-listed procedure was coded, but the discrepancy remained. Where the hospital claim did not accurately reflect the procedure performed during the hospital stay, agreement between the Medicare code and the claim form reached 75.8 percent for the original records and 65.6 for the re-codes. As expected, special conventions were used in about sixty-one percent of the cases where the recode and the first-listed claim item did not agree. The residual 39.5 percent may be regarded as error, assuming the IOM field team was correct. This examination of data on procedures confirms the statement made previously regarding the accuracy of diagnostic information. Some discrepancies stem from the correct use of special Medicare coding guidelines. There is also variability among Medicare coders and probably a certain amount of error. Nevertheless, the major factor influencing the accuracy of diagnostic and procedure data is the failure of hospitals to provide accurate billing information.
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ANALYSIS
49
Table 19. Reliability of Medicare Coding of Principal Procedure for Cases Where There Were Discrepancies Between Medicare Record and IOM abstract Percent of cases in which Medicare code agrees with principal procedure on claim form Initial Coding Accuracy status and coding method
Agree
Disagree
Re-coding for IOM study Total
Agree
Disagree
Total
First-listed
67.0 (80.8)
33.0 (60.8)
100.0 (72.9)
Special convention
42.8 (19.2)
57.2 (39.2)
100.0 (27.1)
IOM abstract = claim firstlisted (claim reflects patient care) n = 205
Total*
0%
0%
0%
60.4 100.0
39.6 (100.0)
(100.0) (100.0)
First-listed
75.8
24.2
100.0
78.2 (74.2)
21.8 (39.5)
100.0 (62.2)
Special convention
#
#
#
44.9 (25.8)
55.1 (60.5)
100.0 (37.8)
Total
75.8
24.2
100.0%
65.6 (100.0)
34.4 (100.0)
100.0% (100.0)
IOM abstract # claim firstlisted (claim does not reflect patient care) n = 600
*Since only cases with discrepancies were submitted for re-coding, the totals for the initial coding are automatically zero. #The absence of this information created the need for the re-coding exercise reported in the right-hand side of the table.
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ANALYSIS
50
INFLUENCE OF DIAGNOSTIC DATA RELIABILITY ON UTILIZATION STATISTICS The analyses of diagnostic information presented to this point are based on cases for which a specific diagnosis was listed on the Medicare record as principal and the field team either agreed or disagreed with that determination. If there was a disagreement, the diagnosis on the Medicare record may be regarded as a false positive. However, there may also be cases for which the same specific diagnosis should have been listed as principal, but was not. These cases may be regarded as false negatives. The sampling plan permits an estimate of the extent to which both types of errors occur. More importantly, their influence on approximations of admission rates and lengths of stay can be explored. Table 20 helps to explain the methods for calculating these estimates. Table 20. Calculation of Net and Gross Difference Rates in Designation of Principal Diagnosis IOM abstracts coded as principal
Medicare record coded as principal Specific diagnosis
Other
Total
Specific diagnosis
a
b
a+b
Other
c
d
c+d
Total
a+c
b+d
N
Percent with no discrepancy = Gross difference rate = Net difference rate =
In Table 20, the cases included in cell “a” are those for which the specific diagnosis was coded as principal on both the Medicare record and IOM abstract. The total number of differences affecting that figure for any specific diagnosis is equal to the number of cases included in that class on the original Medicare record, but not on the IOM abstract (cell “c”), plus the number included in that class on the
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ANALYSIS
51
IOM abstract, but not on the Medicare record (cell “b”). Cell “d” includes all cases from the study population which do not have the specific diagnosis coded as principal on either data source. The sum of the number of cases in cells “b” and “c,” divided by the total number of cases in the population irrespective of diagnosis (N), may be termed the gross difference rate for the diagnosis in question. It reflects aggregate errors and usually includes differences in both directions, which may be partly off-setting. The net difference rate is the difference between “b” and “c,” divided by N. It is an estimate of the non-offsetting part of the gross error. A negative net difference rate indicates that the influence of false positives is greater than false negatives. [1] Net and gross difference rates for the study diagnoses are in Appendix H. Net and gross difference rates are useful in comparing the relative accuracy of different diagnoses and for measuring changes in the reliability of data over time. In interpreting them, however, the reader should note that a change in the frequency of occurrence of a particular diagnosis in a population is not necessarily reflected in net and gross difference rates. The number of cases for which both assessments agree (cell “a”) may change without altering net and gross difference rates. The implications for reliability of similar net and gross difference rates for diagnoses with dissimilar incidence rates may be quite different. Therefore, the proportion of cases for which there is concordance between the abstract and re-abstract must be taken into account. If the concepts of false negatives and false positives are used in calculating admission rates and lengths of stay, the operational implications of net and gross difference rates are easier to understand. Table 21 contains estimates of the distributions of specific diagnoses. Because of the absence of a population-based denominator customarily used to calculate admission rates, a proxy measure was computed based on the number of abstracts for Medicare patients with a particular diagnosis divided by the total number of Medicare admissions in the twenty percent sample. This is referred to as a “rate,” although it is not, in the usual sense. The basic admission rates are based on the number of cases for which both the Medicare and IOM abstracts have the same principal diagnostic code (cell “a”) divided by the total number of admissions. The Medicare admission rates are calculated by dividing the total number of Medicare records with a specific diagnosis (including false positives) by the total number of admissions. The IOM admission rates are calculated by dividing the total number of IOM abstracts with a specific diagnosis (including false negatives) by the
1 U. S. Department of Commerce, Bureau of the Census, The Current Population Survey Reinterview Program: Some Notes and Discussion, Technical Paper No. 6 Washington, D. C.: U. S. Government Printing Office, 1963), pp. 8-9.
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ANALYSIS
52
total number of admissions. The rates are analyzed to three and four digits. However, the IOM rates are the same for both four and three digits because the cases for which the Medicare records and IOM abstracts disagreed at only the fourth digit are shifted from cell “b” to cell “a” in the three-digit comparisons. The numerator (a + b) remains the same and, therefore, the rate does not change.[2] As one would expect, the basic rates usually increase as one moves from four to three digits. The Medicare admission rates are consistently higher than the basic admission rates for both three and four-digit comparisons, because they include the false positives. If the number of false positives is roughly equivalent to the number of false negatives, then the Medicare rates may be an acceptable approximation to the “actual” rates. However, the IOM admission rates, which include the false negatives, are higher than the Medicare rates with the exception of chronic ischemic heart disease, diabetes, and malignant neoplasm of bronchus and lung. The under-estimation of admissions using Medicare data is particularly noticeable for cerebrovascular disease and congestive heart failure. This analysis can also be performed using cases from the entire DRG and comparing the diagnoses using the AUTOGRP classification system. When this approach is used (see Table 22), results are similiar to those obtained for the specific diagnoses within DRGs (see Table 21). Medicare data under-estimate the number of admissions with the exception of diabetes, miscellaneous diseases of the intestine and peritoneum, malignant neoplasm of the respiratory system, and, most importantly, ischemic heart disease. The influence of false positives and false negatives on length of stay may also be examined if the number of days is divided by the number of abstracts in the appropriate groupings of cells, as shown in Table 23. Four-digit lengths of stay for specific diagnoses are not consistently different from three digit. Lengths of stay based on Medicare data (including false positives) are about equally likely to be higher or lower than the corresponding basic numbers for both three and four-digit comparisons. This is also true for the IOM lengths of stay (including false negatives). With the exception of fracture neck of femur (where the IOM length-of-stay is about five days longer than either the basic or Medicare average), most differences are within a range of one day in either direction. When the entire DRG and AUTOGRP classification are used (see Table 24) it is equally difficult to detect consistent differences. The use of Medicare data to calculate diagnostic-specific admission rates may result in systematic distortions. The differences between IOM and Medicare data for diagnostic-specific lengths-of-stay are not consistent; nevertheless, they do exist.
2 The rates in Table 21, Table 22, Table 23 through Table 24 were not adjusted to account for the small number of cases for which there were discrepancies and the Medicare records were correct. Such adjustments were made on an exploratory basis with the previous data set. The changes in the rates were minuscule and insufficient to justify the added complexity of the calculations.
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ANALYSIS
53
Table 21. Influence of False Positives and Negatives on Proxy Admission Rates for Specific Diagnoses Within a Diagnosis Related Group (times 1,000) Based on All Medicare Admissions in the Twenty Percent Sample Basic admission rate rate a/N
Medicare admissions a+c/N
Four-digit
Three-digit
Four-digit
Three-digit
Chronic ischemic heart disease
36.2
38.0
96.7
98.5
52.2
Cerebrovascular diseases
40.1
47.1
50.7
57.7
71.3
Fracture, neck of femur
14.4
19.1
15.7
20.4
22.3
Cataract
29.1
29.4
29.7
30.0
30.7
Acute myocardial infarction
16.4
18.5
22.2
24.3
27.4
Inguinal hernia without mention of obstruction
12.3
12.3
12.7
12.7
13.8
Diabetes mellitus
12.6
14.2
23.7
25.4
21.1
Hyperplasia of the prostate
18.6
18.6
21.3
21.3
22.4
Bronchopneumonia-organism not specified and pneumoniaorganism and type not specified
21.2
21.2
26.7
26.7
33.8
Cholelithiasis/cholecystitis
12.1
15.0
15.2
18.0
21.5
Intestinal obstruction without mention of hernia
5.4
6.4
8.3
9.3
10.1
Congestive heart failure and left ventricular failure
9.7
10.6
16.3
17.1
34.1
Diverticulosis of the intestine
12.6
12.6
14.5
14.5
19.1
Bronchitis
9.7
9.7
12.6
12.6
14.7
Malignant neoplasm of bronchus and and lung
9.2
9.2
11.6
11.6
11.1
Principal diagnosis
IOM admissions rate a+b/N
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ANALYSIS
54
Table 22. Influence of False Positives and Negatives on Proxy Admission Rates for all Diagnoses within a Diagnosis Related Group (times 1,000) Based on all Medicare Admissions in the Medicare Admissions in the Twenty Percent Sample Diagnosis related group
Basic admission rate a/N AUTOGRP
Medicare admission rate a +c/N AUTOGRP
IOM admissions a+b/N
Ischemic heart disease except AMI
49.9
107.7
66.9
Cerebrovascular diseases
58.4
68.9
71.3
Fractures
42.7
47.6
49.6
Diseases of the eye
35.9
37.2
38.0
Acute myocardial infarction
18.5
24.3
27.4
Hernia of abdominal cavity
26.0
28.1
30.9
Diabetes mellitus
14.2
25.4
21.1
Diseases of the prostate
20.8
23.7
25.0
Pneumonia
26.3
30.5
36.9
Diseases of the gall bladder and bile duct
17.9
21.4
23.7
Miscellaneous diseases of the intestine and peritoneum
11.6
19.5
17.7
Heart failure
10.6
17.5
35.3
Enteritis, diverticula and functional disorders of intestine
16.0
18.5
23.9
Bronchitis
11.4
14.3
14.7
Malignant neoplasm of respiratory system
10.3
13.6
12.0
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ANALYSIS
55
Table 23. Influence of False Positives and Negatives on Average Lengths of Stay for Specific Diagnoses within a Diagnosis Related Group Based on all Medicare Admissions in the Twenty Percent Sample Basic length of stay
Medicare length of stay
Principal diagnosis
Four-digit
Three-digit
Four-digit
Three-digit
IOM length of stay
Chronic ischemic heart disease
10.0
10.0
10.7
10.7
10.4
Cerebrovascular diseases
12.8
12.8
12.6
12.6
12.2
Fracture, neck of femur
20.8
20.8
20.1
20.4
25.7
Cataract
5.0
5.0
5.1
5.1
5.4
Acute myocardial infarction
14.4
14.2
13.7
13.6
13.6
Inguinal hernia without mention of obstruction
7.1
7.1
7.2
7.2
7.3
Diabetes mellitus
10.9
10.6
12.2
11.9
10.7
Hyperplasia of the prostate
12.2
12.2
12.2
12.2
12.5
Bronchopneumonia-organism not specified and pneumonia-organism and type not specified
10.9
10.9
11.3
11.3
10.7
Cholelithiasis/cholecystitis
12.8
13.2
12.1
12.5
13.4
Intestinal obstruction without mention of hernia
12.2
12.7
13.8
14.0
11.3
Congestive heart failure and left ventricular failure
9.4
9.2
10.0
9.8
11.3
Diverticulosis of intestine
8.3
8.3
9.0
9.0
10.6
Bronchitis
7.9
7.9
8.3
8.3
8.2
Malignant neoplasm of bronchus and lung
12.8
12.8
11.8
11.8
12.8
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ANALYSIS
56
Table 24. Influence of False Positives and Negatives on Average Lengths of Stay for all Diagnoses within a Diagnosis Related Group Based on all Medicare Admissions in the Twenty Percent Sample Diagnosis related group
Basic length of stay
Medicare length of stay
IOM length of stay
Ischemic heart disease except AMI
9.7
10.7
10.1
Cerebrovascular diseases
12.2
12.2
12.2
Fractures
19.1
18.5
18.9
Diseases of the eye
5.1
5.3
5.2
Acute myocardial infarction
14.2
13.6
13.6
Hernia of abdominal cavity
9.9
9.9
9.6
Diabetes mellitus
10.6
11.9
10.7
Diseases of the prostate
12.0
11.8
12.2
Pneumonia
10.8
11.2
10.6
Diseases of the gall bladder and bile duct
13.0
12.7
14.4
Miscellaneous diseases of the intestine and peritoneum
11.2
12.3
11.0
Heart failure
10.1
10.4
11.7
Enteritis, diverticula and functional disorders of intestine
8.1
8.7
10.2
Bronchitis
7.9
8.2
8.2
Malignant neoplasm of respiratory system
13.0
11.8
13.0
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ANALYSIS
57
Influence of Hospital Characteristics To gain further insight into the influence of hospital characteristics on reliability of data, selected aspects of the process by which claims information is obtained within the hospital and forwarded to the fiscal intermediary were examined. Each hospital or abstracting process characteristic was cross-tabulated by the percent of abstracts for which there were no discrepancies between the Medicare record and the IOM abstract. The effect on diagnoses was measured at the four-digit, three-digit and AUTOGRP levels of comparison; the influence on procedures was also examined. A chi-square test of significance was calculated to determine the independence of the two variables.[3] As shown in Table 25, the influence of most variables was statistically significant. Interpretation is difficult, however, because the resulting relationships were not always consistent for all dependent variables. Occasionally the relationships were statistically significant, but not meaningful--presumably because of inter-correlations with other variables which more directly affect the quality of data. The more important relationships are summarized below. Unless otherwise noted, the effect of AUTOGRP was the same as the three-digit comparison.
3 Because of the instability of the weighted numbers, the chi-square was based on a re-distribution of the unweighted numbers according to the weighted percentages. A statistically significant relationship was assumed if the chance of its occurrence was less than .05.
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ANALYSIS 58
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ANALYSIS
Characteristics
59
Four-digit diagnosis
Three-digit diagnosis
Procedures
Hospital Characteristics Geographic region
Northeast region = less accurate data
Same as four-digit
Same as four-digit
Population density
Not significant
Non SMSA = more accurate
Non SMSA = more accurate
Control
Not significant
Not significant
Proprietary = more accurate; voluntary = less accurate
Bed size
Smaller hospitals = better data
Same as four-digit
Same as four-digit
The checklist included an item intended to elicit information about the training of the person reviewing the medical record to retrieve claims data, regardless of whether the function was performed in the billing office or elsewhere. In hospitals where portions of the medical record are transmitted to the billing office, persons trained in billing office procedures but without medical records experience were associated with better data than were persons without that training. Presumably, the training would include methods for retrieving diagnostic and procedural information from the medical record. Where a discharge list or some other summary of abstracted information is used by the billing office to complete the claim form, the data were better if the abstracted information was provided by either a physician or RRA. The reliability of data across categories was less consistently influenced by the source of abstracted information used by billing. This may suggest that the care with which the information is either recorded or abstracted and the training of persons involved in those functions is more important than the actual document (typed discharge list, computerized discharge list, face sheet, etc.) In any case, when the billing office was provided with diagnostic codes, rather than narrative information, the claims data tended to be more accurate. Similarly, the data were more accurate in hospitals where up-dated diagnostic information is regularly submitted to the billing department, as well as to the fiscal intermediary. The various definitions for principal diagnosis and principal procedure used by the study hospitals were expected to influence the reliability of data. The expectation was confirmed, but the findings are perplexing. The Medicare definition for principal diagnosis was associated with more
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ANALYSIS
60
accurate data, despite the fact that the field team used the UHDDS definition as the basis for comparison. It is possible, however, that hospitals which profess to use the Medicare definition do not consistently apply it. Data were least accurate when the first-listed diagnosis on the face sheet was routinely used for designating a principal diagnosis. Definitions for principal procedure were not significantly associated with the accuracy of data. The accuracy of both diagnostic and procedure data varied by geographic region. Invariably, hospitals in the Northeast region provided less accurate data than hospitals in the South, West, or North Central regions. Hospitals outside a Standard Metropolitan Statistical Area (SMSA) provided more accurate data than those located within a SMSA, although the differences were statistically significant only for diagnoses at three-digits and for principal procedure. Arrangements for hospital control did not influence the accuracy of diagnostic data, although proprietary hospitals had more accurate data for principal procedure. Hospitals with fewer beds were found to have more accurate data for both diagnoses and procedures. In an attempt to determine the relative influence of hospital characteristics on reliability of data, simple and multiple regressions were performed using the characteristics as independent variables. Census region was the only independent variable which was consistently associated with the accuracy of diagnostic and procedure data. For all regressions, the amount of variance explained was low, reaching a maximum of 0.125. The analysis of hospital and billing process characteristics may be useful in instituting program changes to increase the accuracy of diagnostic and procedure data. The reader should note, however, that this information was obtained informally during visits to the study hospitals and the degree of subjectivity in the responses could not be ascertained. In addition, several of the process characteristics may be correlated within a particular hospital, even though there was very little correlation among these characteristics for all hospitals combined. Despite these limitations, it appears that billing office personnel with training in billing procedures, but no medical record experience, may provide accurate diagnostic information if accurate information is provided by the medical record department. If RRAs abstract and code the information and submit it to the billing office, the data forwarded to the fiscal intermediaries tend to be more accurate. The role of physicians in recording patient information is an important variable. In addition, the management practice of having medical record departments submit updated diagnostic information to the billing office and the fiscal intermediaries aids in increasing the accuracy of data. Of the structural characteristics, only the geographic region of the country in which hospitals are located and hospital size were significantly and consistently linked with the accuracy of data.
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SUMMARY AND RECOMMENDATIONS
61
Chapter 4 SUMMARY AND RECOMMENDATIONS
The study reported here was conducted by the Institute of Medicine to assess the reliability of six selected items of information describing the utilization of hospital services by a sample of Medicare beneficiaries during calendar year 1974. The information was obtained from a Medicare record created from a data base maintained by the Health Care Financing Administration (HCFA). The analysis was needed to assist in identifying an existing data base that might be used in assessing the effects of Professional Standards Review Organizations (PSROs), since baseline data were not gathered prior to implementing the PSRO program. The study is a logical extension of an earlier IOM examination of the reliability of hospital utilization data compiled by private abstracting services and based on abstracts of medical records. The Medicare data and information compiled by abstracting services constitute two of the very few existing nationbl data bases for monitoring utilization of health services. Both are potentially useful for a variety of health services research, policy, and administrative needs, in addition to the PSRO evaluation, provided they are sufficiently reliable. The accuracy of six information items on the Medicare record was determined by comparing those items with the results of an independent abstracting of patient medical records by a trained field team and noting the frequency and type of discrepancies. In addition, selected hospital characteristics and the process by which data are forwarded by the hospitals to the fiscal intermediaries were studied to determine if any were related to the accuracy of abstracted data and to assist in identifying areas for improvement. The analysis showed that information on hospital admission date, discharge date, and patient's sex was highly reliable. For all principal diagnoses combined, however, when codes were compared to four digits, the Medicare record and the IOM abstract agreed for only 57.2 percent of the cases. For a small percent of discrepancies, the field team was unable to state with certainty which data source was correct. If these cases are redistributed, the increased percent of correct Medicare records ranges from 59.8 to 64.1. The presence of additional diagnoses was accurately
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SUMMARY AND RECOMMENDATIONS
62
noted on 74.5 percent of the records. The Medicare record and the IOM abstract agreed on principal procedure in 78.9 percent of the cases. These findings are very similar to those from the earlier study (see Appendix I). In particular, when the principal diagnoses on the original abstract and IOM re-abstract were compared to four digits for the total data base (both Medicare and Medicaid patients), 65.2 percent of the cases were in agreement; for the Medicare portion of the data, 64.4 percent agreed. Comparable levels of agreement on principal procedure in the earlier study were 73.2 percent for the total data base and 72.5 percent for the Medicare patients alone. Interpretation of the findings for diagnoses and procedures in this Medicare study is extremely difficult for a variety of reasons. The information is highly technical and almost every statement needs to be qualified. The limitations of the study should also be noted. The set of information items examined is very restricted and relevant primarily for utilization statistics and, in particular, diagnostic-specific utilization statistics. The data were gathered in only seventy-one hospitals, although they were selected and weighted to be representative of all Medicare discharges for persons aged sixty-five and older from short-term general hospitals during 1974. The weights used in the analysis may have introduced some instability. There may have been instances in which the field work was not reliable. Finally, the role of the fiscal intermediary in data reliability is not known. Although some conclusions are reached regarding the relative contributions of hospitals and the Health Care Financing Administration to the accuracy of Medicare records, they are based on the assumption that the intermediaries do little more than simply forward information received from the hospitals to HCFA. If additional error is introduced by the intermediary, it is not reflected in the IOM findings. In general, extreme care was taken in designing the sample, conducting the field work, and processing and analyzing the data. Any error associated with the limitations specified above are expected to be small. Despite these limitations, it is possible to draw some rather firm conclusions about the accuracy of information on diagnoses and procedures contained on the Medicare record and the factors that appear to influence reliability. The credibility of the findings is heightened by the striking similarity to the findings of the earlier study of the reliability of utilization data processed by private abstracting firms. CONCLUSIONS 1. Individual diagnoses contribute to the overall level of reliability. The data were quite reliable for some diagnoses. As examples, no discrepancies were found on 97.3 percent of the records for patients with cataract; 96.7 percent for inguinal hernia without mention of obstruction; 89.9 percent for bronchitis; 87.1 percent for hyperplasia of the prostate; and 86.5 for diverticulosis of the intestine--all
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SUMMARY AND RECOMMENDATIONS
63
analyzed to four-digits. However, discrepancies were found for 63.2 percent of the records for patients with chronic ischemic heart disease; 50.3 percent for diabetes; 41.9 percent for intestinal obstruction without mention of hernia; 41.6 percent for congestive heart failure; and 41.5 percent for cerebrovascular disease. Similar findings resulted from the previous analysis (see Appendix J for comparisons of reliability of diagnoses common to both studies). In particular, no discrepancies were found on 94.3 percent of the records for Medicare patients with cataract; and 87.9 percent for inguinal hernia without mention of obstruction. Discrepancies were found for 72.1 percent of the records for Medicare patients with chronic ischemic heart disease; and 39.5 percent for diabetes mellitus. 2. For most diagnoses that had a relatively low level of reliability, the percent of cases with no discrepancy increased if the analysis was confined to those with no additional diagnoses. This was particularly evident for chronic ischemic heart disease, acute myocardial infarction, diabetes, congestive heart failure, and intestinal obstruction without mention of hernia. Since the presence of additional diagnoses influences the accuracy of data, adjustments in analysis might be made if the fact of co-morbidity were accurately noted on the Medicare claim form. However, this occurs for only 74.5 percent of the discharges. In most cases, the inaccuracy stems from an incomplete review of the medical record within the hospital. The prior study raised the possibility that the designation of primary diagnosis was likely to be more difficult for cases with co-morbidity. Directly comparable data were not gathered, however. 3. The reliability of diagnostic information varies according to the level of coding refinement. For all diagnoses combined, AUTOGRP comparisons were most reliable and comparisons to four digits of the diagnostic codes were least reliable. The increased reliability of the more aggregated data must be balanced against the loss of precision in the information, however. AUTOGRP was not used in the earlier study, but three-digit comparisons were more reliable than four-digit. 4. There does not appear to be a systematic bias on the part of hospitals to submit on the claim an “admitting diagnosis” (reflecting the patient's condition at the time of hospital admission) instead of a “principal diagnosis” (established after a more thorough review of the medical record). This is generally true regardless of whether discrepancies were found between the principal diagnoses on the Medicare record and the IOM abstract. The earlier report did not include a similar analysis.
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SUMMARY AND RECOMMENDATIONS
64
5. Sometimes the field team detected a discrepancy between their work and the Medicare record, but after re-examining the patient's medical record, they were unable to state with certainty which was correct. Instead, they concluded that it was a matter of judgment and that data on either document was equally acceptable. The problem stemmed from difficulty in determining which diagnosis should be regarded as principal. This situation was obtained for 4.6 percent of all diagnoses combined and for 1.7 percent of all procedures. Comparable percents for individual diagnoses ranged from 7.6 percent for chronic ischemic heart disease to 1.4 percent for bronchopneumonia and pneumonia unspecified. Similar figures in the earlier study were somewhat higher--10.7 percent for all diagnoses combined and 16.3 percent for all procedures. For individual diagnoses the percents ranged from 0.3 for fracture of neck of femur to 18.0 for low back pain. The presence of such cases in both studies suggests that for some patients it may be very difficult to isolate a single primary diagnosis responsible for hospital admission--either because of inadequacies in physician's recording habits or because of limitations in current diagnostic classification schemes. 6. The difficulty of determining a principal diagnosis with certainty was confirmed in the independent assessment of the field work (see Appendix F). The levels of discrepancies between the consultant and the field team are not directly comparable to those between the field team and the Medicare record. However, when the consultant and field team disagreed, quite often both also disagreed with the Medicare record. This happened for about fifty percent of the discrepant principal diagnoses and about forty-one percent of the discrepant principal procedures in the assessment of the field work. In other words, each of the three data sources contained different pieces of information--all based on the same patient medical record. Similar variability was found in the assessment of the field work in the earlier study. 7. The reasons for discrepancy vary by diagnosis. For most diagnoses with high discrepancy rates and a likelihood of co-morbidity where the IOM abstract was found to be correct (especially chronic ischemic heart disease, diabetes, and congestive heart failure), discrepancies occurred primarily because of erroneous selection of principal diagnosis (an ordering discrepancy), rather than mistakes in assigning a code number (a coding discrepancy). Diagnoses whose reliability improved with less specific coding tended to have coding discrepancies. These findings affirm the conclusions of the earlier study.
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SUMMARY AND RECOMMENDATIONS
8.
65
Several factors affect the reliability of information on procedures. An incomplete review of the medical record is the single most frequent reason for error and accounts for about forty percent of the discrepancies when the IOM abstract was correct. For about sixty-three percent of the discharges, no procedure was coded, presumably because no procedure was performed and/or the hospital billing office did not submit this information to the fiscal intermediary. The field team agreed with this determination in about ninety percent of the cases. When the analysis was confined to cases for which a procedure was coded on the Medicare record, the level of agreement on principal procedure dropped to about fifty-seven percent. The reasons for discrepancies on principal procedure are not directly comparable between studies. Nevertheless, the percents of discharges for which no procedure was coded are similar (65.2 percent in the initial study). The field team agreed with this determination in 86.9 percent of the cases. The level of agreement dropped to 64.8 percent when only cases containing a procedure code were compared. 9. When the IOM abstract was correct, many of the discrepancies originated in hospital procedures. Many hospitals routinely select the first-listed diagnosis on the face sheet of the medical record as the principal diagnosis, regardless of additional documentation in the record. The other major related reason that led to discrepancies (both ordering and coding) was failure to adequately review the medical record before designating a principal diagnosis and/or principal procedure. Perhaps because of these practices, the claims information submitted to the fiscal intermediary was frequently incorrect. More specifically, when there were discrepancies between the Medicare record and IOM abstract, the information on principal diagnosis submitted by the hospital on the claim form did not accurately reflect the patient's condition for about seventy percent of the cases. Comparable figures for principal procedure were about forty-three percent for all claims for which discrepancies were detected and about fifty-five percent when the analysis was confined to cases for which the Medicare record indicated that a procedure had been performed. These findings confirm similar, but more tentative, impressions raised in the earlier report. The two studies reinforce one another, but the findings from the Medicare study about the influences of hospital policies on data reliability are more persuasive. 10. Coding by HCFA coders also influences the reliability of the Medicare record, but the variability within the coding function is difficult to specify. When cases with correct claims information but incorrect Medicare information for principal diagnosis were submitted to more senior Medicare RRAs for re-coding, agreement with the claims codes reached about forty-two percent. The comparable figure for principal procedure was about sixty percent. Though the level of agreement increased, an unexplained residual of discrepancies remained,
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SUMMARY AND RECOMMENDATIONS
66
where the claims information submitted by the hospital was incorrect, about seventy-five percent of the codes on the Medicare record for both principal diagnosis and procedure agreed with the firstlisted item on the claim form, and the overall percents remained fairly constant upon receding. For these cases the work of the Medicare coders was apparently reliable, even though the narratives submitted by the hospitals on which their codes were based were not. When the Medicare record did not agree with the first-listed diagnosis or related procedure on the claim form, about one-half of the cases reflected the appropriate application of a Medicare coding guideline requiring the coding of something other than the first-listed item. Nevertheless, an unexplained residual of discrepancies persisted. The earlier study did not contain claims information, and there was no parallel assessment. 11. If one examines the number of cases that should have been coded as specific diagnoses but were not, the influence of false positive and false negative diagnoses on admission rates and lengths of stay can be assessed. Proxy admission rates based on the IOM abstracts (including false negatives) suggest that reliance on Medicare data with both three- and four-digit comparisons will underestimate the number of admissions for most diagnoses except chronic ischemic heart disease and diabetes mellitus. Similar results are found when AUTOGRP is used to analyze entire DRGs. Consistent differences were not detected for diagnostic-specific lengths of stay, although variations were noted. IOM admission rates were higher in the earlier study as well--again, with the notable exception of chronic ischemic heart disease. The earlier study also suggested that diagnostic-specific lengths-ofstay based on abstract service data may over-estimate the average stay; consistent differences for length-of-stay were not detected in the Medicare study. 12. Some hospital characteristics were associated with the reliability of data. Hospital location within the Northeast census region was consistently associated with less reliable data. Billing office personnel with no medical record experience appeared to provide accurate diagnostic information, if medical record department personnel abstracted the medical record. The accuracy of the data was greater if coded, rather than narrative, information was sent to the billing offices. The submission of updated diagnostic and procedure information from the medical record department to the billing office and fiscal intermediary increased the accuracy of the data. The role of the physician in completing the medical record was also an important variable. The hospital characteristics considered in the earlier study differed somewhat from those included in the Medicare study, but there
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SUMMARY AND RECOMMENDATIONS
67
are some similarities. Variables associated with increased accuracy of the abstract service data included the thoroughness with which the medical record was reviewed before code numbers were assigned, frequency of communication between the hospital and abstract service, regular medical staff review and use of abstracted reports, and possibly, hospital location within a Standard Metropolitan Statistical Area. Because of the frequent difficulty encountered by the field team in determining which of several diagnoses should be regarded as principal, the care with which the physician completed the record was also considered as an influential structural variable, even though it was not measured directly. The cumulative effect of both studies elicits serious reservations about the adequacy of existing hospital utilization data on diagnoses and procedures. The Social Security Administration (and more recently Health Care Financing Administration) is to be commended for successfully initiating and continuing to administer and monitor a program of critical significance to the health and social welfare of millions of Americans. Private abstracting services have made a similar, if less monumental, contribution by providing information with the potential to assist in administering and monitoring the provision of hospital services by their subscribers. The uncritical processing of diagnostic-specific utilization information must be questioned, however, since increasingly important decisions regarding the adequacy of health care, the allocation of resources, and, perhaps, hospital reimbursement rates will be based on these data. To improve the quality of future data and to assist in appropriately using existing data, the following recommendations are offered. RECOMMENDATIONS 1. One must assume that diagnostic and procedural data on Medicare records contain errors and use them with caution. As in the previous study, the seriousness of the error depends on the purpose to which the data are applied. 2. Existing Medicare data are adequate for some aspects of general program monitoring, such as descriptions of general utilization patterns by age and sex or comparisons of overall lengths-of-stay among hospitals for entire institutions. However, diagnosticspecific discrepancies are of sufficient magnitude to preclude the use of such data for detailed research and evaluation or to measure diagnostic case mix as an indication of intensity of services that could then form the basis for determining reimbursement rates, Similarly, the usefulness of Medicare data on principal procedures should be seriously questioned. 3. The reliability of existing and future information may be improved by selective adjustments (assuming that the same kinds of errors
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SUMMARY AND RECOMMENDATIONS
4.
5.
6.
7.
68
persist). It was noted that the accuracy of diagnostic information increases when diagnostic codes are analyzed to three digits, rather than four, or when broader diagnostic classifications such as AUTOGRP are used. For determining basic utilization trends, and, in some instances, lengths of stay these more aggregate analyses are sufficient. The increased reliability resulting from less precise coding must be balanced against the loss of ability to detect the presence of complications (usually denoted by the fourth digit), which may significantly affect length of stay, however. In addition, adjustments for differences in patient mix may be approximated in the future if the presence or absence of additional diagnoses were accurately noted. If the Medicare data are to be used by Professional Standards Review Organizations for either routine review activities or program evaluation, many of the recommendations from the earlier study are also appropriate here. If quality assurance programs discontinue the current practice of reviewing all patients and physicians and move to a more targeted review of cases likely to be associated with poor quality, in many cases this will require improving the data base in order to detect changes in utilization patterns. As an example, it is quite likely that criteria for hospital admission and continued stay for a diabetic patient with mention of ketoacidosis and coma (code 250.0, using HCFA's modification of ICDA-8) would be quite different from criteria for a patient without mention of acidosis or coma due to diabetes unspecified (code 250.9). In order to evaluate the effect of review, it is essential that diagnostic information be accurately coded to the fourth digit. The likely reliability of data on diagnoses and procedures might be one consideration for selecting cases for targeted review. This should not be the only criterion, however, since it may result in eliminating from review those diagnoses or conditions for which both the quality of care and data are questionable. In such cases it would be important to intensify review efforts, but also to improve medical recording and diagnostic coding. Whenever Medicare data are used to measure changes in utilization patterns, the amount of error, including the influence of false negative and false positive diagnoses, must be assessed at each time that measurements are taken. This is essential in order to determine whether perceived changes are truly associated with altered utilization or, instead, with changes in the reliability of data. If Medicare data are used by PSROs, reliability must be assessed at the local level, as well as nationally. The Health Standards and Quality Bureau of HCFA (formerly the Bureau of Quality Assurance) should develop guidelines to assist in such assessments. Because much of the error in Medicare data is introduced at the hospital level, hospitals should be assisted in developing programs
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SUMMARY AND RECOMMENDATIONS
8.
9.
10.
11.
12.
69
to improve the information submitted on Medicare claim forms. This should include additional training for persons abstracting information from the medical record, routinization of hospital procedures so that the activities of billing personnel could be limited to information transfer, rather than interpretation of medical record data, and instructional programs for physicians in classifying diagnoses, determining primary diagnosis, and completing the medical record. If the current hospital practice of determining principal diagnosis and principal procedure by referring to the first-listed item on the face sheet of the medical record continues, several steps should be taken to assure that the first-listed item is appropriately selected. The medical record format should be revised, so that the sequence of conditions recorded are in order of priority, with the principal diagnosis listed first. Additional training of physicians and medical record personnel is required, as noted above. Alternatively, a more comprehensive review of the medical record might be required before selecting a primary diagnosis and/or procedure. Officials of the Health Care Financing Administration should initiate intensified efforts to assure the reliability of HCFA coding of information on diagnoses and procedures. In addition, serious consideration should be given to up-dating or replacing the Current Procedural Terminology classification for procedures. Data recording and reporting guidelines should continue to require that diagnoses are coded to at least four digits. This is the only way to assure that the resulting data base will have sufficient flexibility to meet a variety of data needs. For less precise requirements, the data can easily be analyzed to three digits only. The influence on reliability of coding to five digits, as anticipated by the 9th International Classification of Diseases-Clinical Modification, should be thoroughly evaluated. Additional research is needed to develop more appropriate classification schemes for describing patient status that could be incorporated into health information systems. These should include explicit consideration of signs and symptoms, co-morbidity, and functional status. For many patients it may be unrealistic to expect that a single primary diagnosis can or should be determined. Regardless of the type of activity initiated to improve the quality of abstracted information, assessments should be made of the resulting improvements in reliability and the associated costs. Increasing the quality of data is likely to be costly, and careful evaluation would help to insure that only the most effective methods are disseminated.
The deficiencies in the accuracy of currently available diagnostic and procedural utilization data cannot be denied. This fact in no way lessens
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SUMMARY AND RECOMMENDATIONS
70
the need for a comprehensive and reliable national health information system, however. Because of the likely expense of improving the reliability of data and maintaining a high level of accuracy, the Department of Health, Education, and Welfare should explore the feasibility of integrating the multiple data systems located thoughout its component agencies. The National Center for Health Statistics' Hospital Discharge Survey has recently incorporated UHDDS definitions for principal diagnosis and principal procedure. Therefore, the Department's activities might begin with an assessment of the reliability of that information and an exploration of the potential for expanding the survey to include information needed for management, reimbursement, program evaluation, and epidemiological purposes.
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APPENDIX A
71
Appendix A BIBLIOGRAPHY
Eighth Revision of International Classification of Diseases, Adpated for Use by the Social Security Administration, U.S. Department of Health Education, and Welfare, Social Security Administration, Pubn. No. 23-72, undated. Goodman, R. and Kish, L., “Controlled Selection - A Technique in Probability Sampling,” Journal of the American Statistical Association, 45 (September 1950): 350-72. Hess, Irene, Riedel, Donald C., and Fitzpatrick, Thomas B., Probability Sampling of Hospitals and Patients, 2nd ed. (Ann Arbor: Health Administration Press, 1975). Institute of Medicine, Reliability of Hospital Discharge Abstracts (Washington, D. C.: National Academy of Sciences, February 1977). Medicare Hospital Manual, U.S. Department of Health, Education, and Welfare, HIM Pubn. 10-(6-66), Reprint, August 1975. Mill, Donald, Fetter, Robert B., Riedel, Donald C., Averill, Richard, “AUTOGRP: An Interactive Computer System for the Analysis of Health Care Data,” Medical Care 14 (July 1976): 603-615. Uniform Hospital Abstract: Minimum Basic Data Set. A Report to the U.S. National Committee on Vital and Health Statistics, Series 4, Number 14, December 1972. U.S. Department of Commerce, Bureau of the Census, The Current Population Survey Reinterview Program: Some Notes and Discussion, Technical Paper No. 6 (Washington, D. C.: U.S. Government Printing Office, 1963). U.S. Department of Health, Education, and Welfare, Office of the Assistant Secretary for Health, Office of Professional Standards Review, Program Evaluation Plan: Professional Review Organizations, Martin A. Baum et al. (22 September 1975).
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APPENDIX A 72
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APPENDIX B
73
Appendix B SAMPLE LETTER TO HOSPITAL ADMINISTRATOR
Dear ____: The Institute of Medicine has contracted with the Social Security Administration to assess the reliability of a statistical file on short-term hospitalizations maintained centrally by SSA for research purposes. The study objective is to examine the usefulness of this information for program evaluation and, in particular, to determine whether it is sufficiently reliable to be used as baseline data for evaluating the impact of Professional Standards Review Organizations. The study is being conducted by an expert steering committee and project staff under the chairmanship of Robert J. Haggerty, M.D., Roger I. Lee Professor of Public Health at Harvard University. It has been approved by the governing board of the National Research Council. A brief summary of the goals and activities of the Institute is enclosed. The purpose of this letter is to request your assistance in conducting the study. More specifically, we are asking you to permit a member of our field team to abstract approximately 75 pre-selected medical records within your medical record department. For a portion of those records it will also be necessary to review the claim form submitted to the fiscal intermediary. The information of interest is limited to patient demographic characteristics, dates of admission and discharge, and diagnoses and procedures. Data on hospital charges will not be reviewed or recorded. The field work will be conducted by four highly skilled Registered Record Administrators (RRAs) with extensive research and administrative experience, who have been specially trained by Faye Brown, a former president of the American Medical Record Association. Because of their proven competence, they will be able to complete their work with no significant disruption of your regular routine, although some staff time will be required to retrieve the records in advance of their visit.
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APPENDIX B
74
Since the study does necessitate the review of medical records, we are very concerned about confidentiality and have taken special precautions in this regard. The unique patient number will only be used to access the relevant record and assist in compiling the abstracted information. In no instance will the name of the patient or physician be recorded. The resulting report will contain only statistical summaries, which will not permit the identification of patient, physician, or hospital. Similarly, this information will not be provided to the federal government. To indicate your willingness to participate in the study would you please sign the enclosed form and return it to us in the self-addressed envelope. We would be very grateful if you could indicate on the form the names of the directors of your medical record and billing departments, who will then be contacted by a member of our field team. If you have any questions about the study, our staff director, Linda Demlo, Ph.D., will be happy to respond. Please call her collect at 202-389-6148. Your hospital was selected through a very carefully designed sampling procedure, and your participation is essential if the results of the study are to be truly generalizable. The findings should have broad applicability for health services research and evaluation in general and may also help to determine the utility of information derived from the MADOC (Medicare Analysis of Days of Care) and more recent MEDPAR (Medicare Provider Analysis and Review) information systems for internal hospital management. We will be pleased to share with you the results of the completed study. We sincerely hope it will be possible for you to join us in this important undertaking. Sincerely yours, David A. Hamburg President Institute of Medicine Enclosures cc: Director, Medical Record Department Director, Billing Department
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APPENDIX C
75
Appendix C DIAGNOSTIC CODES INCLUDED AS COMPONENTS OF THE SAMPLE OF MEDICARE RECORDS
Diagnosis related groups
Sub-groups
ICDA-8 code numbers
Specific diagnosis
Chronic ischemic heart disease
4120 4129
Residual diagnosis
Subacute ischemic heart disease (satellite)
4110 4119
Angina Pectoris
4130 4139
Asymptomatic ischemic heart disease
4140 4149
Subarachnoid hemorrhage
4300 4309
Cerebral hemorrhage
4310 4319
Occlusion of precerebral arteries
4320 4329
Cerebral thrombosis
4330 4339
Cerebral embolism
4340 4349
Transient cerebral ischemia
4350 4359
1. Ischemic heart disease except AMI
2. Cerebrovascular diseases Specific diagnosis
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APPENDIX C
Diagnosis related groups
Residual diagnosis
76
Sub-groups ICDA-8 code numbers
Acute but ill-defined cerebrovascular disease 4360 4369
Generalized ischemic cerebrovascular disease 4370 4379
Other and ill-defined cerebrovascular disease 4380 4389
None
3. Fractures
Specific diagnosis Fracture, neck of femur 8200 8201 8202 8203 8204 8205 8209
Residual diagnosis Fracture of Skull Spine and Trunk 8000 8001 8009 8011 8019 8020 8021 8022 8023 8024 8025 8029 8030 8031 8039 8040 8041 8049
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APPENDIX C
Diagnosis related groups
77
Sub-groups
Fracture of upper limb
ICDA-8 code numbers
8050 8051 8052 8053 8054 8055 8056 8057 8059 8060 8061 8062 8063 8064 8065 8066 8067 8069 8070 8071 8072 8073 8074 8075 8079 8080 8081 8089 8090 8091 8099
8100 8101 8109 8110 8111 8119 8120 8121 8122 8123
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APPENDIX C
Diagnosis related groups
78
Sub-groups
Fracture of lower limb (including fracture of other and unspecified parts of femur but excluding fracture of neck of femur
ICDA-8 code numbers
8124 8125 8129 8130 8131 8132 8133 8134 8135 8139 8140 8141 8149 8150 8151 8159 8160 8161 8169 8170 8171 8179 8180 8187 8181 8190 8191 8199
8210 8211 8212 8213 8219 8220 8221 8229 8230 8231 8232 8233 8239
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APPENDIX C
Diagnosis related groups
79
Sub-groups ICDA-8 code numbers
8240 8241 8249 8250 8251 8259 8260 8261 8269 8270 8271 8279 8280 8281 8289 8290 8291 8299
4. Diseases of the eye
Specific diagnosis Cataract 3740 3741 3748 3749
Residual diagnosis Inflammatory diseases of the eye 360 361 362 3630 3639 364 365 366 367 368 3690 3699
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APPENDIX C
Diagnosis related groups
Specific diagnosis
80
Sub-groups ICDA-8 code numbers
Other diseases and conditions of the eye; excludes cataract and blindness 3700 3701 3702 3703 3709 3710 3719 372 3730 3731 3732 3739 3750 3751 3752 3759 376 3770 3771 3772 3773 3774 3775 3776 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789
5. Acute myocardial infarction
Acute myocardial infarction 4100 4101 4109
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APPENDIX C
Diagnosis related groups
Specific diagnosis
Residual diagnosis
81
Sub-groups
Residual diagnosis
ICDA-8 code numbers None
6. Hernia of abdominal cavity
Specific diagnosis Inquinal hernia without mention of obstruction 550
Residual diagnosis Other hernia of abdominal cavity without mention of obstruction 5510 5511 5512 5513 5518 5519
Inguinal hernia with obstruction (satellite) 552
Other hernia of abdominal cavity with mention of obstruction 5530 5531 5532 5533 5538 5539
7. Diabetes mellitus
Diabetes mellitus 2500 2501 2502 2503 2508 2509
None
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APPENDIX C
Diagnosis related groups
82
Sub-groups
ICDA-8 codes numbers
Specific diagnosis
Hyperplasia of the prostate
600
Residual diagnosis
Prostatitis and other diseases of the prostate
601 602
Specific diagnosis
Bronchopneumonia organism not specified and pneumonia organism and type not specified
485 486
Residual diagnosis
Pneumonia, type and organism specified
480 481 4820 4821 4822 4823 4829 483 484
8. Diseases of the prostate
9. Pneumonia
10. Diseases of the gall bladder and bile duct Specific diagnosis
Cholelithiasis and cholecystitis
5740 5741 5749 575
Residual diagnosis
Other diseases of the gall bladder and bile duct
5760 5761 5769
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APPENDIX C
Diagnosis related groups
83
Sub-groups
ICDA-8 code numbers
11. Miscellaneous diseases of the intestine and peritoneum .
Specific diagnosis
Intestinal obstruction without mention of hernia
5600 5601 5602 5603 5604 5609
Residual diagnosis
Peritonitis, peritoneal adhesions, and other diseases of the intestine and peritoneum
5670 5679 568 5690 5691 5692 5693 5694 5699
Specific diagnosis
Congestive heart failure and left vertricular failure
4270 4271
Residual diagnosis
Acute heart failure, undefined
7824
12. Heart failure
13. Enteritis, diverticula, and functional disorders of intestine Specific diagnosis
Diverticulosis of intestine
5620 5621
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APPENDIX C
84
Diagnosis related groups
Sub-groups
ICDA-8 code numbers
Residual diagnosis
Noninfectious gastroenteritis and colitis, except ulcerative
561
Chronic enteritis and ulcerative colitis
5630 5631 5639
Functional disorders of intestine
5640 5641 5649
Specific diagnosis
Bronchitis
466 490 491
Residual diagnosis
None
14. Bronchitis
15. Malignant neoplasm of the respiratory system Specific diagnosis
Malignant neoplasm of bronchus and lung
1621
Residual diagnosis
Primary malignant neoplasm of respiratory system except of bronchus and lung
1600 1601 1602 1608 1609 1610 1618 1619 1620 1630 1631 1639
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APPENDIX D 85
Appendix D
IOM RE-ABSTRACTING FORM
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APPENDIX D 86
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APPENDIX D
87
Appendix D GENERAL INSTRUCTIONS FOR FIELD TEAM
Prior to the visit of the field team member, the medical record department and the billing office of each hospital will be sent a roster of patient names, dates of birth, and Medicare claims numbers (hospital insurance claims number) with associated admission and discharge dates for the hospital episodes of interest. This list will be used by the billing offices and medical record departments to locate the claims forms and medical records in preparation for the research visit. A space will be provided on the roster for medical record personnel to enter the medical record number which corresponds with the Medicare claims number. A copy of this roster of patient names and identifying information will also be provided to the field team for each hospital visited. Upon entering the the record department, the field team member should compare her master list with the records which have been previously located by department personnel in order to ascertain whether the required records are available. At this time, any missing record should be requested from the supervisor of the record department. If the record is not found, do not replace it. Instead, return the blank Institute of Medicine re-abstract form corresponding to the missing record, indicating that the record was not available. The master list will be attached to a sealed envelope containing the information provided by the Social Security Administration (SSA). The envelope should not be opened until all records have been abstracted. Before beginning the actual abstracting process, the field team member should discuss with the department supervisor appropriate items in the “Medicare Processing Checklist.” This will acquaint the field team member with coding and billing practaces in each hospital which bear on the data compiled from that hospital by the Social Security Administration. In particular it will be important to ascertain how and in what form diagnostic and procedures information is provided to the billing office for entry onto a Medicare claims form. Particular attention should be given to how the hospital defined “primary diagnosis” or “surgical procedure” for the purpose of completing the Medicare claims form during 1974. The field team member should also review the format of the medical record with the department supervisor to detect any unusual practices which are unique to that hospital.
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For each case to be abstracted, the field team member will be given an Institute of Medicine re-abstracting form pre-coded with the patient's hospital insurance claim number, date of birth, and date of discharge for the hospital episode under study. A coder I.D., SSA provider number (hospital I.D.), and sequential number will also be preprinted on the form. No names will appear on the form. Once the Institute of Medicine forms have been matched with the correct charts, the appropriate medical record numbers must be entered on the Institute of Medicine's re-abstracting form and later on the Social Security Administration's abstract when the reconciliation process is carried out. In completing the form, the field team should review the face sheet of the medical record, the discharge summary, operative report, pathology report, X-ray report (if appropriate), consultation notes, laboratory reports, EKG (if appropriate), and diagnostic reports from such departments as physical medicine, physical rehabilitation and nuclear medicine. The form will be used throughout the five steps of the re-abstracting process, as follows: 1. The Institute of Medicine re-abstracting form is used to abstract information from the medical record for the specified discharge date. Column 1 should be completed for all items and all records to be studied at a particular hospital. All records must be re-abstracted before proceeding to the next step. The items to be re-abstracted and definitions for each are given in the “Specific Instructions” below. The field team member may make changes in the information recorded in column 1 on the IOM re-abstract form during the initial re-abstracting process. However, after column 1 of the IOM reabstract is completed and the comparison and reconciliation with the SSA abstract have begun no changes may be made in the re-abstracted information in the column 1. 2. After all records in a given hospital have been re-abstracted, the field team member should open the appropriate sealed envelope, which will contain copies of the abstracts provided by the Social Security Administration. Enter the medical record number onto each Social Security Administration abstract. Compare information on each newly completed Institute of Medicine re-abstract with the information from the appropriate Social Security Administration abstract. Indicate whether or not the two abstracts agree by checking the appropriate “yes-no” response in column 2. If the items do not agree, record the data provided by the Social Security Administration in column 3, which is labeled “If no, enter information from abstract.” After all abstracts have been compared, proceed to the next step for cases in which differences are found.
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3. In each case, for those items in which there is a difference between the information re-abstracted and that provided on the Social Security Administration abstract, search the medical record to determine which abstract is correct. The correct abstract should be indicated by Checking one of the four alternatives in column 4: SSA abstract, reabstract either, or neither. “Re-abstract” refers specifically to the Institute of Medicine re-abstract form. The “either” option should be used only if, in the opinion of the field team, there is no obviously “correct” response and either abstract is equally acceptable. “Neither” means that both abstracts are in error. 4. After the correct abstract has been identified, refer to the item definitions to determine the reasons for discrepancy (see “Specific Instructions”). In the event that both abstracts are in error (i.e., “neither” was checked in the fourth column) the reason for discrepancy should refer to the original abstract provided by the Social Security Administration. 5. In column 2, which is labeled “Do abstract and re-abstract agree?” (a) If yes has been checked, information will not be recorded in columns 3, 4, 5, and 6, i.e., leave the rest of that row BLANK. (b) If no has been checked, information must be entered appropriately in columns 3, 4, 5, and 6. The recording of information in column 6 is discussed in item 7 below. 6. After all IOM re-abstracts have been compared to the Social Security Administration abstracts, and all reconciliation steps have been completed, including column 5, the field team member should review all Institute of Medicine re-abstracts and separate them into two categories: (a) Those in which no discrepancy on any item was found between the Institute of Medicine re-abstract and the Social Security Administration abstract (i.e., in column 2, “yes” was checked for every item) and those abstracts where there was a discrepancy (i.e., in column 2, “No” was checked) but the correct data source checked in column 3 was the Social Security Administration abstract (i.e., the field team member found herself in error). (b) Those in which one or more discrepancies were noted and where the correct data source was determined as “re-abstract”, “either”, or “neither”.
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7.
The field team member should then go to the billing office. After obtaining the remaining information about the manner in which that particular facility completed its Medicare claims forms in 1974, copies of Medicare claims forms (form 1453) should be obtained for all the patient episodes under study. From the claims provided, the field team should select out copies for all cases which fall into the second category mentioned above (i.e. 6[b]). For those items where a discrepancy was found, the respective data from the Medicare form should be transferred onto the Institute of Medicine re-abstract in column 6. For diagnoses and procedures, the narrative information from the Medicare bill should be transferred verbatim onto the Institute of Medicine re-abstract form. The first listed diagnosis should then be coded in column 6 for diagnosis. Likewise, the first listed procedure should be coded in column 6. 8. The following instructions refer to procedures for handling missing data regardless of where the omission occurs (in the medical record, Social Security Administration abstract, or copy of claims form). (a) Admit and discharge date: if data are missing, enter 9's in the appropriate boxes; be sure to fill each box. (b) Sex: check box labeled “not recorded” if data are missing. (c) Principal Diagnosis: by necessity, there will be no allowance for missing data for principal diagnosis on either the SSA abstract or the IOM re-abstract. However, 9's may be entered in column 6 for missing principal diagnosis on the Medicare claims form only. This would mean that the space for listing principal diagnosis on the claims form was completely blank. (d) Admitting Diagnosis: If an admitting diagnosis can not be identified after following the specific instructions given later, enter code 999.9 in the appropriate boxes. (e) Procedures: X's should be entered if the Institute of Medicine field team member finds that no procedures are significant enough to warrant coding as “Principal Procedure” in column 1 at the bottom of Section IX. These X's will not indicate that data are “missing”, but rather that there were no procedures worthy of coding. Zero's should be entered if the Institute of Medicine field team member determines a principal procedure but finds that there is no code in the CPT manual. For example, physical therapy in some cases may be considered as a principal procedure but does not have an assigned CPT code; in this case the field team would then enter 0000 in the boxes for principal procedure. The abstracts provided by the Social Security Administration note “no procedures” by “0000”. When necessary, these four zero's should be entered in column 3. (Of course,
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column 3 is only filled out if the abstracts do not agree and “no” has been checked in column 2; if “yes” has been checked, the rest of the row is to be left blank.) Note: please refer to the Specific Instructions for additional guidance on completing this item.
9. If a Medicare claim form #1453 is needed but is not available, write a note in the right hand margin of the re-abstracting form to indicate such is the case.
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Appendix D SPECIFIC INSTRUCTIONS FOR IOM RE-ABSTRACTING FORM
In general, the Institute of Medicine field team should abstract medical records using the definitions of the Uniform Hospital Discharge Data Set (UHDDS); these definitions are attached, as well as Medicare definitions for relevant items. The field team must be thoroughly familiar with both sets of definitions before beginning the field work. Instances in which the objectives of this study require deviation from the UHDDS definitions are discussed below: I.
Identifying Information: with the exception of medical record numbers, all of the following identifying information will be pre-coded on the Institute of Medicine re-abstracting form: •
• • • •
The Hospital Insurance Claim Number is the number assigned by the Social Security Administration to a particular beneficiary. It is used to assist in locating the appropriate medical record. Only the number will be used, and in no case will the patient's name be recorded on the Institute of Medicine re-abstract. The SSA Provider Number is a six digit number assigned by the Social Security Administration to identify the hospital. The Coder Identification Number is a number assigned to each member of the Institute of Medicine field team. The Sequential Number is assigned to each hospital episode under study by the Institute of Medicine for record keeping purposes. The Medical Record Number is the number assigned to the patient by the hospital and should be entered onto both the Social Security Administration abstract and the Institute of Medicine reabstract by the field team as discussed in the General Instructions.
II. Instructions for Completing Column 1 on the Institute of Medicine Re-abstracting form:
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• Admission Date and Discharge Date definitions are the same as those of UHDDS except that the hour of admission or discharge will not be recorded. For example, October 1, 1974 should be coded:
• •
•
• • • • • •
Note that the boxes for “day” have been darkened on the re-abstracting form. This is to emphasize that the appropriate recording sequency is month, day, year. Sex is to be coded as male or female. The response of not recorded is reserved for missing data. Admitting Diagnosis. The field team member should search the face sheet, emergency room, history and physical reports, and admission notes and write in the diagnoses which appear in these parts of the record. The part of the record from which each diagnosis was abstracted should be indicated as follows: ER = Emergency Room Reports HR = Reports of History and Physical A = Admission Notes After reviewing these diagnoses obtained only from those portions of the record specified, the field team member should determine an admitting diagnosis and place an “A” next to the appropriate diagnosis. A refined diagnosis should not be be abstracted unless it is absolutely clear that this more precise diagnosis was known at admission or soon thereafter. The ICDA-8 code (adapted by the Social Security Administration) for the admitting diagnosis should be inserted in the boxes in the lower portion of Section IV. Principal Diagnosis. After determining an admitting diagnosis, the field team member should continue searching the medical record, writing additional diagnoses in the space provided on the Institute of Medicine re-abstracting form. The part of the record from which each diagnosis was abstracted should be indicated as follows: F = Face Sheet D = Discharge Summary C = Consultation O = Operative Report P = Pathology Report R = Reports, such as EKG, EEG, X-ray, or other diagnostic laboratory reports
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• When the entire medical record has been searched and the important diagnoses entered on the Institute of Medicine re-abstracting form, the field team member should then review the diagnoses listed and determine a principal diagnosis using the UHDDS definition (i.e., that condition established after study to be chiefly responsible for occasioning the admission of the patient to the hosital for care). A “P” should be placed in the second column next to the appropriate diagnosis. The ICDA-8 code (adapted by the Social Security Administration) should be inserted in the appropriate boxes in the lower portion of Section V and also in the first column of Section VII. • Comparison of Admitting and Principal Diagnosis. After coding an admitting and principal diagnosis, the field team member should compare them and check in the appropriate place in Section VI whether they agree. If they do not agree, then she will check the reason which could best account for the difference. These reasons include: completeness, coding refinement, and “other” which are explained later. • Additional Diagnoses. If more than one diagnosis was abstracted in Section IV or V, and in the field team's judgment could be considered as an “additional diagnosis”, check the appropriate code in column 1 of Section VIII to indicate the presence or absence of additional diagnoses. • Guidelines for determining whether to count a diagnosis as “additional include: •
a Any diagnosis clearly stated as a diagnosis by the physician on face sheet or discharge summary. ) Do not include “rule out” unless it is actually a “viable”, “probable”, or “possible”, still active and of some significance, not just a confirmed diagnosis. Do not count “history of” etc., that are sometimes recorded but are not clinically significant for this stay. • b Any diagnosis clearly present when reviewing a chart including surgical diagnosis or consultant's ) diagnosis when definitive. • c Pathology diagnosis when it fits that mentioned immediately above; do not count if they are) not clinically significant or only of histologic interest, e.g., chronic cervicitis, when not a major problem. • d X-ray diagnosis when clearly substantiated and of some significance: a fracture of a bone with ) surgery or treatment would clearly be included, whereas a slight degree of osteoarthritis for which a patient was not treated and noted only
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as an observation would not. Omit if only of radiologic interest or indicated as “consistent with” but not further substantiated by other internal evidence. • e Diagnoses inferred from physical findings or laboratory findings should be included only if obviously ) of clinical importance or otherwise substantiated in the record. • There are three reasons for discrepancy for this item: completeness, hospital definition, and importance, as discussed later. • Procedures • All procedures are to be written on the re-abstracting form. In addition, the portion of the record from which each procedure was abstracted should be indicated in column 1 according to the symbols used in abstracting diagnoses. In column 2, place a “P” next to the procedure which is the principal procedure according to the UHDDS definition. The field team will have to exercise some discretion in assigning a principal procedure when only a minor one--such as “manual arts therapy”--has been noted in the chart. Do be overzealous in coding, but on the other hand procedures of clear significance should definitely be recorded. • Enter the appropriate code for the principal procedure in the boxes provided, in the first column of the lower portion of Section IX. The Current Procedural Terminology (CPT) nomenclature should be used. When the field team determines no procedures significant enough to warrant coding, X's should be entered in all the appropriate boxes; if there is no CPT code for a principal procedure, identified by the field team, 0000 should be entered. The General Instructions provide further information for coding missing data. • Status of Medical Record • In Section X the field team member should check the status which best describes the physical form and completion status of the medcal records which were abstracted for the study by indicating if they used a microfilm of a complete record, a microfilm of an incomplete record or an actual completed medical record. • Status of Claims Form • In section XI the field team member should check the status which bests describes the physical form of the Medicare claims form #1453 by indicating if the actual claims form or microfilm was used.
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III Reasons for Discrepancy • Reasons for Discrepancies in Admission Date, Discharge date and Sex • Two reasons are to be used to explain discrepancies between the Institute of Medicine re-abstract and the Social Security Administration abstract for admission and discharge dates and sex: a Clerical: Discrepancy attributable to human error, mistakes of a particular clerk, errors in transcribing number, etc. (Example: obvious transposing of numbers on admission or ) discharge date.) • b Completeness: Incomplete or inaccurate information on the abstract or re-abstract due to an incomplete review of the chart (Example: item missing from the admitting sheet, but ) clearly stated in the discharge summary.)
•
• Reasons for Discrepancy in Comparing Admitting and Principal Diagnosis a Completeness: Discrepancy due an incomplete review of the of the emergency room report, )the history and physical notes, or the admission notes. (Example: the field team may assign an admitting diagnosis of diabetes, having overlooked the admission notes or ER reports which indicated that an open wound infection would have been the more appropriate admitting diagnosis.) • b Coding Refinement: Discrepancy due to a difference in level of refinement between the codes ) for admitting and principal diagnosis. For example, the admitting diagnosis may be 486.0 (pneumonia) while the principal (“final”) diagnosis would be the more refined code 481.0 (pneumococcal pneumonia). This is not really an error, but more a reason which accounts for the fact that admitting diagnoses are often by necessity more general than discharge diagnoses. • c Investigation: Discrepancy resulting from an admitting diagnosis being assigned on a preliminary finding or symptom and upon further medical investigation, a more precise - and ) quite different - diagnosis was determined. For example, a patient may be admitted with headache (ICDA code 791) and after further testing and investigation, it turns out to be due to hypoglycaemia (ICDA code 251). •
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•
d Other: Discrepancy which cannot be explained by any of the above. The field team member) should make a brief note to explain the problem on the back of the re-abstracting form.
• Reasons for Discrepancy for Diagnosis and Procedure • There are two general categories into which reasons for discrepancy for diagnosis and procedure are grouped: ordering and coding. •
a An ordering discrepancy will be used to reflect an inconsistency between the SSA abstract) and the IOM re-abstract which stems from uncertainty over whether a diagnosis or procedure should be regarded as the “principal” diagnosis or procedure, in accord with UHDDS definitions. The possibility of an ordering discrepancy should be considered and eliminated before the possibility of a coding discrepancy is entertained. Definitions of specific types of ordering discrepancies follows:
1 Ordering--Definition: Discrepancy in ordering of principal diagnosis and/or procedure because. of a difference between the UHDDS definition and that required by the Medicare claims form. (Example: a patient is admitted for an open fracture reduction and, while on the operating table suffers an acute MI which keeps him in the hospital three months. Using the UHDDS definition, fracture reduction would be chosen as the principal diagnosis because, as the definition requires, fracture is the diagnosis explaining cause of admission. If the definition in the “Medicare Hospital Manual” for principal diagnosis is used, however, the code might be AMI. Medicare states: “the primary diagnosis is the diagnosis or the illness or condition which was the primary reason for the patient's hospitalization”--(in other words, the most serious diagnosis.) A fuller listing of the Medicare definitions for diagnoses and procedures appears at the end of these instructions with the UHDDS definitions. In order to use this reason, of course, the field team must first ascertain that the hospital in question carefully and consistently used the Medicare definitions in completing the claims forms in 1974. • 2 Ordering--Hospital List: Discrepancy in ordering of principal diagnosis or procedure which stems for a routine hospital practice (in 1974) of choosing the first listed diagnosis or . procedure on the face sheet as principal. For example, if this practice was followed, by a hospital, chronic ischemic heart disease may be chosen as a principal diagnosis because it was the first listed on the face sheet while congestive heart failure would have been the principal diagnosis if UHDDS definitions were used. •
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•
•
• • •
•
3 Ordering--Completeness: Discrepancy in choice of principal diagnosis and/or procedure . caused by assigning a code based on an incomplete review of the medical record. For example, the principal diagnoses selected by IOM and SSA refer to different diseases, each of which the patient had during the hospital episode under question. However, if the chart had been searched more thoroughly, it would have been clear that one, rather than the other, was the proper principal diagnosis according to UHDDS. More specifically, SSA coded hydrocephalus as principal diagnosis and IOM, a decubitus ulcer. The patient had both problems, but a careful review of the chart would clearly indicate that decubitus ulcer was the true principal diagnosis. 4 Ordering--Judgment: Discrepancy in selection of principal diagnosis or procedure which represents an honest difference of opinion in interpreting the medical record, primarily in . determining which of several diagnoses is principal. One example of this might be a record in which a patient had diabetes and glaucoma and there was sufficient documentation in the record to decide that either diagnois would conform to the UHDDS definition for principal diagnosis. Similarly, a record may indicate carcinoma of several sites and may not be well documented so as to clearly determine a principal diagnosis using the UHDDS definition. 5 Ordering--Other: A discrepancy in ordering of principal diagnosis and/or procedure other than . the above. If this reason is used, please write a note to briefly describe the discrepancy. 6 Ordering--Dependent: This reason applies only to the coding of procedure. This reason will be used to reflect a discrepancy which results from a prior discrepancy in a . related item. Usually, this situation will occur only when an earlier discrepancy in selection of the principal diagnosis results in a dependent discrepancy in selecting the principal procedure. b A coding discrepancy applies only to the actual coding of the principal diagnosis and the principal procedure after the possibility of an ordering discrepancy has been eliminated. In ) other words, there is general agreement on what the principal diagnosis or procedure should be, but the codes differ for one of the following reasons:
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•
•
•
•
•
•
1 Coding--Clerical: Discrepancy caused by transposing diagnostic code numbers or using non-existant . codes, i.e., there was apparent agreement between the Institute of Medicine and the Social Security Administration on which diagnosis or procedure is primary, but the difference lies in a clerical mistake in inserting the code numbers. 2 Coding--completeness: Discrepancy which may be caused by selecting a diagnostic or procedure . code based on an incomplete review of the medical record, i.e. coding a diagnosis with a .9 fourth digit (indicating “not otherwise specified”) when a more careful review of the chart would have yielded a more specific fourth digit code. 3 Coding--procedural: Discrepancy caused by routine and systematic misuse or misunderstanding of the coding system, resulting in a discrepancy. (Examples: reliance on index . without reference to tabular listings, failure to heed inclusion and/or exclusion advice from tabular listings). 4 Coding--Importance. (This reason applies only to the coding of procedures): Discrepancy caused by differences of opinion over how significant a procedure must be to be . coded, i.e., SSA has coded a diagnostic procedure as the principal procedure while the IOM reabstract lists no principal procedure. Because it may be unclear whether a given diagnostic procedure “qualifies” as a principal procedure, this reason would be selected to explain the discrepancy. 5 Coding--Judgment: Discrepancy caused by absence of complete word-for-word correspondence between the recording of the diagnosis or procedure in the record and the . wording in the coding manuals. That is, an honest difference of opinion over the correct code when it is not clear from the coding manual what the numbers should be. (Example: diagnosis listed as recurrent and it is unclear whether “acute” or “chronic” is actually the more appropriate qualifier for coding purposes and these are the only two options available.) 6 Coding--Other:---Discrepancy in coding not due to any of the above. In particular this option would be used to indicate a discrepancy resulting from SSA's use of 0000 or 6040 to code . procedure when the IOM has used a valid code, XXXX or 0000. The use of these codes is explained on page 11.
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• Reasons for Discrepancy on Presence of Additional Diagnoses 1 Completeness: Discrepancies in interpreting the presence or absence of additional diagnoses . which could be the result of an incomplete review of the medical record (to be used only in hospitals which routinely note additional diagnoses on Medicare Claims form, as revealed by the checklist). • 2 Hospital Definition: Discrepancies which result from a hospital policy. For example, some hospitals may routinely enter only one principal diagnosis on the Medicare Claims form . and therefore, by definition, no additional diagnoses would appear on the SSA abstract. • 3 Importance: Discrepancy which may be due to the guidelines specified above for the field team. . For example, osteoarthritis may be listed as an additional diagnosis in the medical record and the Medicare Claims form might indicate the presence of an additional diagnosis. However, if it was noted in the record only as an observation, then, using the IOM guidelines, it would not be counted as an additional diagnosis.
•
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UHDDS DEFINITIONS The Institute of Medicine's field team member will use the following UHDDS definitions for diagnoses and procedures during the re-abstracting and reconciliation process. The definition for “other diagnoses” will be used in conjunction with the guidelines listed in the Specific Instructions for determining whether to count a diagnosis as additional. Specific UHDDS Definitions follow:1/ — Principal Diagnosis: — “The condition established after study to be chiefly responsible for occasioning the admission of the patient to the hospital for care.” — Other Diagnoses: — “All conditions that coexist at the time of admission, or develop subsequently, which affect the treatment received and/or the length of stay. Diagnoses that relate to an earlier episode which have no bearing on this hospital stay are to be excluded.” — Procedures: — “All procedures performed in operating rooms are to be reported... In addition to these procedures, all other significant procedures are to be recorded. A significant procedure is one which carries an operative or anesthetic risk or requires highly trained personnel or special facilities or equipment. Some examples of such procedures are cardiocatheterization, angiography, endoscopy, and supervoltage radiation therapy. — When more than one procedure is recorded the principal procedure is to be designated. In determining which of several procedures is the principal, the following criteria apply: (1)
The principal procedure is one which was performed for definitive treatment rather than one performed for diagnostic or exploratory purposes, or was necessary to take care of a complication. (2) The principal procedure is that procedure most related to the principal diagnosis.”
1 Uniform Hospital Abstract: Minimum Basic Data Set. National Center for Health Statistics, A Report of the United States National Committee on Vital and Health Statistics, Series 4, Number 14, December 1972.
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APPENDIX D
103
MEDICARE DEFINITIONS The Institute of Medicine's field team members should familiarize themselves with definitions for diagnoses and procedures given in the “Medicare Hospital Manual.” These definitions listed below, must be considered during the reconciliation process for diagnoses and procedures, particularly in assessing whether a discrepancy in the ordering of a principal diagnosis or procedure may be due to a difference between the UHDDS definition and that required by Medicare. If such is the case, the correct reason for discrepancy to be chosen should be “ordering definition” as explained on page six of the Specific Instructions. Medicare definitions for diagnosis and procedure follow:2/ • Primary Diagnosis “The primary diagnosis... is defined as the diagnosis of the illness or condition which was the primary reason for the patient's hospitalization.” • Surgical Procedures “Surgical procedures should be specified using a recognized nomenclature... For the purpose of completing Medicare claims form 1453, surgery includes incision, excision, amputation, introduction, and escopy, repair, destruction, suture and manipulations... List first those procedures related to the primary diagnosis.”
MEDICARE CODING OF PROCEDURES The Social Security Administration has two unique codes to indicate problems in coding procedures. They include: • 0000 - This four zero code is used by SSA to note that 1) the space for writing in a procedure on the claims form was blank; or 2) a procedure was recorded for which there is no CPT code; or 3) an ineligible term was entered in the space (such as a date or sex designation). • 6040 - this code is used to note that an illegible procedure was listed on the claims 1453 and that therefore no procedure code could be selected.
2 Medicare Hospital Manual, U.S. Department of Health, Education, and Welfare, HIM Pubn. 10- (6-66), Reprint, August 1975.
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APPENDIX D
104
Specific guidelines for resolving problems generated by the 0000 and 6040 codes include: 1. When a field team member locates a procedure in the chart and wishes to code it, but there is no CPT code for it, enter 0000 in columnm 1. When comparing this with the SSA abstract which also has 0000 listed in the procedures space, note in column 2 that the abstracts “agree.” 2. When the field team member reviews a chart and decides that there is no procedure significant enough to be coded, write XXXX in column 1. As discussed above, SSA enters 0000 when the procedure box on the claims form is empty. Therefore, if the IOM re-abstract has XXXX, and the SSA abstract has 0000, mark in column 2 that the abstract agree. The characters are different in the example given here (X's and 0's), but they refer to the same fact, i.e., no procedure to be coded - and thus the abstracts do agree. 3. If the field team member codes a procedure and the SSA abstract says 6040 (illegible code), note that the abstracts disagree, the IOM re-abstract is correct (column 4) and use CODING OTHER as the reason for discrepancy. Similarly if the field team member has coded XXXX or 0000 for principal procedure and SSA has 6040 the abstracts do not agree, the IOM abstract is correct, and “coding other” is the reason for discrepancy.
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APPENDIX E
105
Appendix E INFORMAL CHECKLIST
HOSPITAL NAME
NUMBER
INFORMAL CHECKLIST OF ITEMS TO DESCRIBE THE FLOW OF CLAIMS INFORMATION FROM THE MEDICAL RECORD DEPARTMENT TO THE BILLING DEPARTMENT TO THE FISCAL INTERMEDIARY (all responses should refer to calendar year 1974) 1. 2. 3. 4. 5.
6. 7. 8. 9. 10.
On the average how many days after discharge was information on diagnosis transmitted to the billing office for insertion on the Medicare claim form during 1974? ___ How many days after discharge was the medical record completed and a final diagnosis determined? ___ When more definitive diagnostic information became available which differed from that previously submitted to the billing office, was that information forwarded to the billing office? ___ Did the billing office forward up-dated diagnostic information to the fiscal intermediary? ___ In what physical form was information on diagnoses and procedures transmitted to the billing office? Admitting sheet ___ Xerox of face sheet ___ Entire record ___ Discharge list: hand-written ___ computerized ___ typed ___ Other If portions of the medical record were transmitted, what was the training of the person in the billing office who determined the diagnoses that should be entered on the Medicare claim form? ___ If a discharge list or some other summary of abstracted information was forwarded to the billing office, by whom was the diagnostic information obtained and from what source? ___ Did the diagnostic information forwarded to the billing office contain codes, narratives, or both? ___ If the information was coded, how was it translated back to a narrative form for submission to fiscal intermediary? ___ What definition of principal diagnosis was used by the hospital in completing Medicare claims forms during 1974? ___
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APPENDIX E
What definition of principal procedure was used by the hospital in completing Medicare claims forms? (Please note whether physical therapy or other non-surgical procedures were included on the Medicare claims form.) ___ 12. Did the billing office provide the intermediary with more than one diagnosis? _________________ 13. Did the billing office provide the intermediary with more than one procedure? _________________ 14. Total number of Medicare discharges from this hospital in 1974. _______ 11.
106
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APPENDIX F
107
APPENDIX F RELIABILITY OF FIELD WORK
Despite the care with which the field team was selected and trained and the thorough editing of data, an independent assessment of the reliability of the field team's work was performed. A consultant who had assisted in training the field team independently re-abstracted a subsample of records and compared her results to those initially compiled by the field team. This Appendix presents the methods and findings from that activity. Methods The original seventy-one hospitals were first divided into four groups, depending on which field team member had visited each facility. The hospitals in each group were then categorized according to whether they were visited during the first or last half of the abstractor's field work. From each of the resulting eight groups, one hospital was chosen at random for inclusion in the study. Thus, all seventy-one hospitals had a posssibility of selection. All eight hospitals agreed to a second site visit by the consultant. In each hospital one-half the records from the original sample (approximately thirty-six records) were selected for review. To accomplish this, the records were equally divided between those in which no discrepancy had been found between the IOM abstract and the Medicare record and those in which one or more discrepancies had been found. Within each group, the assessment records were then chosen at random. Two hundred eightyone records were available for analysis. Each new abstract was assigned a weight to reflect the probability of selection of both abstract and hospital in the assessment analysis. The results can be generalized to the universe of all IOM abstracts. The forms and instructions used in the assessment are the same as those used by the field team (see Appendix D). However, the consultant was not asked to consult any Medicare claim forms because of time constraints. The consultant did not know which member of the field team had done the initial abstracting or whether any discrepancies had initially been detected. After completing the independent abstracting, the consultant reviewed the Medicare record (also used by the field team)
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APPENDIX F
108
and reconciled discrepancies according to the process used by the field team. The goals of this process were to check whether the IOM abstractor made a reasonable judgment about the accuracy of the Medicare data and whether the field team's assessment of the reasons for discrepancy were plausible. The independent re-abstracting does not answer definitively the question of the reliability of the field team's work. The re-abstracting sample was very small. The perspectives of the consultant and field team may have been somewhat dissimilar. An alternative assessment method would have been to have the field team members check on one another, but this option was precluded by time and budget constraints. Nevertheless in a situation where the concept of data accuracy is tenuous at best (for some abstracted items, there is no clear “right answer”) the independent assessment was intended to help in determining the soundness of the basic study data. Analysis The analysis involved a comparison of three sources of data: that generated by HCFA, the IOM abstractors, and the consultant. Special attention was given to determining whether the field team and consultant initially abstracted the medical record in a similar manner and, where there were differences, whether they agree on the correct source of data and the reasons for discrepancies. Table 1 shows that data on dates of admission and discharge and patient's sex were highly reliable, thus confirming the findings of the initial analysis. The levels of agreement on the presence of additional diagnoses and principal procedure were quite high; however, there was considerably less agreement on principal diagnosis. The “no discrepancy” figures slightly under-estimate the data reliability, because they do not include those cases where there was a discrepancy between the field team member and the consultant, but the consultant agreed with the re-abstractor's determination of correct data source. Table 1. Comparison of Data Abstracted by the Consultant and the Field Team (weighted percent) Agreement on correct data source where a discrepancy exists No discrepancy
Agree
Disagree
Total
Admit date
99.6
0.4
-
100.0%
Discharge date
99.2
0.8
-
100.0
Sex
100.0
-
-
100.0
Principal diagnosis*
75.8
0.3
23.9
100.0
Presence of additional diagnoses
93.0
0.6
6.4
100.0
Principal procedure
88.4
0.2
11.4
100.0
Note: Unweighted N = 281 abstracts
*compared to four digits
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APPENDIX F
109
It should be noted that the levels of agreement between the two data sources in this assessment are not fully comparable with similar data in the body of the report because of different weighting factors and differences in the populations to which the statistics are generalizable. More specifically, for the total data set examined by the field team, the data were weighted to reflect the universe of all diagnoses included in the ICDA-8 classification system, as adopted by HCFA (see page 8). The weighted percent of the sample devoted to the 15 specific diagnoses was 40.8, while 59.2 percent of the sample fell into the “all else” category and included those diagnoses necessary to represent the rest of the universe and permit the calculation of net and gross difference rates (see Table 7 on page 33). On the other hand, in the assessment of the field work, the sample was drawn to be representative of the unweighted data set produced by the field team. (Because of the small sample size, applying the basic weights from the total data set to the assessment abstracts would have produced serious distortions.) With this approach, 75.4 percent of the assessment abstracts represented the 15 specific diagnoses, while only 24.6 percent fell into the “all else” category (see Table 4, below). The over-representation of the specific diagnoses in the assessment means that the two data bases are not comparable. Accordingly, it is misleading to use the percent of abstracts with no discrepancy on principal diagnosis between the field team and consultant as an indication of the overall quality of the field team's work. The fact of variation is apparent, however. For that reason further analyses were done to try to determine the extent of differences between the consultant and field team and the underlying reasons. Where there were discrepancies between the field team and consultant, quite often both also disagreed with the Medicare record (see Table 2). This occurred for about fifty percent of the principal diagnoses and about forty-one percent of the principal procedures. In other words, each of the three data sources contained different pieces of information all based on the same patient medical record. For the remaining cases of discrepancies between the field team and consultant, agreement between the field team and the Medicare record was more likely than agreement between the consultant and the Medicare record. Table 2. Data Source in Agreement with Medicare Record when Discrepancies were Found Between the IOM Abstract and Assessment Abstract Data item
Assessment
IOM abstract
Neither
Total
Principal Diagnosis
17.2
33.7
49.1
100.0%
Principal Procedure
21.9
37.3
40.8
100.0
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APPENDIX F
110
To determine whether the sampling categories were related to varying levels of data accuracy, the diagnoses were grouped according to their reason for inclusion in the sample--entire Diagnosis Related Groupings and specific and residual diagnoses within the DRGs (see Table 3). The sampling categories were not influential (confirming the findings of Chapter 3); the level of coding refinement was. Diagnoses compared to four digits were least accurate; AUTOGRP comparisons were most accurate. Table 3. Comparison of Data Abstracted by the Consultant and the Field Team by Sampling Categories and Level of Coding Refinement (weighted percent) Sampling Category
Percent with no discrepancy AUTOGRP
Three-digit
Four-digit
Entire DRGs* (N = 215)
85.8
82.3
79.0
Specific diagnoses (N = 187)
85.4
81.4
77.7
Residual diagnoses**
-
-
-
Diagnostic-specific discrepancy rates are not presented because the numbers of abstracts per diagnosis are so small. However, Table 4 shows the distribution of discrepancies between the field work and assessment by diagnosis. The assessment confirms the finding that reliability is lowest for chronic ischemic heart disease. Where both the field team and assessment data differed from that on the Medicare record but agreed with each other, the extent of agreement on reasons selected to explain discrepancies with the Medicare record was also examined. The possibly subjective nature of this assessment and the need to apply judgment in selecting from the several options were noted in Chapter 2. Because of the sizable number of options and the small number of abstracts reviewed in the assessment, only the general categories of reasons for discrepancy are considered here. Table 5 shows the extent to which the field team and consultant agreed that the reasons for discrepancy between
*Excludes abstracts with a diagnosis listed in the “all else” category in Appendix C. **Results are not presented because of the small number of cases.
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APPENDIX F
111
Table 4. Distribution of Discrepancies between the Field Work and Assessment by Diagnosis Weighted percent of the total number of discrepancies for each diagnosis
Unweighted number of abstracts with discrepancies
Weighted percent of the total number of abstracts in the assessment for each diagnosis
Chronic ischemic heart disease
14.1
7
6.9
Cerebrovascular disease
12.2
7
6.3
Fracture, upper neck of femur
0
6.5
Cataract
0
3.3
4
9.4
0
4.5
Acute myocardial infarction
5.7
Inguinal hernia without mention of obstruction Diabetes
8.1
4
5.2
Hyperplasia of the prostate
7.2
4
5.7
0
9.8
Bronchopneumonia-organism not specified and pneumoniaorganism and type not specified Cholelithiasis/cholecystitis
2.8
12
4.4
Intestinal obstruction without mention of hernia
4.5
6
4.0
Congestive heart failure
4.2
3
2.0
Diverticulosis of intestine
4.7
2
4.0
Bronchitis
0
1.1
Malignant neoplasm of bronchus and lung
0
5.6
All Else
36.5
19
24.6
Total
100.0%
68
100.0%
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APPENDIX F
112
the original abstract and the Medicare record stemmed from difficulties in deciding which diagnosis or procedure was principal (an ordering discrepancy) or from errors in assigning the proper diagnostic or procedural code number (a coding discrepancy). “General agreement” means that both chosen the same general category, but may have selected different specific reasons. For example, one may have decided that the reason was “coding completeness” while the other selected “coding judgment.” Where there was complete agreement, they selected the same general and specific reasons. Where the consultant disagreed with the Medicare record, the reason selected was similar to the one chosen by the field team for about 75 percent of the diagnostic discrepancies (compared to four digits). There was either complete or general agreement between the two on all reasons to explain discrepancies on principal procedure. Table 5. Agreement between the Consultant and Field Team on Reasons for Discrepancies when both Disagreed with the Medicare Record Data (weighted percent) Diagnosis (4 digit)
Procedure
Complete agreement
36.2
40.7
General agreement
39.1
59.3
Complete disagreement
24.7
Total
100.0%
100.0%
(Unweighted N)
(67)
(49)
Summary The reliability of the Institute of Medicine field work was assessed by comparing data provided by HCFA, the IOM abstractors who performed the field work, and the consultant who performed the assessment. The results of the assessment confirm both the findings and the caveats reported in Chapter 3. Data were most reliable for information on hospital admission date, discharge date, and sex. The indication of whether additional diagnoses are present was reported with a high level of reliability. Some difficulty was encountered in conclusively determining which diagnosis or procedure should be regarded as “principal.” The reliability of diagnostic data varied, depending on the level of coding refinement and the specific diagnosis. Overall, agreement between the field team and consultant on principal diagnosis ranged from 75.8 percent with four-digit comparisons to 85.8 percent with AUTOGRP. These figures should
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APPENDIX F
113
not be compared with findings in the body of the report because of different weighting factors and sampling approaches. In some cases all three data sources contained distinctly different notations for principal diagnosis. Reliability was lowest for chronic ischemic heart disease. For principal procedure the level of agreement reached eighty-eight percent. Where both the field team member and assessment consultant disagreed with the Medicare record, they generally agreed on the reasons for about seventy-five percent of the discrepancies on principal diagnosis and for all the discrepancies with principal procedure. These findings should be tempered by the limitations of the assessment. The sample size was very small. The time available for the assessment was limited.
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APPENDIX F 114
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APPENDIX G
115
Appendix G PERCENT OF ABSTRACTS WITH NO DISCREPANCY FOR EACH DIAGNOSIS INCLUDED AS A COMPONENT OF THE SAMPLE OF MEDICARE RECORDS*
Diagnosis related group
Sub-groups
Percent with no discrep-ancy
Total un-weighted N
1. Ischemic heart disease except AMI Specific diagnosis
Chronic ischemic heart disease
36.8
352
Residual diagnosis
Subacute ischemic heart disease (satellite)
34.1
8
Angina pectoris
21.6
7
Asymptomatic ischemic heart disease
-
-
Specific diagnosis
Cerebrovascular diseases*
58.5
320
Residual diagnosis
None
2. Cerebrovascular diseases
3. Fractures Specific diagnosis
Fracture, neck of femur*
-
-
Residual diagnosis
Fracture of other and unspecified parts of femur (satellite)
62.0
6
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APPENDIX G
Diagnosis related group
116
Sub-groups
Percent with no discrepancy
Total unweighted N
Fracture of skull, spine, and trunk
79.2
39
Fracture of upper limb
56.9
34
Fractures of lower limb; excludes fractures of femur
51.6
21
Specific diagnosis
Cataract*
97.4
242
Residual diagnosis
Inflammatory diseases of the eye
60.8
6
Other diseases and conditions of the eye; excludes cataract and blindness
75.3 57.6
7 37
88.1 65.8
15 211
96.7
140
4. Diseases of the eye
5. Acute myocardial infarction Specific diagnosis
Acute myocardial infarction
Residual diagnosis
none
6. Hernia of abdominal cavity Specific diagnosis
Inguinal hernia without mention of obstruction
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APPENDIX G
117
Diagnosis related group
Sub-groups
Percent with no discrepancy
Total unweighted N
Residual diagnosis
Inguinal hernia with obstruction (satellite)
85.9
14
Other hernia of abdominal cavity without mention of obstruction
74.7
57
Other hernia of residual abdominal cavity with obstruction
81.4
5
Specific diagnosis
Diabetes* mellitus
49.7
224
Residual diagnosis
none
7. Diabetes mellitus
8. Diseases of the prostate Specific diagnosis
Hyperplasia of prostate
87.1
229
Residual diagnosis
Prostatitis and other diseases of the prostate
75.9 17.6
8 2
Broncho pneumonia organism not specified and pneumonia organism and type not specified
86.3 72.5
46 163
9. Pneumonia Specific diagnosis
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APPENDIX G
118
Diagnosis related group
Sub-groups
Percent with no discrep-ancy
Total un-weighted N
Residual diagnosis
Pneumonia, type and organism specified
100.0 44.2 0.0 100.0 0.0
3 9 1 1 1
10. Diseases of the gall bladder and bile duct Specific diagnosis
Cholelithiasis and cholecystitis*
66.3 52.2
135 49
Residual diagnosis
Other diseases of the gall bladder and bile duct
30.9
10
11. Miscellaneous diseases of the intestine and peritoneum Specific diagnosis
Intestinal obstruction without mention of hernia
58.1
97
Residual diagnosis
Peritonitis, peritoneal adhesions, and other diseases or the intestine and peritoneum
64.9 0.0 46.8
5 1 102
Congestive heart failure and left ventricular failure
58.5
88
12. Heart failure Specific diagnosis
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APPENDIX G
119
Diagnosis related group
Sub-groups
Percent with no discrep-ancy
Total un-weighted N
Residual diagnosis
Acute heart failure, undefined
24.4
5
13. Enteritis, diverticula, and functional disorders of intestine Specific diagnosis
Diverticulosis of intestine
86.5
138
Residual diagnosis
Noninfectious gastroentiritis and colitis, except ulcerative
37.5
2
Chronic enteritis and ulcerative colitis
79.2
7
Functional disorders of intestine
79.2
20
Specific diagnosis
Bronchitis
89.8
146
Residual diagnosis
None
79.9
141
14. Bronchitis
15. Malignant neoplasm of the respiratory system Specific diagnosis
Malignant neoplams of bronchus and lung
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APPENDIX G 120
Diagnosis related group Sub-groups Percent with no discrepancy Total unweighted N
Residual diagnosis Primary malignant neoplasm of respiratory system except of bronchus and lung 15.8 54.0 22.6 3 15 2
*Indicates specific or “target” diagnosis used in prior re-abstracting study.
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APPENDIX H
121
Appendix H NET AND GROSS DIFFERENCE RATES IN DESIGNATION OF PRINCIPAL DIAGNOSIS (BASED ON FOUR-DIGIT COMPARISONS OF SPECIFIC DIAGNOSES)
IOM abstract
Percent of abstracts with indicated principal diagnoses
Net and gross difference rates (times 1,000)
Medicare record
Net
Gross
-43.4
73.1
19.7
41.8
6.7
9.3
Chronic ischemic heart disease
Other
Total
Chronic ischemic heart disease
3.8
1.5
5.3
Other
5.8
88.9
94.7
Total
9.6
90.4
100.0
Cerebrovascular diseases
Other
Total
Cerebrovascular diseases
4.2
3.1
7.3
Other
1.1
91.6
92.7
Total
5.3
94.7
100.0
Fracture, neck of femur
Other
Total
Fracture, neck of femur
1.5
0.8
2.3
Other
0.1
97.6
97.7
Total
1.6
98.4
100.0
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APPENDIX H
IOM abstract
122
Medicare record Cataract
Other
Total
Cataract
2.9
0.2
3.1
Other
0.0
96.9
96.9
Total
2.9
97.1
100.0
Acute myocardial infarction
Other
Total
Acute myocardial infarction
1.7
1.1
2.8
Other
0.6
96.6
97.2
Total
2.3
97.7
100.0
Inguinal hernia without mention of obstruction
Other
Total
Inguinal hernia without mention of obstruction
1.3
0.2
1.5
Other
0.0
98.5
98.5
Total
1.3
98.7
100.0
Diabetes mellitus
Other
Total
Diabetes mellitus
1.3
0.8
2.1
Other
1.1
96.8
97.9
Total
2.4
97.6
100.0
Hyper-plasia of the prostate
Other
Total
Hyperplasia of the prostate
2.0
0.4
2.4
Other
0.3
97.3
97.6
Total
2.3
97.7
100.0
Net
Gross
1.0
2.0
5.8
16.5
1.3
2.0
-3.2
19.0
1.4
6.5
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APPENDIX H
IOM abstract
123
Medicare record
Bronchopneumonia-organism not specified and pneumoniaorganism and type not specified Other Total
Bronchopneumoniaorganism not specified and pneumonia-organism and type not specified 2.2 1.4 3.6
Other 0.5 95.9 96.4
Total 2.7 97.3 100.0
Cholelithiasis/cholecystitis Other Total
Cholelithiasis/cholecystitis 1.3 0.9 2.2
Other 0.3 97.5 97.8
Total 1.6 98.4 100.0
Intestinal obstruction without mention of hernia Other Total
Intestinal obstruction without mention of hernia 0.5 0.5 1.0
Other 0.3 98.7 99.0
Total 0.8 99.2 100.0
Net Gross
8.7 18.7
6.0 11.9
2.1 7.7
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APPENDIX H
IOM abstract
124
Medicare record
Congestive heart failure and left ventricular failure Other Total
Congestive heart failure and left ventricular failure 1.0 2.1 3.1
Other 0.7 96.2 96.9
Total 1.7 98.3 100.0
Diverticulosis of intestine Other Total
Diverticulosis of intestine 1.3 0.7 2.0
Other 0.2 97.8 98.0
Total 1.5 98.5 100.0
Bronchitis Other Total
Bronchitis 1.0 0.5 1.5
Other 0.3 98.2 98.5
Total 1.3 98.7 100.0
Malignant neoplasm of bronchus and lung Other Total
Malignant neoplasm of bronchus and lung 0.9 0.2 1.1
Other 0.2 98.7 98.9
Total 1.1 98.9 100.0
Net Gross
13.7 27.4
4.8 8.7
2.1 8.0
-0.1 4.1
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APPENDIX I
125
Appendix I GENERAL COMPARISON OF ASSESSMENTS OF THE RELIABILITY OF MEDICARE RECORDS MAINTAINED BY THE HEALTH CARE FINANCING ADMINISTRATION AND HOSPITAL DISCHARGE ABSTRACTS COMPILED BY PRIVATE ABSTRACT SERVICES (WEIGHTED PERCENTS) Data items
No discrepancy
Medicare record or private abstract
Correct data source where a discrepancy exists IOM abstract
Either
Neither
Total
HCFA's Medicare records Diagnoses Four-digit
57.2
2.3
35.7
0.2
4.6
100.0%
Three-digit
61.9
1.9
31.6
0.2
4.4
100.0
All procedures
78.9
1.7
17.3
0.4
1.7
100.0
Procedures coded
56.6
5.2
34.7
2.3
1.2
100.0
No procedures coded
89.7
8.9
1.4
100.0
Private abstract service Medicare records Diagnoses Four-digit
64.4
1.6
23.3
0.3
10.4
100.0
Three-digit
73.9
0.9
15.2
0.2
9.8
100.0
All procedures
72.5
2.6
7.7
0.1
17.1
100.0
Procedures coded
64.8
3.9
9.1
0.2
22.0
100.0
No procedures coded
86.9
8.2
100.0
4.9
Private abstract service Medicare and Medicaid records Diagnoses Four-digit
65.2
1.6
22.2
0.2
10.7
100.0
Three-digit
74.0
0.9
14.8
0.2
10.0
100.0
All procedures
73.2
2.5
7.8
0.2
16.3
100.0
Procedures coded
66.0
3.8
9.3
0.2
20.7
100.0
No procedures coded
86.7
5.3
0.1
8.0
100.0
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APPENDIX I 126
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APPENDIX J
127
Appendix J COMPARISON OF THE RELIABILITY OF HCFA MEDICARE RECORDS AND PRIVATE ABSTRACT SERVICE MEDICARE ABSTRACTS FOR SELECTED DIAGNOSES COMMON TO BOTH STUDIES (SOURCE: IOM FIELD DATA) Percent of abstracts with no discrepancy
Weighted percent of all abstracts that each diagnosis represents
Diagnosis
ICDA-codes*
HCFA's Medicare records
Private service Medicare records
HCFA's Medicare records
Private service Medicare records
Chronic ischemic heart disease
412.0;412.9
36.8
27.9
9.8
8.6
Cerebrovascular diseases
430.0-430.9
58.5
79.8
6.9
10.1
Fracture, neck of femur
820.0-820.9 (820.0-820.5;820.9)
70.5
81.0
2.0
3.7
Cataract
374.0-374.9 (374.0;374.1;374.8;374.9)
97.3
94.3
3.0
7.5
Acute myocardial infarction
410.0-410.9 (410.0;410.1;410.9)
67.3
67.7
2.4
5.3
Inguinal hernia without mention of obstruction
550
96.7
87.9
1.3
2.1
Diabetes mellitus
250.0-250.9 (250.0;250.9)
49.7
60.5
2.5
2.9
Cholelithiasis/ cholecystitits
574.0-574.9;575
62.8
74.8
2.0
3.5
*Where diagnostic codes for sampling private abstract service records differed from those in the study of Medicare records, they are in parentheses.
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APPENDIX J 128
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APPENDIX K 129
Appendix K*
STANDARD ERRORS AND CONFIDENCE INTERVALS FOR STATISTICS BASED ON MEDICARE DATA
Statistic Point estimate Standard error Confidence interval
Percent of abstracts with no discrepancy for principal diagnosis coded to four-digits 56.80 1.47 54.4-59.2
Percent of abstracts with no discrepancy for principal procedure 78.40 1.34 73.7-80.6
*A 0.10 alpha level was used in estimating the confidence intervals given in this appendix.
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APPENDIX K
130
STANDARD ERRORS AND CONFIDENCE INTERVALS FOR NET DIFFERENCE RATES (TIMES 100) BASED ON MEDICARE DATA Diagnosis
Point estimate
Standard error
Confidence interval
Chronic ischemic heart disease
-4.49
0.62
-5.51--3.47
Cerebrovascular diseases
2.21
0.45
1.47-2.95
Fracture, neck of femur
0.64
0.20
0.31-0.97
Cataract
0.13
0.07
0.01-0.25
Acute myocardial infarction
0.57
0.17
0.29-0.85
Inguinal hernia without mention of obstruction
0.09
0.06
-0.01-0.19
Diabetes mellitus
-0.23
0.32
-0.76-0.30
Hyperplasia of the prostate
0.20
0.16
-0.06-0.46
Bronchopneumoniaorganism not specified and pneumoniaorganism and type not specified
0.66
0.27
0.21-1.11
Cholelithiasis/cholecystitis
0.78
0.14
0.55-1.01
Intestinal obstruction without mention of hernia
0.24
0.06
0.14-0.34
Congestive heart failure and left ventricular failure
1.71
0.26
1.28-2.14
Diverticulosis of intestine
0.47
0.15
0.22-0.72
Bronchitis
0.23
0.14
0.00-0.46
Malignant neoplasm of bronchus and lung
0.13
0.08
0.00-0.26
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APPENDIX K
131
STANDARD ERRORS AND CONFIDENCE INTERVALS FOR GROSS DIFFERENCE RATES (TIMES 100) BASED ON MEDICARE DATA Diagnosis
Point estimate
Standard error
Confidence interval
Chronic ischemic heart disease
7.55
0.37
6.94-8.16
Cerebrovascular diseases
4.38
0.23
4.00-4.76
Fracture, neck of femur
0.86
0.17
0.58-1.14
Cataract
0.24
0.07
0.12-0.36
Acute myocardial infarction
1.56
0.23
1.18-1.94
Inguinal hernia without mention of obstruction
0.18
0.03
0.13-0.23
Diabetes
1.99
0.18
1.69-2.29
Hyperplasia of the prostate
0.67
0.10
0.50-0.84
Bronchopneumoniaorganism not specified and pneumoniaorganism and type not specified
1.85
0.47
1.07-2.63
Cholelithiasis/cholecystitis
1.34
0.29
0.86-1.82
Intestinal obstruction without mention of hernia
0.71
0.11
0.53-0.89
Congestive heart failure and left ventricular failure
2.89
0.42
2.20-3.58
Diverticulosis of intestine
0.85
0.17
0.57-1.13
Bronchitis
0.80
0.26
0.37-1.23
Malignant neoplasm of bronchus and lung
0.38
0.05
0.30-0.46
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APPENDIX L 132
Appendix L
STANDARD ERRORS AND CONFIDENCE INTERVALS FOR STATISTICS BASED ON DATA FROM PRIVATE ABSTRACTING SERVICES
Statistic Point estimate Standard error Confidence interval
Percent of abstracts with no discrepancy for principal diagnosis coded to four-digits 65.20 1.14 63.32-67.08
Percent of abstracts with no discrepancy for principal procedure 73.20 1.53 70.68-75.72
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APPENDIX L
133
STANDARD ERRORS AND CONFIDENCE INTERVALS FOR NET DIFFERENCE RATES (TIMES 100) BASED ON DATA FROM PRIVATE ABSTRACTING SERVICES Diagnosis
Point estimate
Standard error
Confidence interval
Cerebrovascular diseases
1.07
0.27
0.62-1.52
Chronic ischemic heart disease
-2.24
0.48
-3.03--1.65
Acute myocardial infarction
0.86
0.08
0.78-0.94
Diabetes mellitus
0.09
0.33
-0.06-0.24
Carcinoma of the breast
-0.10
0.04
-0.17-0.03
End stage renal disease
0.04
0.02
0.01-0.07
Cholelithiasis/cholecystitis
0.31
0.13
0.10-0.52
Hernia without obstruction
-0.02
0.04
-0.09-0.05
Delivery
0.23
0.05
0.15-0.31
Hypertrophy of tonsils and adenoids
-0.01
0.02
-0.04-0.02
Fracture of neck of femur
0.34
0.12
0.14-0.54
Displacement of inter-vertebral disc
0.10
0.06
0.00-0.20
Cataract
0.18
0.10
0.01-0.35
Neuroses
0.09
0.13
-0.12-0.30
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APPENDIX M
Arteriogram Coronary arteriogram Carotid arteriogram Cerebral arteriogram Cholangiogram Cholecystectogram Cobalt therapy Electroencephalogram Inhalation therapy Intravenous pyelogram Iodine 131 cancer treatment Lumbar myelogram Physical therapy Retrograde pyelogram Scans
Bone scan
Brain scan
Lung scan
Renal scan
Thyroid scan
134
Appendix M
SELECTED EXAMPLES OF PRINCIPAL PROCEDURES NOT LISTED IN CPT, AS DETERMINED BY THE IOM FIELD TEAM MEMBERS (coded 0000 on Medicare Record)