Inpatient Rehabilitation Facilities: Alternative Definitions of Hospital Specialization  Presented by Roberta Constantin

Inpatient Rehabilitation Facilities: Alternative Definitions of Hospital Specialization Presented by Roberta Constantin PowerPoint PPT Presentation


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2. Introduction. About 8 percent of all hospital users are admitted to IRFs for PAC services (over 470 thousand admissions between June 30th of 2004 and 2005.) These specialized hospitals provide acute-level treatment in physical medicine and rehabilitation. IRF patients typically have a primary d

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Inpatient Rehabilitation Facilities: Alternative Definitions of Hospital Specialization Presented by Roberta Constantin

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1. Inpatient Rehabilitation Facilities: Alternative Definitions of Hospital Specialization Presented by Roberta Constantine, RN, MBA, PhD RTI International Presented at AcademyHealth 2007 Annual Research Meeting June 4, 2007 Orlando, Florida

2. 2 Introduction About 8 percent of all hospital users are admitted to IRFs for PAC services (over 470 thousand admissions between June 30th of 2004 and 2005.) These specialized hospitals provide acute-level treatment in physical medicine and rehabilitation. IRF patients typically have a primary diagnosis related to musculoskeletal or neurological disorders or injuries and need acute-level physical rehabilitation treatments.

3. 3 Medicare Policy In 2002, IRFs moved from TEFRA cost-based to case-mix, adjusted PPS. Hospitals must meet IRF certification requirements to be paid under the IRF PPS, else IPPS with much lower rates. To qualify for IRF certification, a hospital or unit must have admitted during its last cost reporting period, a certain percentage of cases within 13 diagnostic groups (75% Rule).

4. 4 IRF Certification Groups Stroke Spinal cord injury, Congenital deformity, Amputation, Fracture of femur (hip fracture), Brain injury, Neurological disorders Burns Systemic vasculidities Knee/Hip replacement Active rheumatoid arthritis or severe/ advanced osteoarthritis certain lupus conditions (polymyositis), disorders involving inflammation and blood vessels known as the vasculitides or the vasculitic syndromes certain lupus conditions (polymyositis), disorders involving inflammation and blood vessels known as the vasculitides or the vasculitic syndromes

5. 5 Study Purpose Are there alternative ways of defining IRF specialization that would allow for changes in medical practice?

6. 6 Research Questions Do individual inpatient rehabilitation facilities (IRFs) specialize in certain populations? If so, what types of admissions, who are the patient populations and what types of IRFs are specializing? If specialization does occur does it affect patient outcomes?

7. 7 Methods We analyzed data from: Inpatient Rehabilitation Facility Patient Assessment Instrument (IRFPAI), Patients’ prior acute, inpatient claims, and Provider certification data to examine whether other factors might be useful in classifying IRFs. Focus on the relationship between patient diagnosis, IRF characteristics, and functional outcomes.

8. 8 IRF Characteristics Rehab Unit (%) Freestanding(%) Total(%) No. of Facilities 970 (82) 210 (18) 1,180 (100) No. of Admits 302,524 (68) 168,171 (36) 470,695 (100) Mean Admits 312 801 399 Median Admits 260 766 290 Nonprofit 668 (69) 71 (34) 739 (63) For Profit 159 (16) 131 (62) 290 (25) Although rehab units within hospitals account for 82% of IRFs and 68% of admissions, their average admission is far lower than freestanding units due to bedsize differences. 54% of rehab units had 20 beds or less, 97% had 60 beds or less. 45% of freestanding IRFs had 60 to 90 or more beds. Also, rehab units were more likely to be non profits while the majority of freestanding units were for profit. The category we omitted was government which was a small percentage of IRFs.Although rehab units within hospitals account for 82% of IRFs and 68% of admissions, their average admission is far lower than freestanding units due to bedsize differences. 54% of rehab units had 20 beds or less, 97% had 60 beds or less. 45% of freestanding IRFs had 60 to 90 or more beds. Also, rehab units were more likely to be non profits while the majority of freestanding units were for profit. The category we omitted was government which was a small percentage of IRFs.

9. 9 Rehabilitation Impairment Categories (RICs) Percent of Total Frequency Admissions Replace of Lower Extremity Joint 108,339 23% Stroke 84,114 18% Fracture of the Lower Extremity 64,847 14% Neurological 26,106 5.5% Burns 328 0.07% This slide displays the number of admissions for a specific RIC as a percentage of total IRFs admissions that we analyzed. There were differences among RICs by age, gender, LOS, FIM score on admission, patient severity of illness.This slide displays the number of admissions for a specific RIC as a percentage of total IRFs admissions that we analyzed. There were differences among RICs by age, gender, LOS, FIM score on admission, patient severity of illness.

10. 10 Top IRFs with Highest Number of RIC Admits RIC Admits as % Total Admits RIC Admits of Total IRF Admits ReplacLE 5,343 1,612 30% Stroke 5,343 863 16% FracLE 5,343 550 10% Neuro 2,032 703 35% Burns 1,046 58 6% To begin our analysis, at the IRF level, we wanted to examine the highest number of admissions for a particular RIC – thus high volume. The middle column represents the IRF with the highest number of admissions for that specific RIC. Next, we examined that number in respect to the IRFs total number of admissions – the left hand column Finally, we examined the number of RIC admissions as a % of their total admits – the right hand column. Compared to the national distribution on the previous slide- ReplacLE 23%, stroke 18%, fracLE 14%, Neuro 5.5.%, and burns <1%, there does appear to be what one might consider specialization. To begin our analysis, at the IRF level, we wanted to examine the highest number of admissions for a particular RIC – thus high volume. The middle column represents the IRF with the highest number of admissions for that specific RIC. Next, we examined that number in respect to the IRFs total number of admissions – the left hand column Finally, we examined the number of RIC admissions as a % of their total admits – the right hand column. Compared to the national distribution on the previous slide- ReplacLE 23%, stroke 18%, fracLE 14%, Neuro 5.5.%, and burns <1%, there does appear to be what one might consider specialization.

11. 11 FIM Scores by IRF Affiliation Units RIC ADM CHG ReplaceLE 77 26 Stroke 58 22 FracLE 65 24 Neuro 66 20 Burns 62 19 Freestanding ADM DC 72 31 49 25 57 28 58 26 57 29

12. 12 Creation of New Variables to Characterize Specialization Examined both the number of admissions for a RIC (RIC admits) and the number of admissions for a particular RIC as a percent of the facility’s total admissions (RIC_PCNT) simultaneously. RIC admits RIC_PCNT Quantiles 0-50 51-100 0-50 51-100 Low, Low X X Low, High X X High, Low X X High, High X X We then created a new set of variables to examine both number of RIC admissions (volume) and the number of RIC admits as a % of total admissions for a facility. The restriction was that an IRF had to have at least one admission.We then created a new set of variables to examine both number of RIC admissions (volume) and the number of RIC admits as a % of total admissions for a facility. The restriction was that an IRF had to have at least one admission.

13. 13 Frequency Distribution and Percentage ( ) of Specialization Variables LL LH HL HH ReplacLE 527 (46) 340 (30) 74 (7) 209 (18) Stroke 417 (35) 480 (41) 127 (11) 155 (13) FracLE 428 (37) 449 (39) 99 (9) 182 (16) Neuro 548 (48) 262 (23) 61 (5) 267 (24) Burns 85 (59) 14 (10) 21 (15) 23 (16) Examining the frequency distribution by RIC, on an IRF level, we can detect some differences. Overall, LL was the most frequent but ranged from 35 to 59%. LH was the next frequent, then HH. Of note is for Neuro, 24% of IRFs fell into the HH category and only 5% in the HL category.Examining the frequency distribution by RIC, on an IRF level, we can detect some differences. Overall, LL was the most frequent but ranged from 35 to 59%. LH was the next frequent, then HH. Of note is for Neuro, 24% of IRFs fell into the HH category and only 5% in the HL category.

14. 14 Functional Improvement Measure (FIM) The FIMTM instrument is “a measure of disability not impairment” (IRF-PAI Training Manual, 2004). It is a basic indicator of the severity of disability intended to measure what a patient actually does, regardless of the impairment, not what they should be able to do. There are 18 FIM items in all, scored from 1 total assist to 7 independent. There are six FIM categories; self-care, sphincter control, transfers, locomotion, communication, and social cognition Our next step was to run some OLS models but I wanted to review two variables in the models. Our outcome measure for our models was the FIM score. In IRF, the goal is to increases a patient’s FIM score.Our next step was to run some OLS models but I wanted to review two variables in the models. Our outcome measure for our models was the FIM score. In IRF, the goal is to increases a patient’s FIM score.

15. 15 IRFs with Highest Percentage of RIC Admits as Percentage of Total Admits RIC Admits as % Total Admits RIC Admits of Total IRF Admits ReplacLE 630 439 71% Stroke 78 73 96% FracLE 69 35 51% Neuro 385 190 49% Burns 97 39 40% This next slide analyzes specialization another way- looking at the top IRF when RIC volume was expressed as a % of their total admissions. Compared to the national distribution on the previous slide- ReplacLE 23%, stroke 18%, fracLE 14%, Neuro 5.5.%, and burns <1%, there does appear to be what one might consider specialization. This next slide analyzes specialization another way- looking at the top IRF when RIC volume was expressed as a % of their total admissions. Compared to the national distribution on the previous slide- ReplacLE 23%, stroke 18%, fracLE 14%, Neuro 5.5.%, and burns <1%, there does appear to be what one might consider specialization.

16. 16 Patient Severity of Illness (SOI) Patient SOI was estimated during the index acute care, inpatient hospitalization using the All Patient Refined DRG (APR-DRG) grouping software developed by 3M Health Information Systems. The SOI subclass has values of 0-4 corresponding to: no relevant comorbidities (0), minor severity (1), moderate severity (2), major severity (3), or extreme severity (4). A match was performed on IRF-PAI admissions to MEDPAR acute care admissions and a total of 370,308 claims matched the 470,695 IRFPAI records (78.7%). 370,308 IRFPAI assessments were used for the models adding on the SOI variable from the previous acute care hospitalization.370,308 IRFPAI assessments were used for the models adding on the SOI variable from the previous acute care hospitalization.

17. 17 OLS Models for FIM Outcomes for Selected RICs Dependent Variables: Total FIM Change Score, Total Motor Change Score (self-care, sphincter control, transfers, and locomotion), Total Cognitive Change Score (communication and social cognition) Independent Variables: Patient Characteristics – age, gender, white, severity of illness, LOS, Total FIM admission score IRF Characteristics – Region, Total Admissions, Rehab Unit Indicator, Specialization variables We decided to examine 3 FIM change score – derived by subtracting the admission FIM score from discharge to obtain the FIM change score. We also focused on Motor and Cognitive scores as well to detect any differences. The average change score across all RICs was 25, Neuro and burns 22 and ReplaLE 27 We decided to examine 3 FIM change score – derived by subtracting the admission FIM score from discharge to obtain the FIM change score. We also focused on Motor and Cognitive scores as well to detect any differences. The average change score across all RICs was 25, Neuro and burns 22 and ReplaLE 27

18. 18 Model Results – Adjusted R-Squared Total FIM Total Motor FIM Total Cognitive FIM Change Score Change Score Change Score ReplacLE 0.35 0.14 0.25 Stroke 0.12 0.11 0.11 FracLE 0.13 0.11 0.14 Neuro 0.09 0.06 0.10 Burns 0.15 0.16 0.09 Our results demonstrate that a modest change in the FIM change scores could be explained by the model with the exception of ReplacLE when 35% of the variation in FIM change score could be detected by the model for the total change score and 25% for the Cognitive change score. Our results demonstrate that a modest change in the FIM change scores could be explained by the model with the exception of ReplacLE when 35% of the variation in FIM change score could be detected by the model for the total change score and 25% for the Cognitive change score.

19. 19 Summary of OLS Regressions, Dependent Variable, Total FIM Change Score This slide summarizes the models for Total FIM change score- when a variable was statistically significant the majority were at the level of P=< = 0.01 or less. I discuss the findings in more detail the next two slides.This slide summarizes the models for Total FIM change score- when a variable was statistically significant the majority were at the level of P=< = 0.01 or less. I discuss the findings in more detail the next two slides.

20. 20 Model Results IRF Characteristics Overall, specialization tended to be statistically significant (0.01 or less) but varied by direction, RIC, change score being measured, and specialization category. When the specialization variables were significant, they generally increased the FIM change score. Volume had the greatest influence: HL 1.6 points (Reple); 1.36 (Stroke); 1.7 (Neuro) High percentages in small admission groups were next most influential (LH) in general and especially in rarer cases 5.2 (burns); 0.67 (Reple); 0.69 (FracLE); 0.48 (Neuro) High volume, High percent had mixed results -0.37 (Stroke); -2.3 (Burns); 1.2 (Neuro) We also ran the models without the specialty variables and there was not significant changes in the adjusted r squared in all but one model. We also ran the models without the specialty variables and there was not significant changes in the adjusted r squared in all but one model.

21. 21 Model Results - Patient Characteristics Patient’s severity of illness for indexed acute care hospitalization was the most significant patient variable. Varied across RICs and models – ranged less than one to six points. Other patient characteristics: Female tended to have a positive affect. Age was significant and negative for most models but less than one point effect. Ethnicity – being white was generally significant and positive LOS tended to be significant and positive but less than one point across the models.

22. 22 Policy Implications Using definitions of functional and medical severity to determine appropriate IRF admissions would be consistent with the consideration that PAC is provided on a continuum. Volume and Percent of Admissions have significant effects on rehabilitation outcomes. These types of measures allow for variation within conditions to identify the acute-level rehabilitation case.

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