title: Estimating and reporting on the quality of inpatient stroke care by Veterans Health Administration Medical Centers.
abstract: BACKGROUND
  Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error caused by small numbers of eligible patients per facility. We applied multilevel modeling and empirical Bayes (EB) estimation in addressing this issue in performance reporting of stroke care quality in the Medical Centers.
  
  
  RESULTS
  We studied a retrospective cohort of 3812 veterans admitted to 106 Medical Centers with ischemic stroke during fiscal year 2007. The median number of study patients per facility was 34 (range, 12-105). Inpatient stroke care quality was measured with 13 evidence-based QIs. Eligible patients could either pass or fail each indicator. Multilevel modeling of a patient's pass/fail on individual QIs was used to produce facility-level EB-estimated QI pass rates and confidence intervals. The EB estimation reduced interfacility variation in QI rates. Small facilities and those with exceptionally high or low rates were most affected. We recommended 8 of the 13 QIs for performance reporting: dysphagia screening, National Institutes of Health Stroke Scale documentation, early ambulation, fall risk assessment, pressure ulcer risk assessment, Functional Independence Measure documentation, lipid management, and deep vein thrombosis prophylaxis. These QIs displayed sufficient variation across facilities, had room for improvement, and identified sites with performance that was significantly above or below the population average. The remaining 5 QIs were not recommended because of too few eligible patients or high pass rates with little variation.
  
  
  CONCLUSIONS
  Considerations of statistical uncertainty should inform the choice of QIs and their application to performance reporting.
authors.affiliation: VHA Health Services Research and Development Stroke Quality Enhancement Research Initiative, Indianapolis, IN, USA. GArling@IUPUI.edu
authors.forename: Greg
authors.initials: G
authors.lastname: Arling
authors.forename: Mathew
authors.initials: M
authors.lastname: Reeves
authors.forename: Joseph
authors.initials: J
authors.lastname: Ross
authors.forename: Linda S
authors.initials: LS
authors.lastname: Williams
authors.forename: Salomeh
authors.initials: S
authors.lastname: Keyhani
authors.forename: Neale
authors.initials: N
authors.lastname: Chumbler
authors.forename: Michael S
authors.initials: MS
authors.lastname: Phipps
authors.forename: Christianne
authors.initials: C
authors.lastname: Roumie
authors.forename: Laura J
authors.initials: LJ
authors.lastname: Myers
authors.forename: Amanda H
authors.initials: AH
authors.lastname: Salanitro
authors.forename: Diana L
authors.initials: DL
authors.lastname: Ordin
authors.forename: Jennifer
authors.initials: J
authors.lastname: Myers
authors.forename: Dawn M
authors.initials: DM
authors.lastname: Bravata
mesh_terms: D001499:Bayes Theorem; D055317:Evidence-Based Practice; D006787:Hospitals, Veterans; D006801:Humans; D007297:Inpatients; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D020521:Stroke / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs