Browsing by Subject "Veterans Health"

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    Strategic planning to reduce the burden of stroke among veterans: using simulation modeling to inform decision making
    (Ovid Technologies Wolters Kluwer -American Heart Association, 2014-07) Lich, Kristen Hassmiller; Tian, Yuan; Beadles, Christopher A.; Williams, Linda S.; Bravata, Dawn M.; Cheng, Eric M.; Bosworth, Hayden B.; Homer, Jack B.; Matchar, David B.; Department of Neurology, IU School of Medicine
    BACKGROUND AND PURPOSE: Reducing the burden of stroke is a priority for the Veterans Affairs Health System, reflected by the creation of the Veterans Affairs Stroke Quality Enhancement Research Initiative. To inform the initiative's strategic planning, we estimated the relative population-level impact and efficiency of distinct approaches to improving stroke care in the US Veteran population to inform policy and practice. METHODS: A System Dynamics stroke model of the Veteran population was constructed to evaluate the relative impact of 15 intervention scenarios including both broad and targeted primary and secondary prevention and acute care/rehabilitation on cumulative (20 years) outcomes including quality-adjusted life years (QALYs) gained, strokes prevented, stroke fatalities prevented, and the number-needed-to-treat per QALY gained. RESULTS: At the population level, a broad hypertension control effort yielded the largest increase in QALYs (35,517), followed by targeted prevention addressing hypertension and anticoagulation among Veterans with prior cardiovascular disease (27,856) and hypertension control among diabetics (23,100). Adjusting QALYs gained by the number of Veterans needed to treat, thrombolytic therapy with tissue-type plasminogen activator was most efficient, needing 3.1 Veterans to be treated per QALY gained. This was followed by rehabilitation (3.9) and targeted prevention addressing hypertension and anticoagulation among those with prior cardiovascular disease (5.1). Probabilistic sensitivity analysis showed that the ranking of interventions was robust to uncertainty in input parameter values. CONCLUSIONS: Prevention strategies tend to have larger population impacts, though interventions targeting specific high-risk groups tend to be more efficient in terms of number-needed-to-treat per QALY gained.
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    Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System
    (Springer-Verlag, 2016-04) Phipps, Michael S.; Fahner, Jeff; Sager, Danielle; Coffing, Jessica; Maryfield, Bailey; Williams, Linda S.; Department of Neurology, School of Medicine
    BACKGROUND: The Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality. OBJECTIVE: To evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA data and determine sources of error in using centralized electronic health record (EHR) data. DESIGN: Our study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) data. eCQM results were generated from CDW data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review. MAIN MEASURES: We calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed. KEY RESULTS: In five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %-99.7 % (denominators) and 87.7 %-97.9 % (numerators). PPVs tended to be higher (range 96.8 %-100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73-0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes. CONCLUSIONS: Stroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in central compared to local data sources. To improve stroke MU measure accuracy, EHRs should include standardized data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.