Melvin J. Ingber

Medicare post-acute care episodes and payment bundling.

BACKGROUND The purpose of this paper is to examine service use in an episode of acute and post-acute care (PAC) under alternative episode definitions and to look at geographic differences in episode payments. DATA AND METHODS The data source for these analyses was a Medicare claims file for 30 percent of beneficiaries with an acute hospital initiated episode in 2008 (N = 1,705,794, of which 38.7 percent went on to use PAC). Fixed length episodes of 30, 60, and 90 days were examined. Analyses examined differences in definitions allowing any claim within the fixed length period to be part of the episode versus prorating a claim extending past the episode endpoint. Readmissions were also examined as an episode endpoint. Payments were standardized to allow for comparison of episode payments per acute hospital discharge or PAC user across states. RESULTS The results of these analyses provide information on the composition of service use under different episode definitions and highlight considerations for providers and payers testing different alternatives for bundled payment.

Evaluating Hospital Readmission Rates After Discharge From Inpatient Rehabilitation

OBJECTIVE To examine facility-level rates of all-cause, unplanned hospital readmissions for 30 days after discharge from inpatient rehabilitation facilities (IRFs). DESIGN Observational design. SETTING Inpatient rehabilitation facilities. PARTICIPANTS Medicare fee-for-service beneficiaries (N=567,850 patient-stays). INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES The outcome is all-cause, unplanned hospital readmission rates for IRFs. We adapted previous risk-adjustment and statistical approaches used for acute care hospitals to develop a hierarchical logistic regression model that estimates a risk-standardized readmission rate for each IRF. The IRF risk-adjustment model takes into account patient demographic characteristics, hospital diagnoses and procedure codes, function at IRF admission, comorbidities, and prior hospital utilization. We presented national distributions of observed and risk-standardized readmission rates and estimated confidence intervals to make statistical comparisons relative to the national mean. We also analyzed the number of days from IRF discharge until hospital readmission. RESULTS The national observed hospital readmission rate by 30 days postdischarge from IRFs was 13.1%. The mean unadjusted readmission rate for IRFs was 12.4%±3.5%, and the mean risk-standardized readmission rate was 13.1%±0.8%. The C-statistic for our risk-adjustment model was .70. Nearly three-quarters of IRFs (73.4%) had readmission rates that were significantly different from the mean. The mean number of days to readmission was 13.0±8.6 days and varied by rehabilitation diagnosis. CONCLUSIONS Our results demonstrate the ability to assess 30-day, all-cause hospital readmission rates postdischarge from IRFs and the ability to discriminate between IRFs with higher- and lower-than-average hospital readmission rates.

What Are Nursing Facilities Doing to Reduce Potentially Avoidable Hospitalizations

OBJECTIVES Hospitalizations among nursing facility residents are frequent and often potentially avoidable. A number of initiatives and interventions have been developed to reduce excessive hospitalizations; however, little is known about the specific approaches nursing facilities use to address this issue. The objective of this study is to better understand which types of interventions nursing facilities have introduced to reduce potentially avoidable hospitalizations of long-stay nursing facility residents. DESIGN Cross-sectional survey. SETTING 236 nursing facilities from 7 states. PARTICIPANTS Nursing facility administrators. MEASUREMENTS Web-based survey to measure whether facilities introduced any policies or procedures designed specifically to reduce potentially avoidable hospitalizations of long-stay nursing facility residents between 2011 and 2015. We surveyed facilities about seven types of interventions and quality improvement activities related to reducing avoidable hospitalizations, including use of Interventions to Reduce Acute Care Transfers (INTERACT) and American Medical Directors Association tools. RESULTS Ninety-five percent of responding nursing facilities reported having introduced at least one new policy or procedure to reduce nursing facility resident hospitalizations since January 2011. The most common practice reported was hospitalization rate tracking or review, followed by standardized communication tools, such as Situation, Background, Assessment, Recommendation (SBAR). We found some variation in the extent and types of these reported interventions. CONCLUSIONS Nearly all facilities surveyed reported having introduced a variety of initiatives to reduce potentially avoidable hospitalizations, likely driven by federal, state, and corporate initiatives to decrease hospital admissions and readmissions.

Development of a Risk-adjustment Model for the Inpatient Rehabilitation Facility Discharge Self-care Functional Status Quality Measure

Background: Functional status measures are important patient-centered indicators of inpatient rehabilitation facility (IRF) quality of care. We developed a risk-adjusted self-care functional status measure for the IRF Quality Reporting Program. This paper describes the development and performance of the measure’s risk-adjustment model. Methods: Our sample included IRF Medicare fee-for-service patients from the Centers for Medicare & Medicaid Services’ 2008–2010 Post-Acute Care Payment Reform Demonstration. Data sources included the Continuity Assessment Record and Evaluation Item Set, IRF-Patient Assessment Instrument, and Medicare claims. Self-care scores were based on 7 Continuity Assessment Record and Evaluation items. The model was developed using discharge self-care score as the dependent variable, and generalized linear modeling with generalized estimation equation to account for patient characteristics and clustering within IRFs. Patient demographics, clinical characteristics at IRF admission, and clinical characteristics related to the recent hospitalization were tested as risk adjusters. Results: A total of 4769 patient stays from 38 IRFs were included. Approximately 57% of the sample was female; 38.4%, 75–84 years; and 31.0%, 65–74 years. The final model, containing 77 risk adjusters, explained 53.7% of variance in discharge self-care scores (P<0.0001). Admission self-care function was the strongest predictor, followed by admission cognitive function and IRF primary diagnosis group. The range of expected and observed scores overlapped very well, with little bias across the range of predicted self-care functioning. Conclusions: Our risk-adjustment model demonstrated strong validity for predicting discharge self-care scores. Although the model needs validation with national data, it represents an important first step in evaluation of IRF functional outcomes.

Using Medicare cost reports to calculate costs for post-acute care claims

ii