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dc.contributor.authorVats, Sonal
dc.contributor.authorAsh, Arlene S.
dc.contributor.authorEllis, Randall P.
dc.date2022-08-11T08:08:29.000
dc.date.accessioned2022-08-23T15:56:50Z
dc.date.available2022-08-23T15:56:50Z
dc.date.issued2013-11-01
dc.date.submitted2013-12-23
dc.identifier.citation<p>Vats S, Ash AS, Ellis RP. Bending the cost curve? Results from a comprehensive primary care payment pilot. Med Care. 2013 Nov;51(11):964-9. doi:10.1097/MLR.0b013e3182a97bdc. <a href="http://dx.doi.org/10.1097/MLR.0b013e3182a97bdc" target="_blank">Link to article on publisher's site</a></p>
dc.identifier.issn1537-1948
dc.identifier.doi10.1097/MLR.0b013e3182a97bdc
dc.identifier.pmid24113816
dc.identifier.urihttp://hdl.handle.net/20.500.14038/30056
dc.description.abstractBACKGROUND: 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.
dc.language.isoen_US
dc.publisherLippincott Williams & Wilkins
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24113816&dopt=Abstract">Link to article in PubMed</a></p>
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845668/
dc.subjectAdult
dc.subjectAged
dc.subjectAlgorithms
dc.subjectFemale
dc.subjectHealth Expenditures
dc.subjectHumans
dc.subjectInsurance Claim Review
dc.subjectInsurance Coverage
dc.subjectInsurance, Health
dc.subjectMale
dc.subjectMassachusetts
dc.subjectMedicaid
dc.subjectMedicare
dc.subjectMiddle Aged
dc.subjectPatient-Centered Care
dc.subjectPrimary Health Care
dc.subjectPropensity Score
dc.subjectRisk Adjustment
dc.subjectUnited States
dc.subjectUMCCTS funding
dc.subjectHealth Services Administration
dc.subjectPrimary Care
dc.titleBending the cost curve? Results from a comprehensive primary care payment pilot
dc.typeJournal Article
dc.source.journaltitleMedical care
dc.source.volume51
dc.source.issue11
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/290
dc.identifier.contextkey4943189
html.description.abstract<p>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.</p> <p>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.</p> <p>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.</p> <p>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).</p> <p>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.</p>
dc.identifier.submissionpathfaculty_pubs/290
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages964-9


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