TECHNICAL REPORT. October 2006

Transcription

1 TECHNICAL REPORT DEVELOPMENT OF THE REHABILITATION PATIENT GROUP (RPG) CASE MIX CLASSIFICATION METHODOLOGY AND WEIGHTING SYSTEM FOR ADULT INPATIENT REHABILITATION October 2006 Jason Sutherland, PhD Division of Biostatistics, Indiana University School of Medicine Health Services Research and Development (HSRD), Roudebush VAMC, Indianapolis Jan Walker, PhD Department of Public Health Sciences, University of Toronto 1

2 TABLE OF CONTENTS 1 INTRODUCTION LITERATURE REVIEW METHODS OBJECTIVE 1: PERFORMANCE OF CMG AND FRG USING ONTARIO NRS DATA OBJECTIVE 2: DEVELOP PATIENT CLASSIFICATION SYSTEM USING ONTARIO DATA OBJECTIVE 3: DEVELOP COST WEIGHTS FOR NEW PATIENT CLASSIFICATION SYSTEM RESULTS OBJECTIVE 1: PERFORMANCE OF CMG AND FRG WITH ONTARIO NRS DATA OBJECTIVE 2: DEVELOP PATIENT CLASSIFICATION SYSTEM USING ONTARIO DATA OBJECTIVE 3: DEVELOP COST WEIGHTS USING ONTARIO DATA APPLICATION OF RCW LENGTH OF STAY AND APPLICATION OF RCW DISCUSSION COMORBIDITIES FURTHER DEVELOPMENT DATA QUALITY ISSUES SUMMARY OF RECOMMENDATIONS ACKNOWLEDGEMENTS REFERENCES

3 1 INTRODUCTION This Technical Report is a companion document to the JPPC report RD The purpose of this report is to provide technical details of the analyses conducted to develop and implement a case mix classification and associated weighting system for adult inpatient rehabilitation activity in Ontario. In the fall of 2002, the Ontario Ministry of Health and Long Term Care mandated the collection of National Rehabilitation Reporting System (NRS) data in all designated adult inpatient rehabilitation beds. The minimum dataset and reporting system was developed and is maintained by the Canadian Institute for Health Information (CIHI) and includes demographic, clinical and functional information for adult rehabilitation inpatients at admission and discharge from a designated rehabilitation bed. One purpose for the introduction of the reporting system was to establish a minimum dataset in rehabilitation in order to facilitate the application of a case mix methodology. Accompanying relative cost weights would then provide the information needed to incorporate adult inpatient rehabilitation activity into the Integrated Population Based Allocation (IPBA) hospital funding formula. The JPPC Rehabilitation Technical Working Group was struck in 2004 with a mandate to evaluate FIM-based groupers and to develop cost weights reflective of Ontario inpatient rehabilitation costs. This Technical Report summarizes the technical aspects of the work of that group. 3

4 2 LITERATURE REVIEW Case mix classification methods have been adopted in many countries as a method to manage and finance healthcare resources in acute care settings; the most popular systems are based on diagnosis related groups (DRG). Although effective in the acute care setting, the literature generally reflects agreement that a classification scheme based on diagnosis does not describe resource use for inpatient rehabilitation patients adequately (Harada et al, 1993; Carter, Relles and Wynn, 2000). In 1977, the US Balanced Budget Act directed the Health Care Financing Administration (HCFA, since renamed Centers for Medicare and Medicaid Services, CMS) to implement a prospective payment system (PPS) for inpatient rehabilitation facilities (IRFs). Given that patients were admitted to rehabilitation facilities to improve function and independence, functional status at admission is the primary determinant of resource use for IRFs (Carter et al, 2000). Measurement of functional status as a key determinant of resource use was supported by earlier literature; Batavia s (1988) analysis identified a payment model based on function as the most appropriate for IRFs. Harada, Sofaer and Kominski s (1993) study supported the use of a functional measure to predict resource utilization. Likewise, Wilkerson, Batavia and DeJong (1992) presented studies indicating that functional status, and gain, was a good descriptor of resource utilization in IRFs. Stineman et al. (1994) developed a classification methodology to assign inpatient rehabilitation patients to groups. In this first-generation case mix system for inpatient rehabilitation, length of stay was used as a proxy for resource use and formed the basis for relative cost weights. Known as Function Related Groups (FRG), the system used data from the Uniform Data System (UDS), which included patient level demographic, clinical and hospital stay data. In addition, information on functional status at admission and discharge, as measured by the Functional Independence Measure (FIM 1 ), was available (Stineman et. al. 1994). Using classification and regression tree methodologies (CART), four variables were identified as being predictive of resource use: clinical reason for admission, admission FIM motor scores, admission FIM cognitive scores and patient age. Building on the work of Stineman et al. (1994), Carter et al. (2000) refined and modified FRG for use by the CMS in their inpatient rehabilitation facility prospective payment system instituted in This inpatient rehabilitation classification system was referred to as Case Mix Groups (CMG). In the CMG case mix system, Rehabilitation Impairment Categories (RIC) constitute groups of patients that are clinically similar and are based on the clinical reason for admission to rehabilitation. The most common RIC were stroke and lower joint replacement. Within each RIC, cases were further partitioned into CMG based on age, FIM-measured functional and cognitive status. 1 FIM is copyright 1997 Uniform Data System for Medical Rehabilitation 4

5 In addition, CMG have refinements, known as comorbidity tiers, based on ICD-9-CM codes. Comorbidity tiers acknowledge that comorbidities and complications increase the cost of inpatient rehabilitation care. Comorbidities were ranked according to degree of increase in cost, and grouped into four tiers accordingly. The first tier was the most expensive, representing an increase in cost in excess of 15% compared to clinically similar patients with comorbidites. The second tier reflected an added burden of 11-15%, and the third 4-10%. A fourth comorbidity tier included conditions that did not affect costs (0-3%.) As costs for each tier varied according to clinical condition, the reimbursement amount was calculated according to RIC. The original CMG algorithm, consisting of 95 CMG, was based on 1998/99 data from a sample of IRFs. For patients for whom CMS was the payor, CMG were implemented for prospective payment of inpatient rehabilitation in The Balanced Budget Act provided for refinements to the inpatient rehabilitation PPS over time. Refinements could be necessary for a number of reasons; for example, more recent data reflects practice patterns of the time and changes in coding behaviour. As a result, CMS contracted with RAND to examine possible refinements to the IRF-PPS. That work examined the performance of the classification system using 2003 data. In total, the classification system, including CMGs and relative weights, coding changes and facility level adjustments were examined. In the update to the classification system, a number of recommendations to refine the classification system were made. Among them was a recommendation to use a weighted motor score index which was shown to better predict costs than a motor score that treated all components equally. Further, to better align prospective payments with patient cost, the CMGs were updated, reducing the number of patient groups from 95 to 87. This reduction was primarily due to a reduction in the number of patient groups within the stroke RIC. In addition, there were recommendations to change the list of qualifying comorbidities that defined comorbidity tiers. For example, there were recommendations to remove some codes that were not associated with an increase in treatment costs, and to move dialysis to the tier associated with the highest payment. In addition to these recommendations, CMS made some changes to geographic boundaries. All changes to the PPS appear in a final ruling in the Federal Register on August 15, 2005 (42 CFR Part 412 at 47880). The changes took effect October 1, Uniform Data Systems has adopted these recommendations for their clients submitting data for Medicare recipients. 5

6 3 METHODS Three specific objectives were addressed by the work described in this report. Those objectives were: 1. To examine the performance of existing inpatient rehabilitation grouping methodologies using Ontario data. 2. To develop a new grouping methodology for adult inpatient rehabilitation using available Ontario data 3. To develop cost weights associated with the new grouping methodology. 3.1 Objective 1: Performance of CMG and FRG using Ontario NRS data The purpose of Objective 1 was to examine the performance of existing inpatient rehabilitation grouping methodologies using Ontario NRS data. If the performance of an existing methodology was reasonable, then it was a reasonable candidate as the classification methodology for inpatient rehabilitation of choice in Ontario. The available existing methodologies were FRG and CMG. FRG was a data element in the NRS dataset. Assignment to an FRG occurred through a proprietary system owned by UDS MR and under license to CIHI. The algorithms for computing FRG were not readily available at the time of this analysis, although were subsequently provided for the purposes of this project through CIHI with permission from UDS MR. The algorithm is not otherwise publicly available. The assignment of CMG was based on the CMG classification methodology made publicly available by the Centers for Medicare and Medicaid Services (CMS). The algorithm for this grouper was available at the following website: Data Source For the purpose of assessing the performance of the FRG and CMG inpatient rehabilitation patient grouping methodologies, complete episodes of care from the NRS for fiscal years of 2002/2003 and 2003/2004 were used. Aggregating 2 fiscal years of discharges provided significant sample sizes in important subgroups of the data. Data from all participating facilities in Ontario provided over 40,000 patient records available for analysis. The data included patient-level and facility-level identifying information and included all NRS data elements. The NRS contained all the required elements to group episodes using the CMG algorithm. In addition to demographic and diagnosis-related data, the core data elements of the NRS are the FIM elements. FIM is an 18 item scale which has two dimensions: a motor score and a cognitive score. Decreasing motor, or cognitive, function is related to low FIM scores. The FIM motor scale contains 13 items, while the FIM cognitive scale has 5 items. The NRS contained all the required elements to group episodes using the CMG algorithm. 6

7 3.1.2 Analyses The unit of analysis was patient episode categorized by rehabilitation group, which represented a high level diagnostic category that defined clinically similar groups. The clinical homogeneity of rehabilitation groups has been established in the literature and was not further evaluated in this project. Patients with lengths of stay less than, or equal to 3 days, and those that did not meet service goals were excluded from the analysis. These restrictions were applied in order to be in line with the sample selection used in the development of the CMGs. As mentioned previously, FRG was a data element in the dataset. To calculate CMG, we applied the CMG algorithm to the NRS data. The comparison between the FRG and CMG was based on the ability of the terminal cells to describe variation in length of stay within each rehabilitation group. To evaluate the homogeneity of length of stay (LOS), we calculated the coefficient of variation (CV) within each FRG and CMG. In addition we examined, the R-square statistic, which represents the relative amount of explained variation in LOS due to age, FIM motor score and FIM cognitive score. To facilitate comparisons between the two classification systems, the data was not trimmed for length of stay outliers. Chapter 4 describes in detail the specific results from applying the described methods to stroke patients (which comprised approximately 18% of inpatient rehabilitation patients). Although the results pertain to stroke classification only, the methods were applied to all rehabilitation groups. 3.2 Objective 2: Develop patient classification system using Ontario data The purpose of Objective 2 was to develop a classification system using Ontario data, and compare its performance to existing methodologies Data Source The same dataset used for objective #1 was used in objective # Analyses The first step in the analysis was to randomly split the data into two subgroups. We refer to the two data sets as the Training and Evaluation datasets. The development of the patient classification system was based on the Training data set. As a validation step, the characteristics of the developed classification system were assessed by comparing the results to those obtained when the patient classification system was used on the independent, Evaluation, dataset. Partitioning of the data in this manner served to minimize the risk that the developed classification system was not an artifact of inpatient rehabilitation episodes in the two year period. Using the Training data set, clinically similar groups were determined based on rehabilitation client groups (RCG). RCG is an NRS data item. Table #1 shows the relationship between the Rehabilitation Group (RG) and the RCG. 7

8 Table #1. List of Rehabilitation Groups (RG) Rehabilitation Group RCG 1. Stroke 1.1, 1.2, 1.3, 1.4, Traumatic Brain Injury 2.2, 2.21, Non-Traumatic Brain Injury 2.1, Neurological 3.1, 3.2, 3.3, 3.4, 3.5, 3.8, Traumatic Spinal Cord Injury 4.2, 4.21, 4.211, 4.212, 4.22, , , , , Non-Traumatic Spinal Cord Injury 4.1, 4.11, 4.111, 4.112, 4.12, , , , , Amputation, Non-Lower Extremity 5.1, 5.2, Amputation, Lower Extremity 5.3, 5.4, 5.5, 5.6, Osteoarthritis Rheumatoid arthritis and Other Arthritis 6.1, Pain 7.1, 7.2, 7.3, Fracture of Lower Extremity 8.1, 8.11, 8.12, 8.2, , 8.51, 8.52, 8.53, 8.54, 8.6, 8.61, 8.62, 13. Replacement of Lower Extremity 8.63, 8.64, 8.7, 8.71, 8.72, 8.73, Other Orthopedic Cardiac Pulmonary 10.1, Burns Major Multiple Trauma, Other Multiple 14.9, 8.4 Trauma and Major Multiple Fracture 19. Major Multiple Trauma, with Brain or Spinal 14.1, 14.2, 14.3 Cord Injury 20. Ventilator Dependent Respiratory Disorders 21. Other Disabilities , 12.9, 13.1, 15.1, 16.1, 17.1, 17.2, 17.31, 17.32, 17.4, 17.52, 17.6, 17.7, 17.8, 17.9 In effect, RG represented the highest level of classification for patients in determining their final classification group. Within RG, recursive partitioning (regression tree) methods (Breiman et al., 1984) were applied to create partitions using logarithm of length of stay as the dependent variable. The transformation of length of stay was applied to account for the skewness in the distribution of this variable. The partitioning of patients into terminal nodes was stopped when the number of patients in terminal nodes was small (generally less than 20) or the contribution to describing variability in LOS was minimal. The clinical characteristics used to describe variation in the logarithm of length of stay were age, admission FIM motor score and admission FIM cognitive score. Consistent with CMS updated CMG patient classification system (RAND, 2005), transfer to tub scores were removed from the FIM Motor score calculation, resulting in a ceiling of 84 (each question ranges in value from 1 to 7, where 7 is independent). Residuals were evaluated for normality. The terminal cells in the regression tree analyses represented groups who were homogeneous with respect to the logarithm of length of stay. We refer to these groups as Rehabilitation Patient Groups (RPG). 8

9 Using the Training dataset, generalized additive models were applied to confirm results from the regression tree models. For each RG, the logarithm of length of stay was the dependent variable, while additive components in the model were age, admission FIM motor sore and admission FIM cognitive score. Generalized additive models provided insight into the distribution of length of stay due to each of age, admission FIM motor sore and admission FIM cognitive score, as FIM scores often affected the logarithm of length of stay in a non-linear fashion. Partitions resulting from the regression trees were compared to the additive components for each clinically similar group of patients. We did not find non-linear relationships in the additive models that were not represented in the regression tree models and, in general, the fitted models concurred with the partitioning indicated by the regression trees. In the final analytic step, we used the Evaluation data set to assess the extent to which the groups created using the training dataset fit the evaluation data. To accomplish this, patient episodes were assigned to the partitionings created using the methods described above. Then, a multi level model was fit to length of stay (and logarithm of length of stay), where RPG effects were nested within Rehabilitation Groups (the highest level of clinically similar patients) Cormorbidities As part of the development of the classification methodology using Ontario data, we examined the impact of comorbidities. In the CMG classification system, each CMG has further refinements based on the documented presence of selected comorbidities, known as Comorbidity Tiers. Comorbidity Tiers form a significant development in inpatient rehabilitation classification systems when compared to the FRG classification methodology. There are 4 comorbidity tiers in each CMG (the fourth tier includes comorbidities that have not been found to affect episode cost.) Within that system, assignment to a comorbidity tier is based on presence/absence of ICD-9 codes. The ICD-9 codes that comprise comorbidity tiers were those that were determined to affect episode cost across all CMG. Cost weights increase as the comorbidity tier decreases (Tier 1 is the highest, most costly.) If there are no comorbidities that result in assignment to a comorbidity tier, the episode is assigned to the base CMG. In the event that a patient presents with more than one comorbidity, the lowest tier (which has the highest weight) is selected. Table #2 shows a truncated list of comorbidities from each of the three tiers in the CMG system. Table #2: Examples of comorbidities within each Tier of the CMG system Tier 1 Tier 2 Tier 3 Candidiasis of Lung Tuberculous Pneumonia Tetanus Dependence on Respirator Pulmonary TB NEC Cystic Fibrosis w Ileus Edema of Larynx TB Meningitis Cult Morbid Obesity Tracheostomy Status Streptococcal Septicemia Sickle-cell anemia NEC Foreign Body Bronchus Anaerobic Septicemia Pulmonary Embolism Other Severe Malnutrition Histoplasmosis Viral Pneumonia NEC Vocal Paralysis Bilateral Gangrene Staph Aureus Pneumonia 9

10 Comorbidity Analyses The NRS minimum data set does not include ICD-9 or ICD-10 diagnostic codes. The NRS data items that are most comparable, and available to examine comorbidities, were the Diagnostic Health Condition (DHC) codes. DHC are high-level diagnostic categories that do not map to ICD-9 codes on a one-to-one basis. As a component of this analysis, clinical experts conducted a code-by-code review of the DHC whose purpose was to map DHC to ICD-9 codes. The intent was two-fold: to evaluate whether comorbidity tiers (as defined by ICD-9 codes) described variation in Ontario inpatient rehabilitation LOS; and to assess whether DHC could be used to develop a comorbidity adjustment. 3.3 Objective 3: Develop Cost Weights for New Patient Classification System Data Source The data used to develop cost weights for Rehabilitation Patient Groups (RPG) was drawn from two sources of Ontario data. The first was clinical activity from the NRS from fiscal years 2003/2004 and 2004/2005. The second source was the Ontario Cost Distribution Methodology (OCDM); an Ontario-specific methodology for allocating a hospitals costs across discrete (and comparable) service recipient categories. The categories include: acute inpatient and newborn, rehabilitation, day surgery, chronic and respite care, ELDCAP, mental health inpatient, mental health outpatient, emergency outpatient, hospital outpatient and other hospital or community outpatients. The allocation to categories is dependent upon financial and statistical data provided in hospital MIS trial balance submissions Analyses The objective was to describe total inpatient rehabilitation costs reported in the OCDM as a function of the observed case mix of each hospital. To this end, cost weights, which serve as the case mix adjustment, were required for relate the mean RPG costs to the overall population mean episode cost. A system of linear equations, based on hospital NRS and OCDM financial data, was used to develop cost weights. From the clinical activity in the NRS, the number of patient days in each RPG was calculated for each hospital. From the OCDM, the total inpatient rehabilitation costs were available for each hospital. In the system Ax=b, the matrix A represented the number of patient days in each RPG and the vector b was the total inpatient rehabilitation costs. Each row in A represents a hospitals NRS data, where each column entry was the number of patient days in the column s RPG. Each row of vector b was a hospitals total inpatient rehabilitation costs. The vector x, of length equal to the number of RPG, represented the estimated cost per day of each RPG. The system of equations was solved for x to minimize the sum of the squared errors between the total cost, b, and estimated total cost. The solution represents the estimated cost per day for each RPG. 10

11 Then, the RPG cost per day is multiplied by the mean length of stay per RPG to estimate the mean episode cost (in each RPG.) Each mean RPG cost is normalized to the mean cost per episode, obtained from dividing the total OCDM inpatient rehabilitation costs by the number of episodes, to derive the relative cost weight for each RPG. The RPG cost weights are known as Rehabilitation Cost Weights (RCW.) The CMG classification system recognized that a small portion of patients, after adjusting for case mix differences, do not have a typical care pattern. These patients were identified by having extremely long length of stays; a similar pattern of extremely long length of stays was observed in the NRS data. In the RPG classification system, LOS trim points were established based on the observed distribution of patient length of stays. An episode whose LOS exceeded the trim point was deemed a long stay outlier. The LOS trim point was established for each RPG and represented the 98% percentile LOS. Recommendation #1: Establish LOS trim points annually. Each day of stay beyond the trim point is weighted with a per diem cost weight, known as per diem rehabilitation cost weight (PDRCW). The PDRCW varies by rehabilitation group, and is the same for all RPG in each Rehabilitation Group. Recommendation #2: Establish PDRCW for each RPG when case cost data becomes available. There were cases with atypically short length of stay relative to all patients in an RPG. Independent from RPG assignment, these were identified as cases with LOS of 3 days or less. A separate weighting methodology was developed for these cases to reflect their atypical resource utilization profile. 11

12 4 RESULTS 4.1 Objective 1: Performance of CMG and FRG with Ontario NRS data Table 3 compares the FRG and CMG when applied to Ontario 2002/2003 and 2003/2004 NRS data. In this analysis the dependent variable was the natural logarithm of length of stay and the goal was to examine the ability of both FRG and CMG in describing the variation in LOS. The coefficient of variation (CV) is a measure of the variation relative to the mean. A high number indicates that the variation was high relative to the average and a low number indicates that the variation was low relative to the average. Looking first at the CV for FRG, it ranged from 14.2 for burns to 29.3 for other disabilities. For CMG, the CV was similarly lowest for burns (15.2) but highest for replacement of lower extremity (34.5). For each RG, the CV was higher for CMG compared to FRG. This indicated that the variation relative to the mean in that system was greater compared to the FRG system. This may, in part, be explained by the lower sample sizes in the CMG groups in the Ontario data. The R 2 statistic is a measure of the percent of variance in the LOS that can be explained by the patient group. In the FRG system, the percent of variance explained ranged from 0.3% to 33.6%, and in the CMG system it ranged from 1.0% to 34%. The two systems explained a similar amount of variation for a number of groups (non-traumatic brain injury non-traumatic spinal cord injury, replacement of lower extremity, osteoarthritis, pain, pulmonary, burns, major multiple trauma, and other disabilities). CMG performed marginally better for stroke, neurological, amputation (lower extremity), fracture (lower extremity), other orthopedic, and cardiac. FRG performed significantly better for traumatic brain injury, traumatic spinal cord injury, and rheumatoid arthritis and other arthritis. 12

13 Table 3. Summary statistics of model fit for FRG and CMG. Rehabilitation Group FRG CMG CV R 2 CV R 2 Stroke % % Traumatic Brain Injury % % Non-Traumatic Brain Injury % % Neurological % % Traumatic Spinal Cord Injury % % Non-Traumatic Spinal Cord Injury % % Amputation, Non-Lower Extremity na na % Amputation, Lower Extremity % % Osteoarthritis % % Rheumatoid arthritis and Other Arthritis % % Pain % % Fracture of Lower Extremity % % Replacement of Lower Extremity % % Other Orthopedic % % Cardiac % % Pulmonary % % Burns % % Major Multiple Trauma, Other Multiple % % Trauma and Major Multiple Fracture Major Multiple Trauma, with Brain or % % Spinal Cord Injury Other Disabilities % % 4.2 Objective 2: Develop Patient Classification System Using Ontario Data Rehabilitation Patient Group Logic At the highest level, patients were assigned to a unique Rehabilitation Group (RG), which are collections of clinically similar patients. Assignment to an RG was based on Rehabilitation Client Group code in the NRS dataset. There were 21 RG shown previously in Table 1. Within each RG, patients were assigned to a unique Rehabilitation Patient Group (RPG) based on admission FIM motor scores, admission FIM cognitive scores and patient age. There were 83 RPG in total, shown in Appendix #2. Figure #1 below provides a picture of the patient group logic for the stroke rehabilitation group. Starting at the left side, the first decision point is a split on motor score greater or less than 51. If a patient has a motor score of then the next decision point further subdivides based on motor scores between and If a patient has a motor score between 39 and 51 then RPG 1120 is assigned. If a patient has a motor between 12 and 38, then age becomes a factor. A patient with a motor score between and older than age 69 is assigned RPG 1110, while a patient aged less than 69 within the same motor score range is assigned RPG

14 RPG Motor Stroke > 50 Motor Cognitive < 30 Cognitive Motor Age >= <= Figure #1: Decision Tree for Stroke Rehabilitation Patient Groups In the development of the RPG classification system, we also investigated other clinical data items available in the NRS minimum dataset. The objective was to identify data items that contributed to describing variation in LOS. The items investigated for inclusion in the classification methodology were: Verbal or non-verbal communication Written expression communication Auditory or non-auditory Reading comprehension The primary consideration in these investigations was whether the additional data items were able increase the percent of variation explained in LOS. At the same time, completeness and accuracy of the data items were considered. Recommendation #3: As the data becomes available, further revisions of the RPG classification methodology should investigate the relationship between additional NRS data items and episode cost. 14

15 The ability of the model to describe variation in length of stay based on r-squared and the coefficient of variation is shown in Table #4. Table 4. Summary statistics of model fit for RPG Rehabilitation Group RPG CV R 2 Stroke % Traumatic Brain Injury % Non-Traumatic Brain Injury % Neurological % Traumatic Spinal Cord Injury % Non-Traumatic Spinal Cord Injury % Amputation, Non-Lower Extremity % Amputation, Lower Extremity % Osteoarthritis % Rheumatoid arthritis and Other Arthritis % Pain % Fracture of Lower Extremity % Replacement of Lower Extremity % Other Orthopedic % Cardiac % Pulmonary % Major Multiple Trauma, Other Multiple % Trauma and Major Multiple Fracture Major Multiple Trauma, with Brain or % Spinal Cord Injury Other Disabilities % With respect to the coefficient of variation, it was of very similar magnitude to that shown in the CMG system for each rehabilitation group. On the other hand, based on the R 2 statistic, RPG performed better than both CMG and FRG in terms of the amount of variance explained in many RG. There were two rehabilitation groups in which RPG did not explain more variance; the FRG system explained slightly more variance for traumatic spinal cord injury, and both the FRG and CMG system explained more variance for rheumatoid arthritis and other arthritis Comorbidities Within the NRS, there were 496 DHC. Based on extensive clinical review, 27 (5.4%) mapped directly back to ICD-9-CM codes that define comorbidity tiers in the CMG methodology. Among those DHC that did map directly, 13 fell into Tier 2, while 14 fell into Tier 3. Since many DHC were not specific, most DHC mapped to a range of ICD- 9/ICD-10 codes and assignment to a single ICD-9-CM code could not be made. CIHI has been successful in mapping ICD-10 codes forward to DHC. However, this mapping results in a heterogeneous mixture of ICD-10 codes to DHC. Recommendation #4: DHC, as coded in the NRS, were not specific enough to be used to identify comorbid conditions having a significant impact on variation of LOS. Future work must include the improvement of data elements identifying comorbid conditions to facilitate the development of a comorbidity adjustment. 15

16 4.3 Objective 3: Develop Cost Weights Using Ontario Data Rehabilitation Cost Weights (RCW) In order to be relevant to resource allocation comparisons and funding methodologies, it was important to have cost weights for each of the 83 RPG developed in objective #2. The cost weight was meant to represent an average relative resource use of patients in an RPG (excluding short stay and long stay outliers.) Appendix #3 shows the complete list if rehabilitation cost weights (RCW.) Recommendation #5: Calculate new RCW each fiscal year. As mentioned previously, the unit of analysis was episode of care, therefore an episode received the RCW at discharge only. In the case where an episode crossed fiscal years, the RCW was assigned to the fiscal year of discharge (as opposed to admission FIM.) Analysis of NRS data from 2002/2003, 2003/2004 and 2004/2005 demonstrated the impact of weighting cases upon discharge (as opposed to the fiscal year of admission) on most facilities was very small. No relationship between different discharge reason codes and variation in LOS was found in the data and therefore, it was not considered further when RCW was assigned. Recommendation #6: Evaluate the affect of discharge reason code on patient level cost data annually. 4.4 Application of RCW At discharge, each patient episode was assigned an RCW. Several factors affected the patients assignment of an RCW. The most important factor was the assigned RPG, as each RPG has a unique RCW. The second factor was LOS since it determined whether the episode was weighted as either a: Short stay outlier; or a Long stay outlier. Episodes with LOS equal to, or less than 3 days, were assigned the same short stay RCW, or The short stay weight does not vary by RG or RPG. Episodes with LOS greater than 3 days, or less than (or equal to,) the trim point assumed the RPG RCW. An episode whose LOS exceeded the trim point was deemed a long stay outlier. Each RPG has a unique LOS trim point (shown in Table 5.) Long stay outliers were assigned a cost weight which was the sum of the RCW and the number of days beyond the trim point. The formula for determining long stay outlier weight is: Cost Weight = RCW + (LOS Trim Point) * PDRCW. 16

17 As an example, consider a patient assigned RPG 1130, Stroke (M=51-84 and C=5-25), whose LOS is 100 days. The RCW for this RPG was The PDRCW is and the trim point is 90 days. The cost weight for this episode is then: Cost Weight = (100 90) * = Recommendation #7: Review the methodology used to assign cost weights to long LOS outliers. The cost per weighted case for inpatient rehabilitation can be calculated in the same manner as acute inpatient; divide the total inpatient rehabilitation costs (from the OCDM) by the sum of a facility s RCW. The CPWC was calculated for all facilities and is shown in Appendix 4, Summary of 2004/2005 NRS activity. 4.5 Length of Stay and Application of RCW An RCW was intended to represent an entire episode, inclusive of service interruptions and days waiting for discharge. Using the available clinical and financial data, we could not find a relationship between episode cost and service interruptions. Until more specific data becomes available, we do not recommend adjusting RCW for service interruptions. Recommendation #8: Review the methodology used to assign cost weights to episodes with service interruptions. 17

18 5 DISCUSSION The analyses undertaken for this project have identified that the new RPG classification system, developed using Ontario data is the classification system of choice for adult inpatient rehabilitation activity. The new RPG classification system explained more variance than either CMG or FRG among almost all rehabilitation groups. Additionally, there are non-statistical reasons for adopting the new grouper; use of a grouper developed based on local utilization patterns, rather than those experienced in the United States, may be more acceptable to rehabilitation providers. 5.1 Comorbidities With regard to comorbidities, analyses revealed that the data available was not of sufficient granularity to develop a comorbidity adjustment. One option discussed was to accept and apply the tiers used in the CMG methodology, however due to the lack of specificity in the coding of diagnostic health conditions this is not possible. Future enhancements to the NRS system should consider increasing the specificity of diagnostic health conditions, possibly by using ICD-10 codes. 5.2 Further Development Specific subpopulations were subjectively identified as having atypical cost profiles that would not be reflected in RCW. However, the absence of case cost data prevents further refinement of RCW at this time. As soon as case cost data becomes available, a priority is to evaluate the cost profiles of: Neurobehavioural patients; Diabetic patients (requiring dialysis); and Geriatric psych rehabilitation. Recommendation #9: Develop methods to adjust hospital statistics (weighted cases and CPWC) for specialty populations external to the RPG case mix classification methodology. The data does not exist to identify, or adjust for, specialty populations at this time. To appropriately reflect the cost of these patients, the JPPC RWG proposes either of: Remove these patients from CPWC calculations. This option involves appropriately identifying neurobehavioral patients and removing their activity and cost. The JPPC RWG recommends that CIHI mandate a data item to identify these patients and that patient costs attributable to these patients are removed from the inpatient rehabilitation OCDM costs.. Adjust CPWC for neurobehavioral patients (no specific methodology proposed) in hospital with established neurobehavioral programs (London St Josephs, Hamilton, Toronto WestPark and Ottawa Hospital.) 18

19 5.3 Data Quality Issues Numerous data quality issues were identified when comparing patient days calculated within the NRS system to those reported in the OCDM. Patient days in the NRS system were calculated at a facility level by summing LOS for each episode in the fiscal year. Patient days in the OCDM system are reported by each facility to the Ministry of Health and Long Term Care. In some cases, there was a large difference between NRS days and OCDM days. These differences are shown in Appendix #4 and Appendix #5, comparing OCDM Patient Days and Total NRS LOS. Some discrepancy was expected due to the timing of the data cut used for analysis, and due to the fact that some episodes crossed fiscal years. The impact of this was minimized by taking a February cut of the data from CIHI. In follow-up with those facilities with the greatest discrepancies, a number of reasons for the differences were identified. In some cases facilities were having difficulties with their software systems and finding errors in the form of missing assessments in their transmission files. These errors were being corrected, but future reports from CIHI regarding patient days in the NRS system would be helpful in flagging this problem early. Another reason for discrepancy in days was due to how facilities were using rehabilitation designated beds. In some cases rehab beds were being used as offservice beds. In this case an OCDM day was recorded as rehabilitation, but an NRS assessment was not completed. Use of rehabilitation designated beds must be in line with the mandated reporting system. Lastly, some facilities reported difficulty collecting the NRS data due to staff shortages. This error led to an underreporting of patient days in the NRS system for those facilities. An under reporting of NRS days will influence funding in a funding formula environment. Once again, patient day reports from CIHI would facilitate the monitoring of this under reporting. Recommendation #10: CIHI should include a measure of patient days in their quarterly reports that facilities can compare to their OCDM days. 19

20 6 SUMMARY OF RECOMMENDATIONS 1. Establish length of stay (LOS) trim points annually. 2. Establish per diem rehabilitation cost weights (PDRCW) for each rehabilitation patient group (RPG) when case cost data becomes available. 3. As the data becomes available, further revisions of the RPG classification methodology should investigate the relationship between additional NRS data items and episode cost. 4. Diagnostic Health Conditions (DHC), as coded in the NRS, were not specific enough to be used to identify comorbid conditions having a significant impact on variation of LOS. Future work must include the improvement of data elements identifying comorbid conditions to facilitate the development of a comorbidity adjustment 5. Calculate new rehabilitation cost weights (RCW) each fiscal year. 6. Evaluate the affect of discharge reason code on patient level cost data annually. 7. Review the methodology used to assign cost weights to long LOS outliers. 8. Review the methodology used to assign cost weights to episodes with service interruptions. 9. Develop methods to adjust hospital statistics for specialty populations outside of the RPG case mix classification methodology. 10. CIHI should include a measure of patient days in their quarterly reports that facilities can compare to their OCDM days. 20

21 7 ACKNOWLEDGEMENTS The authors would like to acknowledge the contributions of the JPPC Rehabilitation Technical Working Group committee members, Clinical Focus Group members, Technical Focus Group members and individual contributions from Dan Hill and Dionne Williams of Bridegepoint Health, Toronto. Dr. Sutherland would also like to acknowledge the support of the MOHLTC throughout this project. 21

22 8 REFERENCES Breiman L., Friedman J.H., Olshen R.A., and Stone, C.J., (1984). Classification and Regression Trees. Wadsworth International Group. Buchanan, J. L., P. Andres, S. Haley, S. M. Paddock, D. C. Young, and A. Zaslavsky (2002). Final Report on Assessment Instruments for PPS. Santa Monica, CA: RAND, MR-1501-CMS. Carter, G.M. and Paddock, S.M. (2005) Preliminary Analyses of Changes in coding and case mix under the inpatient rehabilitation facility prospective payment system. TR-213- CMS. Santa Monica, CA: RAND. Carter, G.M. and Totten, M. (2005) Preliminary analyses for refinement of the tier comorbidities in the inpatient rehabilitation facility prospective payment system. TR- 201-CMS. Santa Monica, CA: RAND Carter, G. M., D. A. Relles, B. O. Wynn, J. Kawata, S. M. Paddock, N. Sood, and M. E. Totten (2002). Interim Report on an Inpatient Rehabilitation Facility Prospective Payment System. Santa Monica, CA: RAND, MR-1503-CMS. Carter, G. M., M. Beeuwkes Buntin, O. Hayden, J. Kawata, S. M. Paddock, D. A. Relles, G. K. Ridgeway, M. E. Totten, and B. O. Wynn (2001). Analyses for the Initial Implementation of the Inpatient Rehabilitation Facility Prospective Payment System. Santa Monica, CA: RAND, MR-1500-CMS. Paddock, S.M., Carter, G.M., Wynn, B.O. and Zhou, A.J. (2005) Possible Refinements to the facility-level payment adjustments for the inpatient rehabilitation facility prospective payment system. TR-219-CMS. Santa Monica, CA: RAND Relles, D. A., and G. M. Carter (2002). Linking Medicare and Rehabilitation Hospital Records to Support Development of a Rehabilitation Hospital Prospective Payment System. Santa Monica, CA: RAND, MR-1502-CMS. 22

23 Appendix #1: Status of the Inpatient Rehabilitation Facility Prospective Payment System (IRF-PPS) in the United States The Balanced Budget Act of 1997 provided for the implementation of a prospective payment system (PPS) for inpatient rehabilitation activity in rehabilitation hospitals or rehabilitation units of a hospital providing care to medicare recipients. The Centers for Medicare and Medicaid Services (CMS) is a federal agency in the U.S. that administers the Medicare program. Medicare is the national health insurance program for people who are aged 65 or older, some individuals younger than 65 with a disability, and people with end-stage renal disease. The IRF PPS was implemented in January 2002 and is described in the August 7, 2001 final rule in the Federal Register (66FR at 41316). At that time, the PPS used 100 casemix groups (CMGs) as developed by Carter et al. (2001) using fiscal year 1998 and 1999 data. Ninety-five CMGs were based on 21 rehabilitation impairment categories (RICs). An additional five categories were constructed for those patients with very short lengths of stay and those who expired during their rehabilitation stay. The CMGs were constructed using classification and regression tree (CART) analysis. In addition to RICs, patient age, and functional status at admission were used to classify patients into groups that were homogeneous with respect to resource use. Within each CMG, three tiers of comorbidities were established. Relative weights were assigned based on resource need at the tier level, and applied to the standard payment conversion factor to reach an unadjusted Federal prospective payment rate. Once the payment rates were established, additional adjustment for geographic variations in wages, percentage of low-income patients, and location in a rural area were applied. The resulting adjusted payment provided for inpatient operating and capital costs, not including educational activities, bad debts, and other services outside the scope of the PPS. Updates to the payment rates have been made using the same classifications and weighting factors for subsequent fiscal years. The Balanced Budget Act provided for refinements to the PPS over time. Refinements could be necessary for a number of reasons. For example, since payment is independent of actual service received, there exists some incentive for facilities to maximize their budget positions by being cost efficient. In addition, over time, more recent data better reflects practice patterns of the time and changes in coding behaviour. As a result, CMS contracted with RAND to examine possible refinements to the IRF- PPS. That work examined the performance of the classification system using 2003 data. In total, the classification system, including CMGs and relative weights, coding changes and facility level adjustments were examined. RAND made a number of recommendations specific to the US system. Of particular note among those was the recommendation that the standard payment rate be reduced by 1.9 percent to account for coding changes. As well, there was a recommendation to implement a teaching status adjustment. 23

24 A number of recommendations were made with respect to refinements of the classification system. There was a recommendation to use a weighted motor score index which was shown to better predict costs than a motor score that treated all components equally. Further, to better align payments with cost the CMGs were updated resulting in a reduction in the number of patient groups from 95 to 87. This reduction was primarily due to a reduction in the number of patient groups within the stroke RIC. In addition, there were recommendations to change the list of tier comorbidities. For example, there were recommendations to remove some codes that were not associated with an increase in treatment costs, and to move dialysis to the tier associated with the highest payment. In addition to these recommendations, CMS made some changes to geographic boundaries. All changes to the PPS appear in a final ruling in the Federal Register on August 15, 2005 (42 CFR Part 412 at 47880). The changes are to take effect October 1, The final rule can be found at: 24

25 Appendix #2: RG. Data splits for Rehabilitation Patient Groups within Rehabilitation Group (RG) Rehabilitation Patient Group (RPG) 1. Stroke M=12-38 and Age<= M=12-38 and Age>= M= M=51-84 and C= M=51-84 and C= M=51-68 and C= M=69-84 and C= Traumatic Brain Injury M=12-13 and C= M=14-47 and C= M=48-84 and C= M=12-44 and C= M=45-84 and C= M=12-84 and C= Non-Traumatic Brain Injury C= C=22-32 and Age <= C=22-32 and Age>= C= Neurological M= M= M= M= Traumatic Spinal Cord Injury M= M=17-41 and Age <= M=17-41 and Age >= M= Non-Traumatic Spinal Cord Injury M= M=29-54 and Age >= M=29-54 and Age<= M= M= Amputation, Non-Lower Extremity M= M= Amputation, Lower Extremity M= M= M=65-84 and C= M=65-84 and C= Osteoarthritis M= M= Rheumatoid Arthritis and Other Arthritis M= M= Pain M= M= Fracture of Lower Extremity M=12-47 and Age >= M=12-30 and Age <= M=31-47 and Age <= M= M=52-84 and Age >= M=52-84 and Age <= Replacement of Lower Extremity M=12-53 and C= M=12-53 and C= M=54-68 and C= M=54-60 and C=

26 2340. M=61-68 and C= M= Other Orthopedic M=12-51 and C= M=12-51 and C= M=52-64 and C= M=52-64 and C= M= Cardiac M=12-49 and C= M=12-49 and C= M=50-67 and Age <= M=68-84 and Age <= M=50-84 and Age >= Pulmonary M=12-36 and Age >= M=37-84 and Age >= M=15-33 and Age <= M=34-35 and Age <= Burns M=12-84 and C= Major Multiple Trauma, Other Multiple Trauma and Major Multiple Fracture 19. Major Multiple Trauma, with Brain or Spinal Cord Injury M= M=25-55 and Age <= M=25-48 and Age >= M=49-55 and Age >= M= M= M= M= M=12-84 and C= Ventilator Dependent Respiratory Disorders 21. Other Disabilities M= M= M=59-84 and Age <= M=59-84 and C=5-33 and Age >= M=59-84 and C=34-35 and Age >= 59 26

27 Appendix #3: Rehabilitation Cost Weights based on 04/05 NRS and OCDM data Rehabilitation Group Rehabilitation Patient Group Rehabilitation Cost Weight Trim Point Per Diem Rehabilitation Cost Weight Stroke Stroke Stroke Stroke Stroke Stroke Stroke Traumatic Brain Injury Traumatic Brain Injury Traumatic Brain Injury Traumatic Brain Injury Traumatic Brain Injury Traumatic Brain Injury Non-Traumatic Brain Injury Non-Traumatic Brain Injury Non-Traumatic Brain Injury Non-Traumatic Brain Injury Neurological Neurological Neurological Neurological Traumatic Spinal Cord Injury Traumatic Spinal Cord Injury Traumatic Spinal Cord Injury Traumatic Spinal Cord Injury Non-Traumatic Spinal Cord Injury Non-Traumatic Spinal Cord Injury Non-Traumatic Spinal Cord Injury Non-Traumatic Spinal Cord Injury Non-Traumatic Spinal Cord Injury Amputation, Not Lower Extremity Amputation, Not Lower Extremity Amputation, Lower Extremity Amputation, Lower Extremity Amputation, Lower Extremity Amputation, Lower Extremity Osteoarthritis Osteoarthritis Rheumatoid arthritis and Other Arthritis Rheumatoid arthritis and Other Arthritis Pain Pain