Diagnostic Coding and Medical Rehabilitation Length of Stay: Their Relationship

Transcription

1 241 Diagnostic Coding and Medical Rehabilitation Length of Stay: Their Relationship Margaret G. Stineman, MD, Jos~ J. Escarce, MD, Charles J. Tassoni, PhD, James E. Goin, PhD, Carl V. Granger, MD, Sankey V. Williams, MD ABSTRACT. Stineman MG, Escarce JJ, Tassoni CJ, Goin JE, Granger CV, Williams SV. Diagnostic coding and medical rehabilitation length of stay: their relationship. Arch Phys Med Rehabil 1998;79: Objective: To determine if diagnostic information provided in the form of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes improves rehabilitation length of stay (LOS) prediction when used in combination with the Functional Independence Measure- Function Related Groups (FIM-FRGs) classification system. Design: Various models characterizing diagnostic information using ICD-9-CM codes were created that included individual ICD-9-CM codes and groupings of those codes by organ or etiology involved. Each method was evaluated using linear regression with the natural logarithm of LOS as the dependent variable. Separate validation data sets were held back to quantify the incremental effect of diagnosis when combined with the FIM-FRG classification system. Setting: Records from 252 rehabilitation facilities and hospital units across the nation. Patients: Analyses were undertaken using 82,646 records from patients discharged in Results: The addition of ICD-9-CM diagnostic information to the FIM-FRG classification system increased the variance explained by a maximum of 1.9%, from 31.5% to 33.4%. Conclusions: Refinement of the FIM-FRGs to include ICD-9-CM diagnoses does not appear warranted on the basis of the small increase in the percentage of explained variance in LOS. We believe the lack of improved prediction with the addition of ICD-9-CM codes relates primarily to incomplete coding practices and to the effect of patients' diagnoses being absorbed in variables as already expressed by the FIM-FRG system. Although ICD-9-CM codes, overall, did not greatly improve LOS prediction, they appeared to have some impact in certain impairment categories. From the Leonard Davis Institute of Health Economics (Drs. Stineman, Escarce, and Williams), Center for Clinical Epidemiology and Biostatistics (Drs. Stineman, Escarce, Goin, and Williams), the Department of Medicine (Drs. Escarce and Williams), and the Department of Rehabilitation Medicine (Drs. Stineman and Tassoni), the University of Pennsylvania, Philadelphia; and the Department of Rehabilitation Medicine, State University of New York at Buffalo (Dr. Granger). Dr. Escarce is now affiliated with the RAND Corporation, Santa Monica, CA; Dr. Tassoni is affiliated with Johnson and Johnson Pharmaceutical Research Foundation, Raritan, NJ; and Dr. Goin is associated with DataMedix Corporation, Wayne, PA. Submitted for publication January 13, Accepted in revised form September 2, Supported by grant R01-HS07595 from die Agency for Health Care Policy and Research, National Institutes of Health; grant K08-AG00487 from the National Institute on Aging; and N1H grant R01-HD34378 from the National Institute of Child Health and Human Development. The opinions and conclusions herein are not necessarily those of the sponsoring agencies. No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the authors or upon any organization with which the authors are associated. Reprint requests to Margaret G. Stineman, MD, 101 Ralston-Penn Center, 3615 Chestnut Street, Philadelphia, PA by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation /98/ / by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation EHABILITATION HOSPITALS and distinct-part rehabili- R tation units are paid in accordance with the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA, PL ). These payments are based on each provider's Medicare-allowable inpatient costs, as documented by a facility-specific payment ceiling established from a base year of practice. The TEFRA system has led to disparities in payment across facilities because more efficient facilities with relatively low costs per case have low target amounts. Because the TEFRA system fails to consider the clinical characteristics of patients treated, facilities may incur financial losses if they treat patients who are clinically more severe than those treated in their base year. 1 In 1990, the Secretary of Health and Human Services called for proposals on how to restructure Medicare's payment system for inpatient rehabilitation facilities. In response, Congress has now authorized a prospective payment system (PPS) for medical rehabilitation starting with cost reports on or around October 1, 2000 with a 2-year transition period. 2 This PPS requires a method for classifying patients based on resource use. 3,4 We have developed a resource-use-based classification system, using patient records from the Uniform Data System for Medical Rehabilitation (UDSMRSM). 5 This system uses the impairment causing disability, age, and functional status at admission to rehabilitation as predictors of resource use. 6 Functional status is measured by the Functional Independence Measure (FIMTM). 5,7 Our system, called FIM-Function Related Groups (FIM-FRGs) version 1.1,6 classifies patients into categories that are homogeneous with respect to resource use as measured by rehabilitation length of stay (LOS). The Health Care Financing Administration is currently evaluating the applicability of the FIM-FRGs to the development of the pending rehabilitation Pps.s,9 Recently we completed version 2.0 of the FIM-FRGs. Like the first system, version 2.0 establishes groups of patients expected to have similar LOS. 1 Patients are first classified into 1 of 20 rehabilitation impairment categories or into a category for those undergoing evaluation only. Patients are then further subdivided, generally by level of function and, where determined to be an important element, by their age at admission to rehabilitation. Level of function is expressed as patients' admission performance on the motor FIM (items A to M) and the cognitive FIM (items N to R). 11 Psychometric analyses based on multitrait scaling support the summing of the component items to form subscales. 12 The motor subscale ranges in value from 13 to 91 and the cognitive from 5 to 35. The FRGs were defined by combining recommendations from a national panel of expert clinician researchers with the statistical procedure of recursive partitioning using Classification and Regression Trees (CART). 13 Because costs were not available and charges, which were available, are subject to large variation unrelated to cost of care, LOS was used as the Arch Phys Med RehabU Vol 79, March 1998

2 242 DIAGNOSTIC CODING AND REHABILITATION LOS, Stineman dependent variable. LOS was transformed by its natural logarithm. Patients' admission motor and cognitive FIM performance and age were used as independent variables by CART to form repeated dichotomous splits. Each split specifies the variable and a specific value of the variable that explains the most variance in LOS. An example of the computer-generated dichotomous splits is provided by the traumatic brain injury FIM-FRGs in figure 1. Version 2.0 of the FIM-FRG system explains 31.5% of the variance in rehabilitation LOS, which compares favorably to the explanatory power of Diagnosis- Related Groups 14 (DRGs) when first developed for acute care. Although rehabilitation LOS is decreasing, the capacity of FRGs to explain differences in LOS appears stable over time. 11 The traumatic brain injury FIM-FRGs shown in figure 1 illustrate a strong association between initial physical and cognitive disabilities and LOS, with lower function scores being associated with longer LOS. The figure reflects five clinically distinct categories of traumatic brain injury patients manifesting different levels of disabilities. If these classification rules were implemented as part of a PPS, then hospitals would receive five to six times the revenues for patients with the most severe disabilities (TBI-1) compared with those with the least severe disabilities (TBI-5). Thus, revenues for each facility would more equitably reflect the clinical characteristics of the patients actually served. These associations are similar in other categories of impairment. Practicing clinicians frequently express concern that the FIM-FRG system does not include medical comorbidity. Although FRGs adjust for the most important clinical differences, including the principal diagnosis (impairment) for which patients are receiving rehabilitation, it seems reasonable that the resource needs of patients with coexisting heart failure, major depression, or other illnesses should differ from those without such conditions. An earlier report by Hosek and colleagues is supports this assumption by identifying certain comorbid diagnoses, such as decubiti, that were associated with prolonged LOS. Moreover, illness severity and the presence or absence of major comorbidities explain variation in the resources used by acute care medical and surgical inpatient services. 16 We used the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) 17 codes, as recorded by facilities contributing data to the UDSMR, to study the effect of coexisting diagnoses on rehabilitation LOS. ICD-9-CM codes provide a way to translate verbal descriptions LOS FRGs Mean SD 1~-1s >L >~C' gnitive~ ~>>~~ ~_~j > ~ Fig 1. Dichotomous splits of traumatic brain injury (TBI) FIM-FRGs, showing five distinct FRGs, with lower FIM scores associated with longer LOS. of disease into numerical labels. The codes can then classify morbidity data, making it possible to index medical records for trend, payment, epidemiological, and quality improvement studies. Our objective was to determine if diagnostic information provided as ICD-9-CM codes improves rehabilitation LOS prediction when used in combination with the FIM-FRGs classification system. Data METHODS Data used to develop the FIM-FRGs were obtained from the UDSMm The UDSMR database includes the patient's impairment category and eight fields for ICD-9-CM codes. These fields include the principal diagnosis and seven additional secondary or related diagnoses intended to describe coexisting illness, manifestations, or complications. The principal diagnosis field is intended for recording the etiology of the patient's impairment. The seven other diagnoses may include coexisting conditions, manifestations (ie, comorbidities), and complications. The data also include functional status scores at admission and discharge (as measured by the FIM), and birth, admission, transfer, and discharge dates. Diagnoses are described by ICD-9~CM codes. This study used the same data set that was used to develop FIM-FRGs version 2.0, l which consisted of patients discharged in 1992 from 252 inpatient rehabilitation facilities and was limited to the 65 FRGs that involved impairments. To enhance data quality, analysis was limited to facilities in which clinicians responsible for coding the FIM scored 80% or above on a written examination of coding competence. Those facilities accounted for 68.3% (n = 93,829) of the patients in the database. Any records that were obviously miscoded, had incomplete information for any of the variables necessary for FIM-FRG classification, or that were missing basic demographic information were removed, leaving 92,705 cases. We also excluded patients who had LOS greater than 365 days (n = 29), were younger than 16 years of age (n = 315), were in rehabilitation no longer than 3 days (n = 1,033), were evaluation admissions (n = 1,063), were transferred to acute care (n = 4,858) or to another rehabilitation facility (n = 424), or who died during rehabilitation (n = 263). Lastly, we excluded patients whose LOS was 3 standard deviations (SDs) above the mean for their impairment category (n ). These exclusions were intended to remove atypical patient records (ie, nonadults or those who had unusually long or short stays). All cases with at least one ICD-9-CM code were included in the ICD-9-CM analyses. We coded as missing any principal and other diagnosis fields that contained invalid codes, for example, fields containing an ICD-9-CM code in which the patient's gender or age did not match the code's designated gender or age. is A total of 1,891 records were removed because of missing or inappropriately designated ICD-9-CM codes. The remaining records (n = 82,646) were used in this analysis. Certain descriptive analyses used this entire data set. When predictive models were developed, however, they were created using a model building sample and later tested in a separate validation sample. Through a randomization process, 60% of the observations in each of the 20 impairment categories were designated as the model building sample and the remaining 40% were designated for validation. This process assigned a total of 49,537 patient records to the model building sample and 33,109 to the validation sample.

3 DIAGNOSTIC CODING AND REHABILITATION LOS, Stineman 243 Analysis Plan The data analysis and modelling activities addressed three issues. These are classified as (1) ICD-9-CM coding practices, (2) ICD-9-CM codes with high frequency or high impact on LOS, and (3) the combined effect of ICD-9-CM codes on LOS. 1. The analysis of ICD-9-CM eoding praetices. Because the UDSMR principal diagnosis field is intended for recording the etiology or cause of the patient's impairment, the 1CD- 9-CM code in that field (UDSMR Question 18) 5 should be consistent with the patient's impairment group code (UDSMR Question 16). 5 If the patient's impairment is right-hemiparesis (UDSMR code 1.2), for example, an ICD-9-CM code indicating intercerebral hemorrhage (code 431) would be appropriate, but angina pectoris (code 413.0) would not, and should be listed in 1 of the 7 other ICD-9-CM fields as a coexisting condition or complication. We assessed the degree to which principal diagnosis codes reported in the principal diagnosis field were appropriate to the etiology of the patient's impairment using the Guide for the Uniform Data Set for Medical Rehabilitation, 5 which lists recommended etiologic diagnoses for each impairment group. This analysis combined the model building and validation samples because it was descriptive (rather than predictive). The proportion of cases within each of the 20 FIM-FRG impairment categories that had an appropriate ICD- 9-CM code in the principal diagnosis field was determined. There were 1,171 records out of 82,646 missing ICD-9-CM codes in the primary fields but not in the others. Thus, this analysis included 81,475 records. 2. ICD-9-CM codes with high frequency or high impact on LOS. Our objective was to create a list of common ICD-9-CM codes and to identify those associated with an extremely long LOS. If those codes associated with long stay occurred in a sufficient number of cases, they could then be used to refine individual FRGs based on whether the patient's record included one or more of them. There were up to eight ICD-9-CM codes per patient record studied. Information from all eight diagnosis fields was used. To identify the ICD-9-CM codes that were most common, we selected the three most frequently occurring in each impairment category. To identify the ICD-9-CM codes associated with the longest stays, we selected the three associated with the highest median LOS within each impairment. More than three codes were identified when ties occurred. Finally, we identified a priori those ICD-9-CM codes in which at least 75% of patients in the model building sample of an impairment category had LOS 3 days above the median for all patients in that category. This was referred to as the "75% rule." 3. The combined effect of ICD-9-CM codes on LOS. The impact of medical diagnoses on LOS is likely cumulative, in that the effect of one, such as diabetes, will be magnified by the presence of additional diagnoses. Thus, these analyses assume that both the type of diagnoses and the number of conditions will influence rehabilitation LOS. The combined effect of coexisting medical diagnoses is referred to as diagnostic complexity 19 and is based on the premise that it is the multiplicity of coexisting diagnoses, rather than any one diagnosis, that affects the use of medical rehabilitation resources. Thus, diagnostic complexity refers to both number and type of diagnoses. Three representations of diagnostic complexity were used. The predictive ability of each diagnostic representation alone was determined through impairmentspecific regressions in which the independent variables were (A) ICD-9-CM codes, (B) major diagnostic categories (MDCs), or (C) chapter headings. A. The first representation used each unique ICD-9-CM code that occurred in at least 20 cases in the model building sample of the impairment category being analyzed. Because there are hundreds of individual ICD-9-CM codes represented in the database, when each of these were included in the regression models as variables the models became very large. B. In the second representation, diagnostic complexity was measured as the number and type of MDCs. The MDCs are the first level of classification used in Medicare's DRGs) 4 There are 25 MDCs, each corresponding to a single organ system (pulmonary, digestive, etc) or an etiology (systemic infection or malignancy, etc). Each MDC comprises a group of closely related ICD-9-CM codes 2 (table 1). A patient was considered to have a diagnosis in an MDC if he or she had at least one ICD-9-CM code in that category, as assigned by the Medicare DRG system for acute care hospitals) 8 Two MDCs, multiple trauma and HIV infection, were not used because they depend on primary and secondary diagnoses as characterized in the acute care setting. Data in the UDSMR is limited to the postacute care rehabilitation setting. C. In the third representations complexity was measured as the number and type of chapter headings from the ICD-9-CM manual. There are 17 chapter headings in the manual (table 1), each corresponding to a different organ or body system. A patient was considered to have a diagnosis in a chapter heading if he or she had at least one ICD-9-CM code in that chapter. The predictive ability of each representation of diagnostic complexity was determined alone and in conjunction with the FIM-FRG system. The incremental effect of coexisting medical diagnoses on the prediction of resource use (LOS) was determined in relation to baseline models within impairment containing age and functional status (ie, the FRG elements). Multiple linear regression was used to evaluate tile independent contribution of various representations of comorbidities. The independent variables represented impairment-specific FRG classification system components and one of the representations of diagnostic complexity (FRG complexity models). Those models were then compared with regressions containing only FRG elements specific to each impairment category (FRG-only models) and to regressions containing only complexity (complexity-only models). We also calculated the incremental predictive ability of each of the three diagnostic representations across the entire FIM-FRG system. All analyses consisted of a series of multiple linear regression models ~1 estimated in the model building sample and tested in the validation sample. In all models the dependent variable was the natural logarithm of rehabilitation LOS. After the regressions were developed in the model building samples, they were used to predict LOS for observations in the validation samples. In conjunction with these predictors, the crossvalidation R 2 values were calculated in the validation samples by correlating the patient's actual LOS to that predicted by the regression model and squaring the correlations. 22 Establishing predictive ability in the validation data negates the effects of chance associations between ICD-9 codes and LOS. RESULTS 1. The Analysis of ICD-9-CM Coding Practices There were only eight impairments in which more than half the records had appropriate etiologic primary diagnoses as defined by the UDSMR (table 2). The most consistently appropriate coding was for lower extremity fracture; 75.5% of records for that condition had ICD-9-CM codes in the primary Arch Phys Med Rehabil Vo! 79, March 1998

4 244 DIAGNOSTIC CODING AND REHABILITATION LOS, Stineman Table 1: MDCs, Chapter Headings, and Corresponding ICD-9 Codes MDCs! Diseases and disorders of i the nervous system 2 Diseases and disorders of 2 the eye 3 Diseases and disorders of 3 the ear, nose, and throa t 4 Diseases and disorders of 4 the respiratory system 5 Diseases and disorders of 5 the circulatory system 6 Diseases and disorders of 6 the digestive system 7 Diseases and disorders of 7 the hepatobiliary system and pancreas 8 Diseases and disorders of 8 the musculoskeletal system and connective tissue 9 Diseases an d disorders of 9 the skin, subcutaneous tissue, and breast 10 Endocrine, nutritional, and 10 metabolic diseases and disorders 11 Diseases and disorders of 11 the kidney and urinary tract 12 Diseases and disorders of 12 the male reproductive System 13 Diseases and disorders of 13 the female reproductive system 14 Pregnancy, childbirth r and 14 the puerperium 15 Newborns and other neonates 15 with conditions orig!nating in the perinatal period 16 Diseases and disorders of 16 the blood, blood-forming organs, and immunological disorders 17 Myelopro!iferative diseases and disorders, and poorly differentiated neoplasms 18 Infectious and parasitic diseases (systemic or unspecified sites) 19 Mental diseases and disorders 20 Alcohol/drug use and induced organic mental disorders 21 Injuries, poisonings, and toxic effects of drugs 22 Burns 23 Factors influencing health status and other contacts with health services 24 Multiple trauma 25 HIV infection Chapter Headings and Range of Correapondlng ICD-9 Codes Infectious and parasitic dis - eases ( ) Neoplasm s ( ) Endocrine, nutritional, and metabolic diseases ( ) Diseases of the blood and blood-forming organs ( ) Mental disorders ( ) Diseases of the nervous system and sense organs ( ) Diseases of the circulatory system ( ) Diseases of the respiratory system ( ) Diseases of the digestive system ( ) Diseases of the genitourinary system ( ) Complications of pregnancy, childbirth, and the puerperium ( ) Diseases of the skin and subcutaneous tissue (680?709) Diseases of the musculoskeletal system and connective tissue (7!0-739) Congenital anomalies ( ) Certain conditions originating in the perinatal period ( ) Symptoms, signs, and illdefined conditions ( ) 17 Injury and poisoning ( ) diagnosi s field that described etiology appropriately. The least appropriate coding was for major multiple trauma with spinal cord injury and/or brain involvement; only 1.7% of codes were a recommended etiologic diagnosis. 2. ICD-9-CM Codes With High Frequency or High Impact on LOS Table 3 shows the mean, standard deviation, and median LOS values across the 20 impairment categories in the model building samples acros s all patients. In comparison with these data, the most commonly selected ICD-9-CM codes as determined for each impairment category had little impact On LO S (data not shown). For example, for 37% of patients with stroke, ICD-9-CM code 401 was' listed, indicative of hypertension (without specified end organ involvement), but the median LOS in those patients was 26 days compared with 25 days for the overall stroke population. The ICD-9-CM codes associated with the longest median stays are shown in table 4. ICD-9-CM codes signifying hypercoagulative disorders and infection were among those associated with the longest median LOS in 6 and 5 impairment categories, respectively. Although these ICD-9-CM codes appeared associated with longer stays, they were observed in a small proportion of patients and were not statistically meaningful. Across all 20 impairments, only 4 ICD-9-CM codes met the criteria for major diagnoses. These four ICD- 9-CM diagnoses are identified with asterisks in table 4. Because high-frequency ICD-9-CM codes were not associated with longer LOS and ICD-9-CM codes that were associated with longer LOS were so infrequent, the lists of ICD-9-CM codes associated with long stay were not used in attempts to refine the FIM-FRGs. Table 2: Number and Percent of Diagnoses in the Principal Diagnosis Field that Describe the Etiology of the Patient's Impairment Category (Total Sample) Not ImPairment Category Etiologic Etiologic Neurologic Stroke (ST) 13,712 (54.1%) 11,608 (45.9%) Nontraumat!c brain injury 934 (38.7%) 1,480 (61.3%) Traumatic brain injury 2,259 (71.8%) 887 (28.2%) Nontraumatic spinal cord injury 531 (21.0%) 1,992 (79.0%) Traumatic spinal cord injury 684 (38.5%) 1,094 (51,5%) Guillain Barr~ 276 (73.4%) 100 (26.6%) Neurologic--general 1,833 (53.4%)!,602 (46.6%) Musculoskeletal Lower extremity fracture 9,121 (75.5%) 2,953 (24.5%) Joint replacement 1,790 (14.8%) 10,311 (85.2%) Other orthopedic 777 (22.1%) 2,735 (77.9%) Lower limb amputation 859 (27.7%) 2,246 (72.3%) Other amputation 27 (13.5%) 173 (86:5%) Osteoarthritis 1,073 (66.8%) 534 (33.2%) Rheumatoid arthritis 638 (44.4%) 798 (55.6%) Other Cardiac 269 (27.3%) 715 (72.7%) Pulmonary 546 (57.1%) 410 (42.9%) Pain 969 (62.7%) 577 (37.3%) Major multiple trauma 67 (13.3%) 438 (86.7%) Multiple trauma with brain/ spine injury 7 (1.7%) 414 (98.3%) Miscellaneous 974 (24.1%) 3,062 (75.9%) All 37,346 (45,8%) 44,129 (54.2%)

5 DIAGNOSTIC CODING AND REHABILITATION LOS, Stineman 245 Table 3: Mean, Standard Deviation, and Median Values for LOS Across Impairment Categories in the Model Building Data Mean Median Impairment Categories N LOS SD LOS Stroke 15, Nontraumatic brain injury 1, [raurnatic brain injury 1, Nontraumatic spinal cord injury 1, Traumatic spinal cord injury 1, Guillain-Barr~ Neurologic--general 2,115 23, Lower extremity fracture 7, Joint replacement 7, Other orthopedic 2, Lower extremity amputation 1, Other amputation Osteoarthritis Rheumatoid arthritis Cardiac Pulmonary Pain Major multiple trauma Multiple trauma with brain/ spine Miscellaneous 2, The Combined Effect of ICD-9-CM Codes on LOS The three alternative representations of complexity showed little predictive ability on cross validation by themselves when used in regressions without the FRG classification. The proportion of variance explained (R 2) within impairment ranged from zero (chapter heading representation in the major multiple trauma with brain and/or spinal cord injury) to 12% (ICD-9-CM representation A in the nontraumatic spinal cord injury impairment). The incremental predictive ability of diagnostic complexity beyond that of the FIM-FRG system was even smaller. The largest incremental increases in explained variance occurred in the musculoskeletal impairments. This was above 5% only in joint replacement and only for the ICD-9-CM representation of complexity (representation A). This small increment in predicfive ability required an additional 112 predictor variables (ie, the 112 ICD-9-CM codes that occurred in at least 20 records in the joint replacement model building sample). The FIM-FRGs alone (with the evaluation categories excluded) explained 31.5% of the variance in the natural logarithm LOS. The proportion of variance explained in the validation data increased to 33.4% with addition of ICD-9-CM codes (representation A) and to 32.6% with MDCs (representation B), but decreased to 31.4% when chapter headings were added (representation C). The negative incremental change in explained variance on cross validation likely occurred because of the addition of large numbers of variables with weak predictive power. The proportion of variance explained by the FRGs alone within each impairment category compared with that explained after adding each representation of diagnostic complexity is shown in figure 2. Comparisons of the first with the remaining three bar sets in this figure show that in the vast majority of impairments diagnostic complexity, when used in conjunction with FIM-FRGs, did little to enhance predictive capacity of the classification system. However, there was a small but consistent association with longer LOS in joint replacement, osteoarthri- tis, inflammatory arthritis, lower extremity fracture, spinal cord injury, and nontraumatic brain injury. DISCUSSION Information about coexisting medical diagnoses, as expressed by ICD-9-CM codes and as being currently recorded by facilities reporting to UDSNR, would offer only negligible improvement in the ability of the FIM-FRGs to predict inpatient rehabilitation length of stay. Knowledge of a limited association between medical diagnoses and rehabilitation LOS is not new. Roth and colleagues, 23 for example, found that cardiac disease was not associated with longer stays for stroke patients. Moreover, inpatient rehabilitation was excluded from the DRG system back in the 1980s because diagnosis, as captured by that system, failed to explain sufficient variance in LOS. is By representing complexity in three alternative ways, our approach was intended to maximize the chance that we would be able to capture subtle effects of diagnoses on rehabilitation LOS. The ICD-9-CM representation was the most promising. Refinement of the FIM-FRGs to include medical diagnoses in this manner would increase the percentage of variance explained from 31.5% to 33.4%; however, the small increase in predictiv e ability would occur at the expense of adding large numbers of variables. Although the count of MDCs was effective in a patient classification system for inpatient psychiatry, 24 it was not important in explaining the variance in rehabilitation LOS. The chapter heading representation performed even less well. There are any number of explanations for the limited association between medical complexity and LOS. ICD-9-CM codes only indicate diagnoses and, in general, do not express severity or impact on function. Information about coexisting medical diagnoses, as indicated by ICD-9-CM codes, may be redundant to information about impairment, function, and age already included in the FIM-FRGs. For example, the functional level at admission of a patient who is receiving rehabilitation for stroke who also has cardiac and renal disease will be a reflection of all three conditions, not just the stroke. Alternatively, medical diagnoses may be an important determinant of LOS, but current coding conventions do not capture that information accurately. If a record has a code indicating hypertension, for example, that patient likely was hypertensive or being treated for it, but a hypertension code may not be recorded for all people with that condition. Thus, unless coding is exhaustive, ICD-9-CM codes in administrative data such as the UDSMR cannot be expected to yield accurate prevalence estimates. Problems caused by incomplete or biased ICD-9-CM coding in acute hospital records have been noted for conditions, such as previous myocardial infarction and diabetes, that were expected to increase the risk of death but actually appeared to lower it. This was attributed to bias against coding chronic or comorbid conditions in people who died. 25 Thus, coding problems are not unique to medical rehabilitation. Although records in the UDSMR may currently provide the best source for studying the influence of medical diagnoses on LOS, ICD-9-CM coding relies on clinician documentation and chart review by medical records staff that sometimes may not be complete and accurate. Even assuming accuracy, the choice of ICD-9-CM codes is complex and their content may not provide sufficient information about how medical problems are affecting the rehabilitation process. Our study involved a secondary analysis of existing UDSMR data, where ICD-9-CM codes were not necessarily collected for the purpose of delineating the impact of diagnoses on LOS. In contrast, the study by Hosek and colleagues, 15 which found an association, had a data-collection protocol explicitly designed to measure the impact of diagnostic information on resource use.

6 246 DIAGNOSTIC CODING AND REHABILITATION LOS, Stineman Table 4: ICD-9-CM Codes With the Longest Median LOS by Impairment Category (Model Building Sample) Median Diagnostic Labels (ICD-9-CM Codes) N % LOS Stroke Other venous thrombosis (453) Autonomic nerve disorder (337) Other intestinal disorder (569) Coagulation defects (286) Nontraumatic brain injury Cerebral artery occlusion (434) Artificial opening status (V44) 36 2, Other venous thrombosis (453) 28 1,9 36,0 Traumatic brain injury Artificial opening status (V44)* 60 3,1 58,5 Bacterial infection in other disease (41) 58 3, Other joint derangement (718) Nontraumatic spinal cord injury SCI without fracture (952) Other surgical complications (998) 30 1, Other venous thrombosis (453) Traumatic spinal cord injury Other bladder disorders (596)* Bacterial infection in other disease (41) Other venous thrombosis (453) Guillain-Barr~ Other paralytic syndromes (344) Other urinary tract disorder (599) Gastrointestinal system symptoms (787) ,0 Neurologic Chronic ulcer of skin (707) Other venous thrombosis (453) Other cellulitis/abscess (682) Lower extremity fractu re Other venous thrombosis (453) Fracture (829) Multiple sclerosis (340) Joint replacement Late effect cerebrovascular disease (438) Hemiplegia (342) Other femoral fracture (821) Symptoms involving head/neck (784) Mononeuritis (355) 31 0, Other orthopedic conditions Other paralytic syndromes (344) Chronic ulcer of skin (707) Urinary system symptoms (788) Other femoral fracture (821) Angina pectoris (413) Other surgical complications (998) Other lung diseases (518) Lower extremity amputation Depressive disorder (311 ) Adjustment reaction (309) Other celluiitis/abscess (682) Symptoms involving head/neck (784) Hypertensive renal diseases (403) Other amputation Other acquired limb deformities (736) Other peripheral vascular disease (443) Rehabilitation procedure (V57) Osteoarthritis Other ill-defined morbidity (799) Depressive disorder (311 ) Heart failure (428) Disease of muscle/ligament/fascia (728) Table 4: ICD-9-CM Codes With the Longest Median LOS by Impairment Category (Model Building Sample) (Cont'd) Median Diagnostic Labels (ICD-9-CM Codes) N % LOS Rheumatoid arthritis Other paralytic syndromes (344)* Disease of muscle/ligament/fascia (728) Bacterial infection in other disease (41) Cardiac Other urinary tract disorder (599) Diseases of muscle/ligament/fascia (728) Pleurisy (511) Cardiomyopathy (425) Pulmonary Other machine dependence (V46)* Other urinary tract disorder (599) Other postsurgical states (V45) Pain syndrome Drug dependence (304) Functional digestive disease (564) Late effect nervous system injury (907) Major multiple trauma Pelvic fractu re (808) Other urinary tract disorder (599) Other postsurgical states (V45) Major multiple trauma with brain/spine Nonpsychotic brain syndrome (310) Hemiplegia (342) ,0 Other paralytic syndromes (344) Miscellaneous Other surgical complications (998) Different connective tissue disease (710) Other brain conditions (348) Percent (%) refers to the proportion of patients in each impairment category who had the ICD-9-CM code. The corresponding median LOS for those patients is shown to the right. * Diagnosis where at least 75% of patients had LOS longer than 3 days above the median for the model building sample of their impairment group, Diseases represented by ICD-9-CM codes may also have opposing effects on LOS, sometimes increasing patient stay while other times causing patients to be discharged prematurely. In UDSMR data it was impossible to distinguish between preexisting conditions and secondary complications occurring during rehabilitation. It may be that complications have a greater impact on LOS. The ICD-9-CM codes with the longest stays support this assumption, because those designating thrombosis or infection, which are likely complications, were among those associated with the longest median stays. Our analysis disregarded the distinction between the primary diagnosis and other diagnosis fields, thus treating information from all eight diagnosis fields the same way. This decision was necessary, because, consistent with our earlier findings, 26 rehabilitation clinicians (or other coders) do not always reserve the primary UDSMR diagnosis field for etiology, Thus, it was unrealistic to make distinctions between information contained in the primary and secondary fields. This coding inconsistency, along with other findings in our analyses, highlights the importance of developing and adopting ICD-9-CM coding standards specific to the needs of medical rehabilitation. The development of coding conventions specifically designed to express the impact of diagnoses on the rehabilitation process might yield more positive results. Our decision to combine diagnoses from the principal and other fields contrasts with the way diagnosis was handled in the

7 DIAGNOSTIC CODING AND REHABILITATION LOS, Stineman 247 b g 0.25 ~ LLI.~_ , ~- r3 g z ~: z N o == N = <, o. :~ o = Fig 2. The amount of variance explained in validation data by FRGs alone ([]l) and by FRGs with the addition of each representation of diagnostic category (m, FRG + chapter heading; D, FRG + MDC; [], FRG + ICD-9-CM code). BrSp, multiple trauma with brain or spine; TrBr, traumatic brain injury; TrScd, traumatic spinal cord injury; G-Barre, Guillain-Barre syndrome; NtrBr, nontraumatic brain injury; NtrScd, nontraumatic spinal cord injury; neuro, neurologic; JntRep, joint replacement; Osteo, osteoarthritis; Rheum, rheumatoid arthritis and other; LEFX, lower extremity fracture; Othorth, other orthopedic; LLAMP, lower limb amputation; Pulm, pulmonary; MMT, major multiple trauma. original DRG system 27 and in the recently proposed severityadjusted refinements) 6 In both the original and the proposed refinements of the DRGs, only secondary diagnoses are used to account for comorbidities, complications, "major" complications, or "major" comorbidities. Our approach, however, allows information about the etiology of impairment to influence rehabilitation LOS. For example, assume that for a patient in the stroke impairment category a diagnosis of intracerebral hemorrhage (in the etiologic field) might add information about LOS above and beyond the fact that he or she has had a stroke. The Patient Management Categories, 28 which were proposed as an alternative to DRGs for hospitalized patients, also ignored the actual sequence of diagnoses. Our analyses focused on the impact of diagnoses on LOS. Ongoing work is determining if comorbidities and complications are more strongly related to the cost of care than to the length of care. 8,9 Patients with certain complications or comorbidities might reasonably incur higher daily costs for supplemental services, even if LOS remains constant. More than anything else, the results of this study emphasize the need to develop more adequate and uniform ICD-9-CM coding practices and to develop more relevant ways of characterizing diagnostic complexity for rehabilitation services. One improvement would be to ensure that the ICD-9-CM code in the UDSMR principal diagnosis field records the etiology of the patient's principal impairment. Other improvements would be to distinguish among etiologies or manifestations related to the principal impairment, comorbidities unrelated to the principal impairment, and complications that occur during rehabilitation. Much of this work is being undertaken at the UDSMR and elsewhere. Clinicians and coders might also be encouraged to limit selection to those diagnoses that they believe most influenced the rehabilitation process, LOS, or patient outcomes. With growing emphasis on the need to demonstrate effectiveness of programs at the national level, it is important for clinicians to become more aware of how diagnostic information might be used. In turn, policy makers need to remain aware of the inherent limitations in generating clinical assessments based on ICD-9-CM codes alone. 25 Although the predictive ability of diagnostic information, as we analyzed it, was too limited to warrant inclusion in the FIM-FRGs, there were some indications that coexisting diagnoses might be more important for certain impairments than for others. Diagnoses had a small but consistent effect in several of the musculoskeletal impairments, nontraumatic brain injury, and spinal cord injury, suggesting that these impairment groups should be targeted for further study. In addition, hypercoagularive states (such as deep vein thrombosis) and infection were

8 248 DIAGNOSTIC CODING AND REHABILITATION LOS, Stineman associated with longer LOS in several impairments. This suggests that LOS might be reduced and quality of care enhanced if clinicians focus on programs to prevent such complications in the rehabilitation setting. There may be better approaches to classifying comorbidity or different methodologies for incorporating diagnostic complexity. One approach would grade comorbid conditions by the degree to which they limit activities or therapy. 29 Conclusions about the limited influence of medical complexity on resource utilization based on the results of this single study must be avoided. ICD-9-CM codes contained in the UDSMR may not accurately reflect medical complexity, because of incomplete coding practices, because the content of codes do not reflect certain specific conditions of relevance to rehabilitation, or because codes fail to adequately express illness severity. More appropriate and standardized coding practices might reveal stronger associations between coexisting diagnoses and LOS. Finally, medical costs, rather than LOS, may be a more appropriate way to measure the effect of coexisting medical illness on resource use. Alternatively, the effect of diagnoses on LOS may be irrelevant because it is absorbed into the patient's impairment or function as assessed at admission through the FIM-FRGs. Unlike with the current TEFRA system, by adjusting resources to patients' clinical characteristics the FIM-FRGs have the potential to yield major improvements in the equitable distribution of rehabilitation resources to people with disabilities in this country. The system does not appear significantly improved by the addition of medical complexity as we measured it, although efforts are ongoing to measure the impact of diagnoses differently. Acknowledgments: The authors thank Delores C. Foster- Kennedy and John Lehr for gathering relevant materials and editing the manuscript. The authors also thank Dr. Theodore Cole, Dr. Joel DeLisa, Dr. Alan Jette, and Dr. John Melvin for their thoughtful input as technical advisors to this phase of the FIM-FRG project. References 1. Newhouse JP. Testimony before the US House of Representatives Committee on Ways and Means Subcommittee on Health for the Prospective Payment Assessment Commission, April 10, Omnibus Budget Reconciliation Act of Report to Congress on H.R. 2015, H.R (1997). 3. Coopers & Lybrand. NARF position paper on a prospective payment system for inpatient medical rehabilitation services and a study regarding a prospective payment system for inpatient medical rehabilitation services: final report. Washington (DC): National Association of Rehabilitation Facilities; Langenbrunner JC, Willis E Jencks SF, Dobson A, Iezzoni L. Developing payment refinements and reforms under Medicare for excluded hospitals. Health Care Financ Rev 1989;10: Uniform Data System for Medical Rehabilitation. Guide for the Uniform Data Set for Medical Rehabilitation (Adult FIM), version 4.0. Buffalo (NY): Research Foundation, State University of New York at Buffalo; Stineman MG, Escarce JJ, Goin JE, Hamilton BB, Granger CV, Williams SV. A case mix classification system for medical rehabilitation. Med Care 1994;32: Hamilton BB, Granger CV, Sherwin FS, Zielezny M, Tashman JS. A uniform national data system for medical rehabilitation. In: Fuhrer M J, editor. Rehabilitation outcomes: analysis and measurement. Baltimore (MD): Paul H. Brookes Publishing Co; p Carter G, Relles D, Buchanan J, Bean D, Donyo T, Rosenfeld K, et al. A classification system for inpatient medical rehabilitation patients: a review and proposed revisions to the Functional Independence Measure-Function Related Groups (PM-682- HCFA). A project memorandum prepared for the Health Care Financing Administration by the RAND/UCLA/Harvard Center for Health Care Financing Policy Research, June Carter G, Relles D, Buchanan J, Donyo T, Inkelas M, Spritzer K. A prospective payment system for inpatient rehabilitation (PM-683- HCFA). A project memorandum prepared for the Health Care Financing Administration by the RAND/UCLA/Harvard Center for Health Care Financing Policy Research, June Stineman MG, Tassoni CJ, Escarce JJ, Goin JE, Granger CV, Fiedler RC, et al. Development of function related groups, version 2.0: a classification system for medical rehabilitation. Health Serv Res 1997;32:52% Linacre JM, Heinemann AW, Wright BD, Granger CV, Hamilton BB. The structure and stability of the Functional Independence Measure. Arch Phys Med Rehabil 1994;75: Stineman MG, Shea JA, Jette A, Tassoni CJ, Ottenbacher KJ, Fiedler R, et al. The Functional Independence Measure: tests of scaling assumptions, structure, and reliability across 20 diverse impairment categories. Arch Phys Med Rehabil 1996;77: Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Belmont (CA): Wadsworth, Inc.; Fetter RB, Shin Y, Freeman JL, Averill RF, Thompson JD. Case mix definition by diagnosis-related groups. Med Care 1980; 18: Hosek M, Kane R, Carney M, Hartman J, Reboussin D, Serrato C, et al. Charges and outcome for rehabilitation care: implications for the prospective payment system. Santa Monica (CA): The RAND Corporation; Edwards N, Honemann D, Burley D, Navarro M. Refinement of the Medicare diagnosis-related groups to incorporate a measure of severity. Health Care Financ Rev 1994;16: Practice Management Information Corporation. International classification of diseases, 9th revision. 3rd ed. Los Angeles: PMIC; HCIA, Inc. EZ-Ref ICD-9-CM codes and titles. Ann Arbor (MI): HCIA; Averill RF. Development. In: Fetter RB, editor. DRGs: their design and development. Ann Arbor (MI): Health Administration Press; p HCFA/3M Health Information Systems. DRGs--diagnosis related groups definitions manual, version Wallingford (CT): 3M Health Information Systems; SAS Institute, Inc. SAS procedures guide, version 6.3rd ed. Cary (NC): SAS Institute, Inc.; Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariable methods. Boston (MA): PWS- Kent Publishing Co.; Roth EJ, Mueller K, Green D. Stroke rehabilitation outcome: impact of coronary artery disease. Stroke 1988;19: Ashcraft MLF, Fries BE, Nerenz DR, Falcon SP, Srivastava SV, Lee CZ, et al. A psychiatric patient classification system: an alternative to diagnosis-related groups. Met Care 1989;27: Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? JAMA 1992;267: Stineman MG, Granger CV, Hamilton BB, Melvin JL, Fiedler IG. Specificity of ICD-9 coding practices for stroke rehabilitation. Am J Phys Med Rehabil 1993;74: Fetter RB. Background. In: Fetter RB, editor. DRGs: their design and development. Ann Arbor (MI): Health Administration Press; p Young WW, Swinkola RB, Zorn DM. The measurement of hospital case mix. Med Care 1982;5: Liu M, Domen K, Chino N. Comorbidity measures for stroke outcome research: a preliminary study. Arch Phys Med Rehabil 1997;78: