Bending the Cost Curve? Results From a Comprehensive Primary Care Payment Pilot
Dados Bibliográficos
| ID | 9102313 |
|---|---|
| Autores | Sonal Vats (Boston University, autor correspondente), 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 correspondente) |
| Ano | 2013 |
| Volume | 51 |
| Fascículo | 11 |
| Páginas | 964-969 |
| Data de publicação | 2013-11-01 |
| Peer Reviewed | Sim |
| Open Access | Não |
| Tipo | ARTICLE |
| Periódico | Medical Care (JOURNAL) |
| Identificadores do periódico | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Editora | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0b013e3182a97bdc |
| PMID | 24113816 |
| PMCID | PMC3845668 |
| OpenAlex | W2029758412 |
| Idioma | EN |
| Citações recebidas | 2 |
| Referências citadas | 5 |
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
| Obras citantes distintas | 2 |
|---|---|
| Citações por ano | 0,22 |
| Intervalo de citações | 2017 - 2025 (9) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 2 |