Prioritizing Quality Measures in Acute Stroke Care : A Cost-Effectiveness Analysis.
Jinyi Zhu, Hooman Kamel, Ajay Gupta, Alvin I Mushlin, Nicolas A Menzies, Thomas A Gaziano, Meredith B Rosenthal, Ankur Pandya
Author Information
Jinyi Zhu: Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee (J.Z.). ORCID
Hooman Kamel: Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute, and Department of Neurology, Weill Cornell Medicine, New York, New York (H.K.).
Ajay Gupta: Department of Radiology, Weill Cornell Medicine, New York, New York (A.G.).
Alvin I Mushlin: Departments of Population Health Sciences and Medicine, Weill Cornell Medical College, New York, New York (A.I.M.). ORCID
Nicolas A Menzies: Center for Health Decision Science and Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (N.A.M.). ORCID
Thomas A Gaziano: Center for Health Decision Science, Harvard T.H. Chan School of Public Health, and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts (T.A.G.).
Meredith B Rosenthal: Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (M.B.R.). ORCID
Ankur Pandya: Center for Health Decision Science and Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (A.P.).
BACKGROUND: The American Heart Association and American Stroke Association (AHA/ASA) endorsed 15 process measures for acute ischemic stroke (AIS) to improve the quality of care. Identifying the highest-value measures could reduce the administrative burden of quality measure adoption while retaining much of the value of quality improvement. OBJECTIVE: To prioritize AHA/ASA-endorsed quality measures for AIS on the basis of health impact and cost-effectiveness. DESIGN: Individual-based stroke simulation model. DATA SOURCES: Published literature. TARGET POPULATION: U.S. patients with incident AIS. TIME HORIZON: Lifetime. PERSPECTIVE: Health care sector. INTERVENTION: Current versus complete (100%) implementation at the population level of quality measures endorsed by the AHA/ASA with sufficient clinical evidence (10 of 15). OUTCOME MEASURES: Life-years, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios, and incremental net health benefits. RESULTS OF BASE-CASE ANALYSIS: Discounted life-years gained from complete implementation would range from 472 (tobacco use counseling) to 34���688 (early carotid imaging) for an annual AIS patient cohort. All AIS quality measures were cost-saving or highly cost-effective by AHA standards (<$50���000 per QALY for high-value care). Early carotid imaging and intravenous tissue plasminogen activator contributed the largest fraction of the total potential value of quality improvement (measured as incremental net health benefit), accounting for 72% of the total value. The top 5 quality measures accounted for 92% of the total potential value. RESULTS OF SENSITIVITY ANALYSIS: A web-based user interface allows for context-specific sensitivity and scenario analyses. LIMITATION: Correlations between quality measures were not incorporated. CONCLUSION: Substantial variation exists in the potential net benefit of quality improvement across AIS quality measures. Benefits were highly concentrated among 5 of 10 measures assessed. Our results can help providers and payers set priorities for quality improvement efforts and value-based payments in AIS care. PRIMARY FUNDING SOURCE: National Institute of Neurological Disorders and Stroke.
References
N Engl J Med. 2014 Aug 28;371(9):796-7
[PMID: 25162885]
