The Johns Hopkins Ambulatory-care Coding Scheme By Donald M. Steinwachs and Alvin I. Mushlin A classification and coding system for ambulatory-care problems has been developed at the Johns Hopkins Medical Institutions and three affiliated institutions. The provider's statement of the patient's problem, as recorded on an encounter form, is kept in a computer file. Codes from the classification scheme, based on those used in four existing schemes, are automatically assigned to diagnoses, symptoms, well-care services, and treatment procedures categorized by physiological system and subsystem. About 85 percent of recorded problems are machine-codable; the remainder are alphabetized for efficient manual coding. The coding system is integrated with an overall information system that allows linkage of coded problem data to diverse data on patient and provider characteristics. Examples are given of the uses and limitations of the linked data for care evaluation, management, and dinical research.
The importance of ambulatory care in the prevention, diagnosis, and treatment of disease is generally recognized, but the content and quality of ambulatory care have not been thoroughly studied. The need for information on such matters led to recommendations for a uniform minimal data set for ambulatory care [1,2] that could be incorporated into existing routine information systems to serve the needs of managers, dinicians, and health services researchers. The potential of uniform data bases for enhancing our understanding of patterns of patient care, its quality, its cost, and the influence of organizational, financial, and manpower characteristics on cost and quality has been frequently cited. However, a variety of methodological problems require further resolution if the cited potential is to be fully realized. One of the more complex methodological problems is the classification of problem/diagnostic information generated in the patient-provider encounter. Since 1970, the Health Services Research and Development Center of the Johns Hopkins Medical Institutions has been involved in developing routine encounter-based information systems incorporating the recommended minimal data set. The center has worked closely with two prepaid group practice programs-the Columbia Medical Plan and the East Baltimore Medical Plan-and two hospital outpatient
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This research was supported in part by grant no. HS 00429 from the National Center for Health Services Research, DHEW. Address communications and requests for reprints to Donald M. Steinwachs, Research Manager, Health Services Research and Development Center, Johns Hopkins Medical Institutions, 624 North Broadway, Baltimore, MD 21205. Alvin 1. Mushlin is now associate professor of medicine at the University of Rochester.
0017-9124/78/1301-0036/$01.40/0 0 1978 Hospital Research and Educational Trust
departments-the Johns Hopkins Hospital Medical Clinic and the THE JHACS Baltimore City Hospital Housestaff Clinic. In each of these settings, an encounter form is completed by the provider for each patient visit. The encounter form identifies the patient, provider, date of service, appointment status, purpose of visit, laboratory and radiology procedures done, disposition, and the provider's written statement of the patient's condition, i.e., problem or diagnosis. All of this information except the written statement is precoded on the form and requires only check-marking. (The patient's statement of the reason for the visit is not part of the routine system but has been collected in special studies.) The encounter data are linked with registration information, which includes such items as date of birth, sex, source of insurance, and a unique patient identifier or medical history number for each patient. In the prepaid group practices (which also treat fee-for-service patients) the registration data also identify the enrollee population. The registration and encounter data are integrated into a historical utilization file with discharge and prescription data obtained from other sources. In all four sites the provider's written statement specifying the patient's conditions evaluated, diagnosed, or treated during the visit is keypunched, and the complete text is retained in the computer files. This artide describes the coding scheme developed to dassify the problem/diagnosis data and illustrates the evaluative applications that the coding scheme allows.
Development of the Johns Hopkins Ambulatory-care Coding Scheme (JHACS) The development of the JHACS, begun in 1973, had four fundamental phases: (1) analysis of data from the four ambulatory care sites to determine what conditions were presented and establish the content of the coding scheme; (2) examination of existing coding schemes to determine their usefulness in dassifying ambulatory care data; (3) identification of uses desired for the coded data; and (4) evaluation of the coding system by testing its effectiveness in selected applications. Data Collection Four alternative ways of collecting problem data were judged on accuracy of data and recording method, content of the coding scheme (including specificity, number of categories, and comprehensiveness), and the extent to which the coded data could meet the intended uses. The four alternative methods we considered were using a checklist of problem categories, having the provider code patient conditions, abstracting the medical notes, and having the provider record the patient's problems on the encounter form in his own words. We chose the last of these methods, primarily on considerations of specificity, completeness, and reliability. Checklists can facilitate data collection, but they sacrifice specificity to the need for preconceived categories. Provider coding of problem data, as used in the International Classification of Health Problems in Primary Care (ICHPPC) [3], places both work load and responsibility for accuracy on the provider,
SPRING
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STEINWACHS & MUSHLIN
which may not be acceptable to providers and may limit the reliability of the process. Abstracting medical notes places the burden on clerical staff, but without clear and consistent problem-oriented records uniform identification of problems cannot be assured. Provider recording of the patient's problems in his own words gives the highest level of specificity since the complete text is retained in the computer files: it is easy to complete, and illegibility is a problem in only a small percentage of cases. The desired uses of the coded data also influenced the content and structure of the coding scheme. Although the data were intended primarily for use in research and evaluation, we considered management and clinical uses, e.g., accounting, billing, and selection of patients for special review or follow-up. Potential uses of the data were categorized as statistical and case-identification applications; statistical applications were categorized further as case-mix studies, incidence and prevalence calculations, and studies of patterns of patient care and provider practice. The utility, for these applications, of data organized by the final version of the coding scheme will be discussed in a later section.
Review of Existing Coding Schemes Our examination of problem data revealed that providers re-
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corded information in four general categories: signs and symptoms not resolved into diagnoses, which accounted for 10 to 20 percent of recorded problems, depending on the patient population and the provider's specialty; preventive and well care (including physical examinations and routine well-child visits), which varied from 2 to 23 percent; surgical and nonsurgical procedures (excluding routine laboratory and radiology procedures), which accounted for less than 1 percent; and traditional diagnoses specifying a disease or injury, which, along with a small percentage of uncodable entries, made up the remainder. We systematically reviewed the specificity and comprehensiveness of several coding schemes as applied to data in these four general categories. The schemes reviewed included the International Classification of Diseases, Adapted (ICDA) [4], the Hospital Adaptation of ICDA (HICDA) [5], the Diagnostic and Statistical Manual of Mental Disorders (DSMMD) [6], the Kaiser symptom-classification system [7], Renner and Piernot's adaptation of the Kaiser scheme [8], and later, when they became available, the ICHPPC [3], the National Ambulatory Medical Care Survey (NAMCS) symptom-classification scheme [9], and the recently suggested revision of the last [10]. Most of these were not designed to code provider statements of problems diagnosed, evaluated, or treated in an ambulatory visit. The ICDA and HICDA are pnmarily directed at mortality dassifications and common morbidities treated on an inpatient basis. The RennerPiernot and Kaiser coding schemes were developed to classify presenting symptoms and do not incorporate diagnoses or well care. The NAMCS scheme was designed to classify the patient's primary reason
for the visit, as stated by the patient. Experience has shown that patients respond in terms of diagnoses and procedures, as well as signs and symptoms. Thus a coding scheme applicable to the patient's reason for the visit will overlap a scheme designed for the provider's statement of the problem. However, even if both items were to be coded within the same scheme, they are substantially different pieces of information representing distinct perspectives on the use of services. (The suggested revisions of the NAMCS scheme [10] considerably extend its capability to code diagnoses and procedures, which enhances its utility for coding provider-stated problems.) Only the ICHPPC was specifically designed to meet the need of practitioners to code ambulatory problems, with a minimal number of categories and consistency with the ICDA. None of these systems was useful for the entire spectrum of applications that was anticipated. Although some of the existing schemes were adequate for coding a single category of data, none was adequate for all categories of problems/diagnoses. For example, Table 1 shows a summary of our analysis of seven existing schemes as applied to musculoskeletal problems that appeared in our collection of problem data. For 37 musculoskeletal diagnoses, the ICDA offered codes adequate for all; the HICDA offered codes similarly adequate for 81 percent of these diagnoses and offered even more specificity for 8 percent of them but less specificity for 11 percent. On the basis of such comparisons, we selected sections from the various coding schemes that were the most specific for problems that occurred with sufficient frequency in our data; the result was an abridgment and integration of those schemes specific to the needs of ambulatory care. For diagnoses (except for mental disorders) we selected the ICDA instead of the HICDA because it is more widely used, not because of any clear advantage in specificity. The DSMMD, which is compatible with but more extensive than the mental disorders section of the
THE JHACS
Table 1. Percentage of Musculoskeletal Problems Seen in Ambulatory Care that are Coded with Adequate Specificity (A), More Specificity (M), and Less Specificity (L) by Seven Classification Schemes Problem category
Diagnoses (N = 37)
Injuries (N = 40)
Symptoms (N = 28)
Procedures,
M, %A,% L,%
M, %A,% L,%
M,% A,% L,%
M, % A,% L,%
Classification scheme
ICDA .......... HICDA ........ Kaiser .........
100* 8 81 11 Not included Not included Not included .. ... 100 8 92
100*
(Nw = 3)
... 100 100 82 ... 100*... . 25 Not included Renner-Piemot . ... 100* Not induded NAMCS ....... ... ... 100 100 NAMCS, revised 4 96 ... 100 ... ICHPPC ....... 100 100 * Indicates scheme from which codes were taken for the musculoskeletal section of the JHACS. ...
...
...
...
...
3 90 8 Not induded Not included ... ... 100 13 23 65 20 80 ...
...
...
...
...
18 75
...
...
...
STEINWACHS & MUSHLIN
Table 2. Code Sources for Major Problem Categories and Sections of the Johns Hopkins Ambulatory-care Coding Scheme Section
Category Infective and parasitic
Neoplasms
Diagnosis Symptom Well care Procedure ..................
.............................
Endocrine, nutritional, and metabolic Blood and blood-forming organs ........
...
Mental disorders ....................... Nervous system and sense organs ........
Circulatory ............................ Respiratory ............................ Digestive .............................. Genitourinary ......................... Pregnancy and childbirth .............. Skin and subcutaneous tissue ............ Musculoskeletal and connective tissue Congenital abnormalities ............... Perinatal morbidity .................... Nature of injury ....................... Extemal cause of injury ............... ....
Nonspecific
ICDA ICDA ICDA ICDA DSMMD ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA
... ... ...
HICDA
R-P R-P R-P R-P R-P R-P R-P R-P R-P R-P ... ... ...
... HICDA
...
...
...
... ...
... ... ... HICDA HICDA ...
... ...
HICDA HICDA ...
HICDA HICDA HICDA HICDA HICDA HICDA HICDA HICDA
...
...
...
...
...
...
...
...
... R-P ............. General medical examination for ... HICDA ... administrative purposes .............. ..... HICDA Other general medical examination . ... Special examinations and investigations .. ... HICDA HICDA Medical and surgical aftercare ............. ... HICDA Nonmedical reason for visit ............. ... Other personal history affecting health ... HICDA Abnormal findings in laboratory and examinations .................... ... HICDA HICDA Prophylactic vaccinations and inoculations ... Family planning ....................... ... ... HICDA Referral and evaluations ............... ... ... HICDA Diagnostic and nonsurgical procedures ... ... * Renner and Piernot's adaptation [8] of the Kaiser classification scheme ...
... ... ... ... ... ...
... ... ... ...
HICDA [7].
ICDA, was the source of diagnostic codes for mental disorders. We chose Renner and Piernot's scheme for the coding of symptoms because it is more specific than any other. For well care and procedures the choice was the HICDA because of its greater specificity. Table 2 shows the four major sections of the JHACS by problem category, with the source of the code for each problem in the relevant section or sections.
Structure of the JHACS Within each of four major sections of the JHACS, up to three levels of aggregation are represented by system categories, subsystems, and the coded entity. Typically a system category represents a physiological system (e.g., digestive) and a subsystem represents a major disHEALTH ease site within the system. The coded entity is the specific disease or SERVICES within the subsystem and system. RESEARCH injury The diagnosis section of the JHACS is divided into 17 organ and disease systems paralleling categories in the ICDA, as shown in Table
40
2; the symptoms section follows the format of the Renner-Piernot THE JHACS coding scheme, with symptoms subdivided into 10 organ systems and a nonspecific category. The well care and the operations and therapeutic procedures sections follow the HICDA format, with 14 categories specifying the type of well care, including medical and surgical aftercare, and operations and nonsurgical procedures divided into 11 organ-specific categories. The HICDA is more comprehensive and specific for well care than the ICDA, including a section on abnormal laboratory and examination findings for which the ICDA has only limited provisions. Also, operations and therapeutic procedures are arranged by anatomic location in the HICDA, instead of by surgical specialty as in the ICDA. In general, the codes used in the JHACS were taken from whatever system offered the most specificity in a given problem category. Not all of the available codes in a given source scheme were needed for a given problem category, however, because of the difference in content between ambulatory and inpatient care. Within the diagnosis section of the musculoskeletal category, for example, the ICDA offers codes for 199 major terms, but only 77 of these codes were needed for the JHACS. For the symptom section of the same category the Renner-Piernot coding scheme offers 120 coded terms; only 28 were needed for the JHACS. The exclusions resulted from our frequency criterion: to be included, a term had to occur at least four times in a year's data. This rule was systematically applied to data from the Columbia Medical Plan to build the initial dictionary for ambulatory care problems and then to the three other sites to determine what new terms and codes were needed. Currently there are 984 distinct codes in the dictionary. The value of the frequency criterion in limiting the growth of the dictionary was confirmed by examination of the uncoded items generated during 1975 by the Columbia Medical Plan: if these were to be added to the dictionary, the additions would increase the number of distinct codes by 50 percent but add only 1.4 percent to the percentage of all conditions coded. Code Format. The code formats follow the formats of the parent schemes. To avoid complications of duplicate codes and overlapping numbering systems, a letter precedes the numeric portion of each code to designate the system category into which the condition falls. Two supplementary categories are used for uncodable entries and for conditions that ar6 uncoded because of infrequent occurrence. The advantage of this method of assigning codes is that new ones can be easily added from the appropriate source as needed.
Computerized Coding An automated processor for diagnostic data was developed, beginning in 1973, on the basis of previous experience in the Comprehensive Child Care Center at Johns Hopkins. This program had implemented a computerized scheme for coding pediatric diagnoses 1978 abstracted from the medical note. Significant extensions of that methodology were necessary to handle a much larger dictionary and to 41
STEINWACHS obtain maximal system effectiveness. These extensions induded a & MUSHLIN computerized process to skip irrevelant prefixes and to identify selected root words that were sufficiently specific to indicate the appropriate code. The computerized version of the dictionary includes 5,064 synonyms, 2,000 of which are common abbreviations or misspellings that permit the computer to match variations in terminology and spelling and to link these with the appropriate code. Four steps are involved in the automated processing and coding operation: 1. The complete text of the provider's statement of the patient's condition is compared with the terms and synonyms of the dictionary, and exact matches are coded. 2. Entries still uncoded are compared with dictionary terms that have been specifically designated as "general." These terms require only that the initial wording match the general term; all subsequent terminology is ignored. For instance, "muscle spasm" is designated a general term and is used to code musde spasm left leg, muscle spasm while swimming, muscle spasm recurrent in right forearm, and the like. 3. A list of selected prefixes, such as "recent" and "recurrent," that can be ignored in coding is compared with all uncoded entries. When such prefixes are identified in an entry, they are ignored and the first two steps of the coding process are repeated for whatever phrase they precede. 4. Any uncoded entries that remain are sequenced alphabetically and printed out for manual coding. This step indudes a frequency count to identify terms and synonyms that repeat and that should be considered for inclusion in the dictionary. In the manual process, all remaining uncoded statements are coded or assigned to the categories for uncodable entries and those that are potentially codable but too infrequent, and entered into the computer file. The effectiveness of automated coding, in terms of the percentage of entries that are machine-codable, varies by site and appears to be sensitive to staff characteristics (size and turnover). At the two prepaid group practice dinics, the automated coding process links 85 percent of the diagnostic entries to appropriate codes; at the Johns Hopkins Medical Clinic, only 70 percent of the entries are machinecodable because of the large number of different providers and the frequent turnover of housestaff. At the Baltimore City Hospital dinic there are fewer physicians, and diagnostic information receives routine derical review before keypunching; as a result, 90 percent of entries are automatically coded. Another measure of the effectiveness of this computerized methHEALTH odology is the relative frequency of uncodable entries. In 1975, the RESEARCH two group practice clinics and the Baltimore City Hospital dinic found from 2.5 percent to 3.1 percent of all problem entries uncod42 able; at Johns Hopkins 12.5 percent were uncodable; this percentage
included illegible entries and entries too nonspecific to be coded. (Un- THE JHACS coded-as opposed to uncodable-entries at all four sites ranged from 1.4 percent to 2.7 percent; these are periodically reviewed and added to the dictionary if justified by frequency of occurrence or when a specific research or clinical need develops.) Operating Cost The JHACS is currently operational in the four sites mentioned, which together experience more than 150,000 ambulatory visits per year. The programs are all written in COBOL for IBM 370-145/ 135 computer systems. Encounter and registration data are typed in weekly from keyboard to tape, in batch mode; diagnostic data from the accumulated utilization file are copied monthly onto a separate file for coding. Both the provider's statement of the patient's problem and the JHACS code are retained as part of the utilization history of each patient. Three categories of costs are incurred with this approach to the capture and coding of diagnostic data. Data-entry costs for key-to-tape transfer of written diagnoses average approximately $.02 per entry, and running the computer coding program costs $5 plus $2 per 1,000 entries processed. Manual coding of entries not recognizable to the computer dictionary can be done at the rate of 300-400 per day by a trained clerk, so that the total of these costs for 100,000 diagnostic entries, assuming 85 percent are machine-codable, averages $4,200. Additional expenses arise for dictionary maintenance, which includes addition of new synonyms and periodic tabulations to monitor the qu.rlity of both computerized and manual components of the coding system.
Applications: Utility of the System Some example applications of the coding system as developed for medical care evaluation and research will provide insight into its utility and into some unresolved issues of the coding and use of ambulatory-care problem data. Our statistical applications of coded data have addressed questions of manpower, organization of service, and quality of care, using four basic types of data groupings with modifications depending on the particular question; these four data groupings are case-mix distributions, linkage of diagnoses and population characteristics, statistical summaries of care provided to patients with selected characteristics (e.g., diagnoses, age, sex, and enrollment duration), and statistical summaries of care given by a selected provider or group of providers. Case-mix Applications The distribution of patients or patient visits by types of conditions presented is influenced by organizational configurations, available manpower, and population characteristics; in turn, case mix 1978NG influences the overall use of resources. Thus it relates to productivity differences among organizations and providers. 43
STEINWACHS & MUSHLIN
Case-mix data were used to examine the effect of adding more health associates (comparable in function to nurse practitioners) in the departments of pediatrics and medicine of the Columbia Medical Plan [11] in terms of changes in the distributions of care provided by physicians and health associates. In 1971 the department staff consisted of three full-time equivalent (FTE) physicians and two health associates; in 1974 there were 3.2 FTE physicians and 5.2 FTE health associates. The volume of visits served indicated a shift in physician work load from ambulatory care to inpatient care. Table 3 shows the changing distribution of care provided by the health associates for selected conditions over the three-year period: case mix changed to include a higher proportion of chronically ill patients, and more physician time was spent in consultation with the health associates. Calculation of the numbers of physicians that would have been required to serve the 1974 volume of visits in the absence of the health associates suggested that a substitution rate of 2.5 health associates per fulltime physician had been achieved. Although case-mix distributions can provide insight into practice content and temporal changes in the roles and functions of providers, such data have limited utility for management and program planning because of the lack of a one-to-one relation between resources (visits, laboratory procedures, etc.) and diagnoses. Some visits limited to a specific procedure include no diagnostic information, and some, particularly from chronically ill patients, have multiple diagnoses. When no diagnosis or problem is recorded, other data items on the encounter form permit categorization of the visit as related to illness, injury, well care, etc.; but when multiple conditions are diagnosed, evaluated, or treated in a visit, other complexities are introduced. Classifying visits by primary problem imposes the restrictive assumption that
Table 3. Percent of Patient Visits in Selected Categories Served by Health Associates Alone and with Physician in Three Periods Visit category
Allt
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.................
Period 2* Period 1* With MD, Alone, With MD, Alone, 5
0*
11
4
Period 3* Alone, With MD,
30
10
4 1 5 3 0 Diabetes mellitus . .......0 3 1 11 3 0 Hypertension ......... 0 3 56 13 3 43 35 Pharyngitis .35... 9 10 0 5 10 0 Asthma ... 31 4 6 0 28 .0..0 Bronchitis 9 4 32 0* 2 General physical exam . 0t 4 18 19 0* 5 Headache .1... 4 0 2 27 0 0 21 Obedty .............. 7 1 1 13 0 Back pain .0..0 * Period 1 was from July 1971 through June 1972; period 2 was from July 1972 through June 1973; period 3 was from July 1973 through June 1974. t Indudes all visits, not only those specified. * Less than 0.5 percent.
other conditions have little effect on the total resources used. Another THE JHACS approach is to collect data in a problem-oriented format that relates the use of ancillary services and medications to specific conditions; however, it is not uncommon for treatments or procedures to relate to more than one problem. For these reasons, and because of the complexities of form design, little progress has been made in relating case-mix data to resource use, although the potential is worthy of exploration. and Prevalence Applications The system allows estimation of the incidence and prevalence of specific conditions within a defined population. Estimates of the extent of undiagnosed and untreated cases can be derived by comparing prevalence levels of diagnoses with epidemiologic data on the expected prevalence, thus identifying needs for increased efforts directed at detection and treatment. Annual and more frequent estimates of the incidence of new cases of acute conditions and the prevalence of chronic conditions aid in planning future programs to meet the needs of a changing population or the effects of seasonal variations in disease incidence. For example, Table 4 shows a significant increase, over the years 1971 to 1975, in the prevalence of diagnosed hypertension among those who had been enrolled in the Columbia Medical Plan for at least one year. The increase results from changes in the population as well as from diagnosis of new cases among those with relatively long periods of enrollment, but the magnitude of the change may reflect increasing concern among providers and patients about detection of Incidence
Table 4. Chang Patterns of Diagnosis and Treatment of Hypertension Among Columbia Medical Plan Enrollees, 1971-1975* Enrolleet
age and sex
Enrollees diagnosed and given prescription, % FY 1971 FY 1972* FY 1973* FY 1974* (N = (N = (N= (N =
4494)
6140)
1532)
917)
Hypertensives given new prescription or refill, % FY 1971 FY 1972* FY 1973* FY 1974* (N = (N= (N = (N= 83) 156) 228) 312)
17 and older, total 1.8 2.5 3.0 3.4 80 82 Male ........... 1.7 2.8 3.1 84 3.2 89 1.9 Female ......... 3.0 2.3 76 74 3.6 1.0 1.4 0.8 17A44, total ....... 1.3 67 75 Male ........... 1.1 1.4 0.7 77 1.5 89 Female ......... 0.9 1.0 1.3 59 1.3 60 5.6 8.1 8.6 45-64, total ....... 86 9.5 87 9.1 5.5 Male ........... 8.7 8.6 90 91 10.4 Female ......... 7.1 5.8 8.4 84 78 65 and older, total . 13.8 19.5 22.4 86 25. 87 24A Male ........... 16.7 27.4 83 25.9 83 Female ......... 12.3 17.0 25.4 89 20.1 88 Data shown by fiscal years beginning July 1 and ending June 30. t Only those enrolled for the entire year are included in each year's data.
80 83 78
72 73 71 83 91 76 89 82 93
82 80 84 74 72 76 86 88 85 85
73 91
STEINWACHS
hypertension. During each of the four years, approximately 80 per-
& MUSHLIN
cent of diagnosed hypertensives received one or more prescriptions for hypertension. This information suggests that a high proportion of those diagnosed receive care, but it does not indicate the content or outcomes of care. Medical chart review would give insight on this question and allow comparison of severity and degree of control of hypertension for those diagnosed soon after joining the plan and those diagnosed later in their enrollment. Other tabulations have raised questions concerning the use of a single diagnostic entry to identify chronically ill persons in the absence of corroborating data on treatment or subsequent visits for the condition. Some patients found to have high blood pressure may never be ultimately diagnosed as hypertensive; they may be considered borderline or labile and not receive therapy. How such cases enter an analysis of incidence and prevalence depends on linkages to other data, e.g., the medical chart. Patterns of incidence for acute episodic conditions are derived by identifying episodes of care for a specific condition, rather than from individual visits. Single episodes may result in multiple visits, each with the same problem statement repeated. By linking visits into discrete episodes of care, it has been possible to determine the extent of seasonal variation in incidence and the portion of incidence attributable to repeated episodes over a defined period.
Pattemrs of Care Defining episodes also provides a framework for examining the content of care in terms of the use of resources and quality of care provided. Our focus has been on identifying questions for research on the delivery of health services. Otitis media provides an example of an acute episodic condition in which the coding system represented an important starting point but additional investment was necessary to gain adequate specificity. The role of follow-up care in preventing hearing impairment and repeated episodes of otitis is not totally dear, and follow-up care for a condition as common as otitis has significant implications for the allocation of resources and the cost of care. All episodes of otitis media were categorized as purulent, serous, or mixed; preliminary analyses indicated that, in the aggregate, follow-up care had become less frequent for serous episodes and more frequent for purulent episodes of otitis. This focused attention on the potential value of testing alternative follow-up criteria and their impact on patient outcomes. An example in which the encounter-data coding system was used directly without further refinement is an analysis of patterns of broken appointments in the Johns Hopkins dinic. Records were analyzed for all dinic patients seen during April-June 1975 who received followHEALTH up appointments and separately for all diagnosed as hypertensive RESEARCH during April-August 1975. A marked difference was found between the hypertensives and the others, most of whom had other chronic 46 conditions: among follow-up appointments for 2,150 hypertensive
patients, 33 percent were missed, whereas 46 percent of appointments THE JHACS were missed among the other patients. The encounter-data system allowed ready analysis of these data by patient age, sex, and other characteristics as well as by diagnosis; the differences between hypertensive and nonhypertensive patients are being further examined to gain insight into the relation between selected measures of accessibility and continuity of care and their effect on patient outcomes. Since the purpose of coding is to collapse and organize information, there is always some sacrifice of specificity. Even though the coding of otitis media was further refined as in the HICDA, the specificity may still be inadequate for some applications. Having access to the original data when necessary is a major advantage of retaining computer files with the provider's exact wording linked to a code. The ability to recall the original data has been used repeatedly in clinical and quality-assurance applications and has facilitated periodic reviews of the reliability and accuracy of the coding process. Patterns of Practice Within the framework of care patterns one can examine patterns of provider practice relative to a specific condition. For example, patterns of requested follow-up at an initial encounter for otitis media can be compared by type of provider seen at the visit and by patient characteristics such as age, sex, and number of prior episodes in the year. Such tabulations can reveal significant deviations and permit closely targeted inquiries into the cost and quality implications of the deviations. Similarly, one can ascertain distributions of providers in regard to prescribing patterns, use of laboratory and radiology services, numbers of patient visits per day, procedures ordered per visit, referral rates, and the like. Increasing concern about cost and productivity in health services has emphasized the importance of developing new analytic pIethods for comparing providers who see different patients.
Case-specific Applications Few problems have arisen in the use of the coded data to identify and select cases representing specific problem/diagnosis categories; the few that have appeared have been resolved through recourse to the medical chart, the patient, or the provider. The need to identify patients having selected conditions may arise from either management or clinical problems; these have included selection of hypertensive patients for medical audit, selection of chronically ill patients for immunization, and identification of patients having specific conditions who have used services with great frequency. The selection of patients with specific chronic conditions for immunization would not have been feasible without routine capture and coding of problem data. However, comparison of the sensitivity and specificity of the coded data for one condition (juvenile diabetes) with SPRING 1978 that of a manually maintained file illustrated an important limitation of the encounter-data system: the latter identified all the juvenile
47
STEINWACHS &
MUSHLIN
diabetics (100-percent sensitivity) but also selected a few children who were not diabetic. The primary reason for this specificity problem was the use of diabetes as a tentative diagnosis (tentative diagnoses are routinely recorded at initial visits for acute conditions such as urinary tract infections and streptococcal throat infections). Possible solutions to this problem, such as requiring later repetition before a diagnosis of a chronic disease is taken as definite, or adding a new item to the encounter form to designate diagnoses as tentative or definitive, are being examined.
Discussion The JHACS is a comprehensive and specific coding system for problems seen in ambulatory care. The cost of coding and operation is reasonable when the system is incorporated into an ongoing information system for research, clinical, and management uses. Our experience indicates that computerized coding and the retention of the provider's complete statement make the JHACS readily adaptable to meet new and existing requirements for problem-specific data. The JHACS provides a high degree of specificity across all categories of coded data; frequent recourse to the provider's original statement (in computer-readable form) permits even more detailed analyses, such as distinguishing tentative diagnoses and diagnoses of chronic and recurrent conditions. Although the coding scheme could be refined to make such distinctions explicit, our experience suggests that the potential advantages are outweighed by increased complexity. The use and interpretation of the data frequently require the linkage of related data describing the content and source of care. For example, data on visit purpose, appointment status, medications, requests for follow-up care, and use of tests provide important insights into the stage of care, the provider's certainty of diagnosis, and patterns of resource use. New types of information (such as the provider's assessment of the patient's timeliness in seeking care [10?) should also be considered for inclusion. Linking diverse data sources to the coded problem data through an information system limits the need to modify the coding scheme to incorporate new types of data on problem characteristics; such an approach enhances the usefulness of the problem data for a broad range of applications. The coding categories will be reevaluated when the ninth revision of the ICDA and the third edition of the HICDA become available. If it proves desirable to modify the coding scheme because of these revisions, the process will be greatly facilitated by the automated coding process and the availability of the text of providers' statements. Not only can the codes and categories be readily altered in the computer programs, but, if necessary, historical data can be recoded to match new categorizations. HEALTH The example applications that have been discussed have clear imfor quality assurance programs. Such questions as the explications REERVIRCES tent to which the population utilizes medical service, the conditions 48 prevalent in the population, and the content of care received can be
addressed by means of the routine information system supplemented THE JHACS by the medical chart, appointment logs, and the like. The system does, of course, have limits: outcomes of care, for example, cannot be derived from these sources. However, the system can facilitate research by allowing selection of patient samples by age, sex, history of chronic illness, or recent visits for selected conditions. Quality assessment studies of such issues as access and outcome can be highly targeted using routine problem-data information systems, and such possibilities are sufficiently promising to warrant continued research and development [13,14]. Acknowledgment. The authors acknowledge the invaluable contributions of Daniel Barr, Faith Koch, John Stiney, and Arlene Baker in the development of the Johns Hopkins Ambulatory-care Coding Scheme.
REFERENCES 1. Murnaghan, J.H. (ed.). Ambulatory care data: Report on the conference on ambulatory medical care records. Med Care Vol. 11, No. 2 (suppl) Feb. 1973. 2. National Center for Health Services Research and Development. Guideline for Producing Uniform Data for Health Care Plans. DHEW Pub. No. (HSM) 7303005. Washington, DC: U.S. Government Printing Office, July 1972. 3. International Classification of Health Problems in Primary Care. Chicago: American Hospital Association, 1975. 4. Eighth Revision International Classification of Diseases, Adapted for Use in the United States, Vol. 2. PHS Pub. No. 1693. Washington, DC: U.S. Government
Printing Office, 1968. 5. Hospital Adaptation of ICDA, 2nd ed. Ann Arbor, MI: Commission on Professional and Hospital Activities, 1973. 6. American Psychiatric Association Committee on Nomenclature and Statistics. Diagnostic and Statistical Manual of Mental Disorders, 3rd ed. Washington, DC: American Psychiatric Association, 1970. 7. Hurtado, A.V. and M.R. Greenlick. A disease dassification system for analysis of medical care utilization, with a note on symptom classification. Health Serv Res 6:235 Fall 1971. 8. Renner, J.H. and R.W. Piernot. A revised symptom code list for ambulatory medical record data. Working paper, Family Practice Program, University of
Wisconsin at Madison, 1972. 9. National Center for Health Statistics. The National Ambulatory Medical Care Survey: Symptom Classification. DHEW Pub. No. (HRA) 74-1337. Washington, DC: US. Government Printing Office, May 1974. 10. Schneider, D.P. Proposed revisions to the National Ambulatory Symptom Coding Scheme. Working paper, American Medical Records Association, Chicago, 1976. 11. Steinwachs, D.M., S. Shapiro, R. Yaffe, D.M. Levine, and H. Seidel. The role of new health practitioners in a prepaid group practice: Changes in the distribution of ambulatory care between physicians and non-physician providers of care. Med Care 14:95 Feb. 1976. 12. Steinwachs, D.M. and R. Yaffe. Assessing the timeliness of ambulatory medical care. Am J Public Health June 1978 (in press). 13. Howell, J.R., M. Osterweis, and R.R. Huntley. Curing and caring-a proposed method for self assessment in primary care organization. J Community Health 1:256 Summer 1976. 14. Kessner, D.M. and C.E. Kalk. Contrasts in Health Status, Vol. 2: A Strategy for Evaluating Health Services. Washington, DC: National Academy of Sciences, Institute of Medicine, 1973. SPRING
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49
The importance of ambulatory care in the prevention, diagnosis, and treatment of disease is generally recognized, but the content and quality of ambulatory care have not been thoroughly studied. The need for information on such matters led to recommendations for a uniform minimal data set for ambulatory care [1,2] that could be incorporated into existing routine information systems to serve the needs of managers, dinicians, and health services researchers. The potential of uniform data bases for enhancing our understanding of patterns of patient care, its quality, its cost, and the influence of organizational, financial, and manpower characteristics on cost and quality has been frequently cited. However, a variety of methodological problems require further resolution if the cited potential is to be fully realized. One of the more complex methodological problems is the classification of problem/diagnostic information generated in the patient-provider encounter. Since 1970, the Health Services Research and Development Center of the Johns Hopkins Medical Institutions has been involved in developing routine encounter-based information systems incorporating the recommended minimal data set. The center has worked closely with two prepaid group practice programs-the Columbia Medical Plan and the East Baltimore Medical Plan-and two hospital outpatient
HEALTH SERVICES
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3t 3U
This research was supported in part by grant no. HS 00429 from the National Center for Health Services Research, DHEW. Address communications and requests for reprints to Donald M. Steinwachs, Research Manager, Health Services Research and Development Center, Johns Hopkins Medical Institutions, 624 North Broadway, Baltimore, MD 21205. Alvin 1. Mushlin is now associate professor of medicine at the University of Rochester.
0017-9124/78/1301-0036/$01.40/0 0 1978 Hospital Research and Educational Trust
departments-the Johns Hopkins Hospital Medical Clinic and the THE JHACS Baltimore City Hospital Housestaff Clinic. In each of these settings, an encounter form is completed by the provider for each patient visit. The encounter form identifies the patient, provider, date of service, appointment status, purpose of visit, laboratory and radiology procedures done, disposition, and the provider's written statement of the patient's condition, i.e., problem or diagnosis. All of this information except the written statement is precoded on the form and requires only check-marking. (The patient's statement of the reason for the visit is not part of the routine system but has been collected in special studies.) The encounter data are linked with registration information, which includes such items as date of birth, sex, source of insurance, and a unique patient identifier or medical history number for each patient. In the prepaid group practices (which also treat fee-for-service patients) the registration data also identify the enrollee population. The registration and encounter data are integrated into a historical utilization file with discharge and prescription data obtained from other sources. In all four sites the provider's written statement specifying the patient's conditions evaluated, diagnosed, or treated during the visit is keypunched, and the complete text is retained in the computer files. This artide describes the coding scheme developed to dassify the problem/diagnosis data and illustrates the evaluative applications that the coding scheme allows.
Development of the Johns Hopkins Ambulatory-care Coding Scheme (JHACS) The development of the JHACS, begun in 1973, had four fundamental phases: (1) analysis of data from the four ambulatory care sites to determine what conditions were presented and establish the content of the coding scheme; (2) examination of existing coding schemes to determine their usefulness in dassifying ambulatory care data; (3) identification of uses desired for the coded data; and (4) evaluation of the coding system by testing its effectiveness in selected applications. Data Collection Four alternative ways of collecting problem data were judged on accuracy of data and recording method, content of the coding scheme (including specificity, number of categories, and comprehensiveness), and the extent to which the coded data could meet the intended uses. The four alternative methods we considered were using a checklist of problem categories, having the provider code patient conditions, abstracting the medical notes, and having the provider record the patient's problems on the encounter form in his own words. We chose the last of these methods, primarily on considerations of specificity, completeness, and reliability. Checklists can facilitate data collection, but they sacrifice specificity to the need for preconceived categories. Provider coding of problem data, as used in the International Classification of Health Problems in Primary Care (ICHPPC) [3], places both work load and responsibility for accuracy on the provider,
SPRING
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37
STEINWACHS & MUSHLIN
which may not be acceptable to providers and may limit the reliability of the process. Abstracting medical notes places the burden on clerical staff, but without clear and consistent problem-oriented records uniform identification of problems cannot be assured. Provider recording of the patient's problems in his own words gives the highest level of specificity since the complete text is retained in the computer files: it is easy to complete, and illegibility is a problem in only a small percentage of cases. The desired uses of the coded data also influenced the content and structure of the coding scheme. Although the data were intended primarily for use in research and evaluation, we considered management and clinical uses, e.g., accounting, billing, and selection of patients for special review or follow-up. Potential uses of the data were categorized as statistical and case-identification applications; statistical applications were categorized further as case-mix studies, incidence and prevalence calculations, and studies of patterns of patient care and provider practice. The utility, for these applications, of data organized by the final version of the coding scheme will be discussed in a later section.
Review of Existing Coding Schemes Our examination of problem data revealed that providers re-
HEALTH SERVICES RESEARCH
38
corded information in four general categories: signs and symptoms not resolved into diagnoses, which accounted for 10 to 20 percent of recorded problems, depending on the patient population and the provider's specialty; preventive and well care (including physical examinations and routine well-child visits), which varied from 2 to 23 percent; surgical and nonsurgical procedures (excluding routine laboratory and radiology procedures), which accounted for less than 1 percent; and traditional diagnoses specifying a disease or injury, which, along with a small percentage of uncodable entries, made up the remainder. We systematically reviewed the specificity and comprehensiveness of several coding schemes as applied to data in these four general categories. The schemes reviewed included the International Classification of Diseases, Adapted (ICDA) [4], the Hospital Adaptation of ICDA (HICDA) [5], the Diagnostic and Statistical Manual of Mental Disorders (DSMMD) [6], the Kaiser symptom-classification system [7], Renner and Piernot's adaptation of the Kaiser scheme [8], and later, when they became available, the ICHPPC [3], the National Ambulatory Medical Care Survey (NAMCS) symptom-classification scheme [9], and the recently suggested revision of the last [10]. Most of these were not designed to code provider statements of problems diagnosed, evaluated, or treated in an ambulatory visit. The ICDA and HICDA are pnmarily directed at mortality dassifications and common morbidities treated on an inpatient basis. The RennerPiernot and Kaiser coding schemes were developed to classify presenting symptoms and do not incorporate diagnoses or well care. The NAMCS scheme was designed to classify the patient's primary reason
for the visit, as stated by the patient. Experience has shown that patients respond in terms of diagnoses and procedures, as well as signs and symptoms. Thus a coding scheme applicable to the patient's reason for the visit will overlap a scheme designed for the provider's statement of the problem. However, even if both items were to be coded within the same scheme, they are substantially different pieces of information representing distinct perspectives on the use of services. (The suggested revisions of the NAMCS scheme [10] considerably extend its capability to code diagnoses and procedures, which enhances its utility for coding provider-stated problems.) Only the ICHPPC was specifically designed to meet the need of practitioners to code ambulatory problems, with a minimal number of categories and consistency with the ICDA. None of these systems was useful for the entire spectrum of applications that was anticipated. Although some of the existing schemes were adequate for coding a single category of data, none was adequate for all categories of problems/diagnoses. For example, Table 1 shows a summary of our analysis of seven existing schemes as applied to musculoskeletal problems that appeared in our collection of problem data. For 37 musculoskeletal diagnoses, the ICDA offered codes adequate for all; the HICDA offered codes similarly adequate for 81 percent of these diagnoses and offered even more specificity for 8 percent of them but less specificity for 11 percent. On the basis of such comparisons, we selected sections from the various coding schemes that were the most specific for problems that occurred with sufficient frequency in our data; the result was an abridgment and integration of those schemes specific to the needs of ambulatory care. For diagnoses (except for mental disorders) we selected the ICDA instead of the HICDA because it is more widely used, not because of any clear advantage in specificity. The DSMMD, which is compatible with but more extensive than the mental disorders section of the
THE JHACS
Table 1. Percentage of Musculoskeletal Problems Seen in Ambulatory Care that are Coded with Adequate Specificity (A), More Specificity (M), and Less Specificity (L) by Seven Classification Schemes Problem category
Diagnoses (N = 37)
Injuries (N = 40)
Symptoms (N = 28)
Procedures,
M, %A,% L,%
M, %A,% L,%
M,% A,% L,%
M, % A,% L,%
Classification scheme
ICDA .......... HICDA ........ Kaiser .........
100* 8 81 11 Not included Not included Not included .. ... 100 8 92
100*
(Nw = 3)
... 100 100 82 ... 100*... . 25 Not included Renner-Piemot . ... 100* Not induded NAMCS ....... ... ... 100 100 NAMCS, revised 4 96 ... 100 ... ICHPPC ....... 100 100 * Indicates scheme from which codes were taken for the musculoskeletal section of the JHACS. ...
...
...
...
...
3 90 8 Not induded Not included ... ... 100 13 23 65 20 80 ...
...
...
...
...
18 75
...
...
...
STEINWACHS & MUSHLIN
Table 2. Code Sources for Major Problem Categories and Sections of the Johns Hopkins Ambulatory-care Coding Scheme Section
Category Infective and parasitic
Neoplasms
Diagnosis Symptom Well care Procedure ..................
.............................
Endocrine, nutritional, and metabolic Blood and blood-forming organs ........
...
Mental disorders ....................... Nervous system and sense organs ........
Circulatory ............................ Respiratory ............................ Digestive .............................. Genitourinary ......................... Pregnancy and childbirth .............. Skin and subcutaneous tissue ............ Musculoskeletal and connective tissue Congenital abnormalities ............... Perinatal morbidity .................... Nature of injury ....................... Extemal cause of injury ............... ....
Nonspecific
ICDA ICDA ICDA ICDA DSMMD ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA ICDA
... ... ...
HICDA
R-P R-P R-P R-P R-P R-P R-P R-P R-P R-P ... ... ...
... HICDA
...
...
...
... ...
... ... ... HICDA HICDA ...
... ...
HICDA HICDA ...
HICDA HICDA HICDA HICDA HICDA HICDA HICDA HICDA
...
...
...
...
...
...
...
...
... R-P ............. General medical examination for ... HICDA ... administrative purposes .............. ..... HICDA Other general medical examination . ... Special examinations and investigations .. ... HICDA HICDA Medical and surgical aftercare ............. ... HICDA Nonmedical reason for visit ............. ... Other personal history affecting health ... HICDA Abnormal findings in laboratory and examinations .................... ... HICDA HICDA Prophylactic vaccinations and inoculations ... Family planning ....................... ... ... HICDA Referral and evaluations ............... ... ... HICDA Diagnostic and nonsurgical procedures ... ... * Renner and Piernot's adaptation [8] of the Kaiser classification scheme ...
... ... ... ... ... ...
... ... ... ...
HICDA [7].
ICDA, was the source of diagnostic codes for mental disorders. We chose Renner and Piernot's scheme for the coding of symptoms because it is more specific than any other. For well care and procedures the choice was the HICDA because of its greater specificity. Table 2 shows the four major sections of the JHACS by problem category, with the source of the code for each problem in the relevant section or sections.
Structure of the JHACS Within each of four major sections of the JHACS, up to three levels of aggregation are represented by system categories, subsystems, and the coded entity. Typically a system category represents a physiological system (e.g., digestive) and a subsystem represents a major disHEALTH ease site within the system. The coded entity is the specific disease or SERVICES within the subsystem and system. RESEARCH injury The diagnosis section of the JHACS is divided into 17 organ and disease systems paralleling categories in the ICDA, as shown in Table
40
2; the symptoms section follows the format of the Renner-Piernot THE JHACS coding scheme, with symptoms subdivided into 10 organ systems and a nonspecific category. The well care and the operations and therapeutic procedures sections follow the HICDA format, with 14 categories specifying the type of well care, including medical and surgical aftercare, and operations and nonsurgical procedures divided into 11 organ-specific categories. The HICDA is more comprehensive and specific for well care than the ICDA, including a section on abnormal laboratory and examination findings for which the ICDA has only limited provisions. Also, operations and therapeutic procedures are arranged by anatomic location in the HICDA, instead of by surgical specialty as in the ICDA. In general, the codes used in the JHACS were taken from whatever system offered the most specificity in a given problem category. Not all of the available codes in a given source scheme were needed for a given problem category, however, because of the difference in content between ambulatory and inpatient care. Within the diagnosis section of the musculoskeletal category, for example, the ICDA offers codes for 199 major terms, but only 77 of these codes were needed for the JHACS. For the symptom section of the same category the Renner-Piernot coding scheme offers 120 coded terms; only 28 were needed for the JHACS. The exclusions resulted from our frequency criterion: to be included, a term had to occur at least four times in a year's data. This rule was systematically applied to data from the Columbia Medical Plan to build the initial dictionary for ambulatory care problems and then to the three other sites to determine what new terms and codes were needed. Currently there are 984 distinct codes in the dictionary. The value of the frequency criterion in limiting the growth of the dictionary was confirmed by examination of the uncoded items generated during 1975 by the Columbia Medical Plan: if these were to be added to the dictionary, the additions would increase the number of distinct codes by 50 percent but add only 1.4 percent to the percentage of all conditions coded. Code Format. The code formats follow the formats of the parent schemes. To avoid complications of duplicate codes and overlapping numbering systems, a letter precedes the numeric portion of each code to designate the system category into which the condition falls. Two supplementary categories are used for uncodable entries and for conditions that ar6 uncoded because of infrequent occurrence. The advantage of this method of assigning codes is that new ones can be easily added from the appropriate source as needed.
Computerized Coding An automated processor for diagnostic data was developed, beginning in 1973, on the basis of previous experience in the Comprehensive Child Care Center at Johns Hopkins. This program had implemented a computerized scheme for coding pediatric diagnoses 1978 abstracted from the medical note. Significant extensions of that methodology were necessary to handle a much larger dictionary and to 41
STEINWACHS obtain maximal system effectiveness. These extensions induded a & MUSHLIN computerized process to skip irrevelant prefixes and to identify selected root words that were sufficiently specific to indicate the appropriate code. The computerized version of the dictionary includes 5,064 synonyms, 2,000 of which are common abbreviations or misspellings that permit the computer to match variations in terminology and spelling and to link these with the appropriate code. Four steps are involved in the automated processing and coding operation: 1. The complete text of the provider's statement of the patient's condition is compared with the terms and synonyms of the dictionary, and exact matches are coded. 2. Entries still uncoded are compared with dictionary terms that have been specifically designated as "general." These terms require only that the initial wording match the general term; all subsequent terminology is ignored. For instance, "muscle spasm" is designated a general term and is used to code musde spasm left leg, muscle spasm while swimming, muscle spasm recurrent in right forearm, and the like. 3. A list of selected prefixes, such as "recent" and "recurrent," that can be ignored in coding is compared with all uncoded entries. When such prefixes are identified in an entry, they are ignored and the first two steps of the coding process are repeated for whatever phrase they precede. 4. Any uncoded entries that remain are sequenced alphabetically and printed out for manual coding. This step indudes a frequency count to identify terms and synonyms that repeat and that should be considered for inclusion in the dictionary. In the manual process, all remaining uncoded statements are coded or assigned to the categories for uncodable entries and those that are potentially codable but too infrequent, and entered into the computer file. The effectiveness of automated coding, in terms of the percentage of entries that are machine-codable, varies by site and appears to be sensitive to staff characteristics (size and turnover). At the two prepaid group practice dinics, the automated coding process links 85 percent of the diagnostic entries to appropriate codes; at the Johns Hopkins Medical Clinic, only 70 percent of the entries are machinecodable because of the large number of different providers and the frequent turnover of housestaff. At the Baltimore City Hospital dinic there are fewer physicians, and diagnostic information receives routine derical review before keypunching; as a result, 90 percent of entries are automatically coded. Another measure of the effectiveness of this computerized methHEALTH odology is the relative frequency of uncodable entries. In 1975, the RESEARCH two group practice clinics and the Baltimore City Hospital dinic found from 2.5 percent to 3.1 percent of all problem entries uncod42 able; at Johns Hopkins 12.5 percent were uncodable; this percentage
included illegible entries and entries too nonspecific to be coded. (Un- THE JHACS coded-as opposed to uncodable-entries at all four sites ranged from 1.4 percent to 2.7 percent; these are periodically reviewed and added to the dictionary if justified by frequency of occurrence or when a specific research or clinical need develops.) Operating Cost The JHACS is currently operational in the four sites mentioned, which together experience more than 150,000 ambulatory visits per year. The programs are all written in COBOL for IBM 370-145/ 135 computer systems. Encounter and registration data are typed in weekly from keyboard to tape, in batch mode; diagnostic data from the accumulated utilization file are copied monthly onto a separate file for coding. Both the provider's statement of the patient's problem and the JHACS code are retained as part of the utilization history of each patient. Three categories of costs are incurred with this approach to the capture and coding of diagnostic data. Data-entry costs for key-to-tape transfer of written diagnoses average approximately $.02 per entry, and running the computer coding program costs $5 plus $2 per 1,000 entries processed. Manual coding of entries not recognizable to the computer dictionary can be done at the rate of 300-400 per day by a trained clerk, so that the total of these costs for 100,000 diagnostic entries, assuming 85 percent are machine-codable, averages $4,200. Additional expenses arise for dictionary maintenance, which includes addition of new synonyms and periodic tabulations to monitor the qu.rlity of both computerized and manual components of the coding system.
Applications: Utility of the System Some example applications of the coding system as developed for medical care evaluation and research will provide insight into its utility and into some unresolved issues of the coding and use of ambulatory-care problem data. Our statistical applications of coded data have addressed questions of manpower, organization of service, and quality of care, using four basic types of data groupings with modifications depending on the particular question; these four data groupings are case-mix distributions, linkage of diagnoses and population characteristics, statistical summaries of care provided to patients with selected characteristics (e.g., diagnoses, age, sex, and enrollment duration), and statistical summaries of care given by a selected provider or group of providers. Case-mix Applications The distribution of patients or patient visits by types of conditions presented is influenced by organizational configurations, available manpower, and population characteristics; in turn, case mix 1978NG influences the overall use of resources. Thus it relates to productivity differences among organizations and providers. 43
STEINWACHS & MUSHLIN
Case-mix data were used to examine the effect of adding more health associates (comparable in function to nurse practitioners) in the departments of pediatrics and medicine of the Columbia Medical Plan [11] in terms of changes in the distributions of care provided by physicians and health associates. In 1971 the department staff consisted of three full-time equivalent (FTE) physicians and two health associates; in 1974 there were 3.2 FTE physicians and 5.2 FTE health associates. The volume of visits served indicated a shift in physician work load from ambulatory care to inpatient care. Table 3 shows the changing distribution of care provided by the health associates for selected conditions over the three-year period: case mix changed to include a higher proportion of chronically ill patients, and more physician time was spent in consultation with the health associates. Calculation of the numbers of physicians that would have been required to serve the 1974 volume of visits in the absence of the health associates suggested that a substitution rate of 2.5 health associates per fulltime physician had been achieved. Although case-mix distributions can provide insight into practice content and temporal changes in the roles and functions of providers, such data have limited utility for management and program planning because of the lack of a one-to-one relation between resources (visits, laboratory procedures, etc.) and diagnoses. Some visits limited to a specific procedure include no diagnostic information, and some, particularly from chronically ill patients, have multiple diagnoses. When no diagnosis or problem is recorded, other data items on the encounter form permit categorization of the visit as related to illness, injury, well care, etc.; but when multiple conditions are diagnosed, evaluated, or treated in a visit, other complexities are introduced. Classifying visits by primary problem imposes the restrictive assumption that
Table 3. Percent of Patient Visits in Selected Categories Served by Health Associates Alone and with Physician in Three Periods Visit category
Allt
HEALTH SERVICES
RESEARCH
44
.................
Period 2* Period 1* With MD, Alone, With MD, Alone, 5
0*
11
4
Period 3* Alone, With MD,
30
10
4 1 5 3 0 Diabetes mellitus . .......0 3 1 11 3 0 Hypertension ......... 0 3 56 13 3 43 35 Pharyngitis .35... 9 10 0 5 10 0 Asthma ... 31 4 6 0 28 .0..0 Bronchitis 9 4 32 0* 2 General physical exam . 0t 4 18 19 0* 5 Headache .1... 4 0 2 27 0 0 21 Obedty .............. 7 1 1 13 0 Back pain .0..0 * Period 1 was from July 1971 through June 1972; period 2 was from July 1972 through June 1973; period 3 was from July 1973 through June 1974. t Indudes all visits, not only those specified. * Less than 0.5 percent.
other conditions have little effect on the total resources used. Another THE JHACS approach is to collect data in a problem-oriented format that relates the use of ancillary services and medications to specific conditions; however, it is not uncommon for treatments or procedures to relate to more than one problem. For these reasons, and because of the complexities of form design, little progress has been made in relating case-mix data to resource use, although the potential is worthy of exploration. and Prevalence Applications The system allows estimation of the incidence and prevalence of specific conditions within a defined population. Estimates of the extent of undiagnosed and untreated cases can be derived by comparing prevalence levels of diagnoses with epidemiologic data on the expected prevalence, thus identifying needs for increased efforts directed at detection and treatment. Annual and more frequent estimates of the incidence of new cases of acute conditions and the prevalence of chronic conditions aid in planning future programs to meet the needs of a changing population or the effects of seasonal variations in disease incidence. For example, Table 4 shows a significant increase, over the years 1971 to 1975, in the prevalence of diagnosed hypertension among those who had been enrolled in the Columbia Medical Plan for at least one year. The increase results from changes in the population as well as from diagnosis of new cases among those with relatively long periods of enrollment, but the magnitude of the change may reflect increasing concern among providers and patients about detection of Incidence
Table 4. Chang Patterns of Diagnosis and Treatment of Hypertension Among Columbia Medical Plan Enrollees, 1971-1975* Enrolleet
age and sex
Enrollees diagnosed and given prescription, % FY 1971 FY 1972* FY 1973* FY 1974* (N = (N = (N= (N =
4494)
6140)
1532)
917)
Hypertensives given new prescription or refill, % FY 1971 FY 1972* FY 1973* FY 1974* (N = (N= (N = (N= 83) 156) 228) 312)
17 and older, total 1.8 2.5 3.0 3.4 80 82 Male ........... 1.7 2.8 3.1 84 3.2 89 1.9 Female ......... 3.0 2.3 76 74 3.6 1.0 1.4 0.8 17A44, total ....... 1.3 67 75 Male ........... 1.1 1.4 0.7 77 1.5 89 Female ......... 0.9 1.0 1.3 59 1.3 60 5.6 8.1 8.6 45-64, total ....... 86 9.5 87 9.1 5.5 Male ........... 8.7 8.6 90 91 10.4 Female ......... 7.1 5.8 8.4 84 78 65 and older, total . 13.8 19.5 22.4 86 25. 87 24A Male ........... 16.7 27.4 83 25.9 83 Female ......... 12.3 17.0 25.4 89 20.1 88 Data shown by fiscal years beginning July 1 and ending June 30. t Only those enrolled for the entire year are included in each year's data.
80 83 78
72 73 71 83 91 76 89 82 93
82 80 84 74 72 76 86 88 85 85
73 91
STEINWACHS
hypertension. During each of the four years, approximately 80 per-
& MUSHLIN
cent of diagnosed hypertensives received one or more prescriptions for hypertension. This information suggests that a high proportion of those diagnosed receive care, but it does not indicate the content or outcomes of care. Medical chart review would give insight on this question and allow comparison of severity and degree of control of hypertension for those diagnosed soon after joining the plan and those diagnosed later in their enrollment. Other tabulations have raised questions concerning the use of a single diagnostic entry to identify chronically ill persons in the absence of corroborating data on treatment or subsequent visits for the condition. Some patients found to have high blood pressure may never be ultimately diagnosed as hypertensive; they may be considered borderline or labile and not receive therapy. How such cases enter an analysis of incidence and prevalence depends on linkages to other data, e.g., the medical chart. Patterns of incidence for acute episodic conditions are derived by identifying episodes of care for a specific condition, rather than from individual visits. Single episodes may result in multiple visits, each with the same problem statement repeated. By linking visits into discrete episodes of care, it has been possible to determine the extent of seasonal variation in incidence and the portion of incidence attributable to repeated episodes over a defined period.
Pattemrs of Care Defining episodes also provides a framework for examining the content of care in terms of the use of resources and quality of care provided. Our focus has been on identifying questions for research on the delivery of health services. Otitis media provides an example of an acute episodic condition in which the coding system represented an important starting point but additional investment was necessary to gain adequate specificity. The role of follow-up care in preventing hearing impairment and repeated episodes of otitis is not totally dear, and follow-up care for a condition as common as otitis has significant implications for the allocation of resources and the cost of care. All episodes of otitis media were categorized as purulent, serous, or mixed; preliminary analyses indicated that, in the aggregate, follow-up care had become less frequent for serous episodes and more frequent for purulent episodes of otitis. This focused attention on the potential value of testing alternative follow-up criteria and their impact on patient outcomes. An example in which the encounter-data coding system was used directly without further refinement is an analysis of patterns of broken appointments in the Johns Hopkins dinic. Records were analyzed for all dinic patients seen during April-June 1975 who received followHEALTH up appointments and separately for all diagnosed as hypertensive RESEARCH during April-August 1975. A marked difference was found between the hypertensives and the others, most of whom had other chronic 46 conditions: among follow-up appointments for 2,150 hypertensive
patients, 33 percent were missed, whereas 46 percent of appointments THE JHACS were missed among the other patients. The encounter-data system allowed ready analysis of these data by patient age, sex, and other characteristics as well as by diagnosis; the differences between hypertensive and nonhypertensive patients are being further examined to gain insight into the relation between selected measures of accessibility and continuity of care and their effect on patient outcomes. Since the purpose of coding is to collapse and organize information, there is always some sacrifice of specificity. Even though the coding of otitis media was further refined as in the HICDA, the specificity may still be inadequate for some applications. Having access to the original data when necessary is a major advantage of retaining computer files with the provider's exact wording linked to a code. The ability to recall the original data has been used repeatedly in clinical and quality-assurance applications and has facilitated periodic reviews of the reliability and accuracy of the coding process. Patterns of Practice Within the framework of care patterns one can examine patterns of provider practice relative to a specific condition. For example, patterns of requested follow-up at an initial encounter for otitis media can be compared by type of provider seen at the visit and by patient characteristics such as age, sex, and number of prior episodes in the year. Such tabulations can reveal significant deviations and permit closely targeted inquiries into the cost and quality implications of the deviations. Similarly, one can ascertain distributions of providers in regard to prescribing patterns, use of laboratory and radiology services, numbers of patient visits per day, procedures ordered per visit, referral rates, and the like. Increasing concern about cost and productivity in health services has emphasized the importance of developing new analytic pIethods for comparing providers who see different patients.
Case-specific Applications Few problems have arisen in the use of the coded data to identify and select cases representing specific problem/diagnosis categories; the few that have appeared have been resolved through recourse to the medical chart, the patient, or the provider. The need to identify patients having selected conditions may arise from either management or clinical problems; these have included selection of hypertensive patients for medical audit, selection of chronically ill patients for immunization, and identification of patients having specific conditions who have used services with great frequency. The selection of patients with specific chronic conditions for immunization would not have been feasible without routine capture and coding of problem data. However, comparison of the sensitivity and specificity of the coded data for one condition (juvenile diabetes) with SPRING 1978 that of a manually maintained file illustrated an important limitation of the encounter-data system: the latter identified all the juvenile
47
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diabetics (100-percent sensitivity) but also selected a few children who were not diabetic. The primary reason for this specificity problem was the use of diabetes as a tentative diagnosis (tentative diagnoses are routinely recorded at initial visits for acute conditions such as urinary tract infections and streptococcal throat infections). Possible solutions to this problem, such as requiring later repetition before a diagnosis of a chronic disease is taken as definite, or adding a new item to the encounter form to designate diagnoses as tentative or definitive, are being examined.
Discussion The JHACS is a comprehensive and specific coding system for problems seen in ambulatory care. The cost of coding and operation is reasonable when the system is incorporated into an ongoing information system for research, clinical, and management uses. Our experience indicates that computerized coding and the retention of the provider's complete statement make the JHACS readily adaptable to meet new and existing requirements for problem-specific data. The JHACS provides a high degree of specificity across all categories of coded data; frequent recourse to the provider's original statement (in computer-readable form) permits even more detailed analyses, such as distinguishing tentative diagnoses and diagnoses of chronic and recurrent conditions. Although the coding scheme could be refined to make such distinctions explicit, our experience suggests that the potential advantages are outweighed by increased complexity. The use and interpretation of the data frequently require the linkage of related data describing the content and source of care. For example, data on visit purpose, appointment status, medications, requests for follow-up care, and use of tests provide important insights into the stage of care, the provider's certainty of diagnosis, and patterns of resource use. New types of information (such as the provider's assessment of the patient's timeliness in seeking care [10?) should also be considered for inclusion. Linking diverse data sources to the coded problem data through an information system limits the need to modify the coding scheme to incorporate new types of data on problem characteristics; such an approach enhances the usefulness of the problem data for a broad range of applications. The coding categories will be reevaluated when the ninth revision of the ICDA and the third edition of the HICDA become available. If it proves desirable to modify the coding scheme because of these revisions, the process will be greatly facilitated by the automated coding process and the availability of the text of providers' statements. Not only can the codes and categories be readily altered in the computer programs, but, if necessary, historical data can be recoded to match new categorizations. HEALTH The example applications that have been discussed have clear imfor quality assurance programs. Such questions as the explications REERVIRCES tent to which the population utilizes medical service, the conditions 48 prevalent in the population, and the content of care received can be
addressed by means of the routine information system supplemented THE JHACS by the medical chart, appointment logs, and the like. The system does, of course, have limits: outcomes of care, for example, cannot be derived from these sources. However, the system can facilitate research by allowing selection of patient samples by age, sex, history of chronic illness, or recent visits for selected conditions. Quality assessment studies of such issues as access and outcome can be highly targeted using routine problem-data information systems, and such possibilities are sufficiently promising to warrant continued research and development [13,14]. Acknowledgment. The authors acknowledge the invaluable contributions of Daniel Barr, Faith Koch, John Stiney, and Arlene Baker in the development of the Johns Hopkins Ambulatory-care Coding Scheme.
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Printing Office, 1968. 5. Hospital Adaptation of ICDA, 2nd ed. Ann Arbor, MI: Commission on Professional and Hospital Activities, 1973. 6. American Psychiatric Association Committee on Nomenclature and Statistics. Diagnostic and Statistical Manual of Mental Disorders, 3rd ed. Washington, DC: American Psychiatric Association, 1970. 7. Hurtado, A.V. and M.R. Greenlick. A disease dassification system for analysis of medical care utilization, with a note on symptom classification. Health Serv Res 6:235 Fall 1971. 8. Renner, J.H. and R.W. Piernot. A revised symptom code list for ambulatory medical record data. Working paper, Family Practice Program, University of
Wisconsin at Madison, 1972. 9. National Center for Health Statistics. The National Ambulatory Medical Care Survey: Symptom Classification. DHEW Pub. No. (HRA) 74-1337. Washington, DC: US. Government Printing Office, May 1974. 10. Schneider, D.P. Proposed revisions to the National Ambulatory Symptom Coding Scheme. Working paper, American Medical Records Association, Chicago, 1976. 11. Steinwachs, D.M., S. Shapiro, R. Yaffe, D.M. Levine, and H. Seidel. The role of new health practitioners in a prepaid group practice: Changes in the distribution of ambulatory care between physicians and non-physician providers of care. Med Care 14:95 Feb. 1976. 12. Steinwachs, D.M. and R. Yaffe. Assessing the timeliness of ambulatory medical care. Am J Public Health June 1978 (in press). 13. Howell, J.R., M. Osterweis, and R.R. Huntley. Curing and caring-a proposed method for self assessment in primary care organization. J Community Health 1:256 Summer 1976. 14. Kessner, D.M. and C.E. Kalk. Contrasts in Health Status, Vol. 2: A Strategy for Evaluating Health Services. Washington, DC: National Academy of Sciences, Institute of Medicine, 1973. SPRING
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