Development and Validation of an Administrative Claims-based Measure for All-cause 30-day Risk-standardized Readmissions After Discharge From Inpatient Psychiatric Facilities.

OBJECTIVE: The objective of this study was to develop and test a measure that estimates unplanned, 30-day, all-cause risk-standardized readmission rates (RSRRs) after inpatient psychiatric facility (IPF) discharge. PARTICIPANTS: We established a retrospective cohort of adults with a principal diagnosis of psychiatric illness or dementia discharged from IPFs to nonacute care settings, using 2012-2013 Medicare fee-for-service claims data. MEASURES: All-cause unplanned readmissions within 3-30 days post-IPF discharge were assessed by constructing then validating a parsimonious logistic regression model of 56 risk factors (selected via empirical data, systematic literature review, clinical expert opinion) for readmission using bootstrapping. RSRRs were calculated from the ratio of predicted versus expected readmission rates for each IPF using hierarchical regression. Measure reliability and validity were assessed via multiple strategies. RESULTS: The measure development cohort included 716,174 admissions to 1679 IPFs and 149,475 (20.9%) readmissions. Most readmissions (>80%) had principal diagnoses of mood, schizoaffective or substance use disorders, delirium/dementia, infections or drug/substance poisoning. Facility RSRRs ranged from 11.0% to 35.4%. The risk adjustment model showed good calibration and moderate discrimination similar to other readmission risk models (c statistic 0.66). Sensitivity analyses solidified the risk modeling approach. The intraclass correlation coefficient of estimated IPF RSRRs was 0.78, indicating good reliability. The measure identified 8.3% of hospitals as having better and 13.4% as having worse RSRRs than the national readmission rate. CONCLUSIONS: The measure provides an assessment of facility-level quality and insight into risk factors useful for informing preventive interventions. The measure will be included in the Centers for Medicare and Medicaid Services (CMS) Inpatient Psychiatric Quality Reporting program in 2019.

Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

BACKGROUND: It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES: To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN: Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS: For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS: We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS: In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS: An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions.