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Bending the Cost Curve? Results From a Comprehensive Primary Care Payment Pilot

Datos Bibliográficos

ID9102313
AutoresSonal Vats (Boston University, autor de correspondencia), Arlene S Ash (0000-0002-8448-0253, University of Massachusetts Chan Medical School), Randall P Ellis (0000-0003-2176-5347, Verisk Analytics (United States), autor de correspondencia)
Año2013
Volumen51
Número11
Páginas964-969
Fecha de publicación2013-11-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e3182a97bdc
PMID24113816
PMCIDPMC3845668
OpenAlexW2029758412
IdiomaEN
Citas recibidas2
Referencias citadas5

BACKGROUND: There is much interest in understanding how using bundled primary care payments to support a patient-centered medical home (PCMH) affects total medical costs. RESEARCH DESIGN AND SUBJECTS: We compare 2008-2010 claims and eligibility records on about 10,000 patients in practices transforming to a PCMH and receiving risk-adjusted base payments and bonuses, with similar data on approximately 200,000 patients of nontransformed practices remaining under fee-for-service reimbursement. METHODS: We estimate the treatment effect using difference-in-differences, controlling for trend, payer type, plan type, and fixed effects. We weight to account for partial-year eligibility, use propensity weights to address differences in exogenous variables between control and treatment patients, and use the Massachusetts Health Quality Project algorithm to assign patients to practices. RESULTS: Estimated treatment effects are sensitive to: control variables, propensity weighting, the algorithm used to assign patients to practices, how we address differences in health risk, and whether/how we use data from enrollees who join, leave, or change practices. Unadjusted PCMH spending reductions are 1.5% in year 1 and 1.8% in year 2. With fixed patient assignment and other adjustments, medical spending in the treatment group seems to be 5.8% (P=0.20) lower in year 1 and 8.7% (P=0.14) lower in year 2 than for propensity-weighted, continuously enrolled controls; the largest proportional 2-year reduction in spending occurs in laboratory test use (16.5%, P=0.02). CONCLUSIONS: Although estimates are imprecise because of limited data and quasi-experimental design, risk-adjusted bundled payment for primary care may have dampened spending growth in 3 practices implementing a PCMH

Bending · Business · Economics · Family medicine · Operations management · Payment · Primary care · Structural engineering · Chronic Disease Management Strategies · Engineering · Finance · Healthcare Policy and Management · Medicine · Primary Care and Health Outcomes

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Obras citantes distintas2
Citas por año0,22
Intervalo de citas2017 - 2025 (9)
Velocidad de citaciónrecent
Altamente citadoNo
Tipos de citaNeutras: 2
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