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dc.contributor.authorAsh, Arlene S.
dc.contributor.authorMick, Eric O.
dc.contributor.authorZhang, Jianying
dc.contributor.authorEllis, Randall P.
dc.contributor.authorEanet, Frances E
dc.contributor.authorSteinberg, Judith
dc.date.accessioned2023-11-06T15:52:22Z
dc.date.available2023-11-06T15:52:22Z
dc.date.issued2016-06-20
dc.identifier.doi10.13028/rm11-tw07
dc.identifier.urihttp://hdl.handle.net/20.500.14038/52692
dc.descriptionIssued by the Center for Health Policy and Research, Commonwealth Medicine, UMass Medical School.en_US
dc.description.abstractMassHealth’s current risk adjustment method is to measure risk as proportional to a DxCG relative risk score (RRS) calculated from age, sex and diagnoses reported on medical encounters. We call this the BASE model and use it as a tool for examining enrollee differences in health risk and utilization in the fee-for-service part of MassHealth (the PCC plan) and the managed care (MCO) part. We describe large differences in PCC and MCO program enrollees, with the PCC program attracting a much higher proportion of patients entitled to coverage due to disability and/or with severe behavioral health problems, developmental disabilities and complex medical needs. We find limited to negative evidence that the MCO plans are able to either save money or better manage the care of such patients. We also explore ways to better predict health care costs. Although we examine data from 2011 through 2014, we focused on model building in the 2013 data. In addition to the DxCG RRS, our recommended FULL model for predicting cost includes demographics, selected medical factors, and social determinants of health. Moving from the BASE to the FULL model improved total explanatory power (R2) for both PCC enrollees and for MCO enrollees. Applying the FULL model, as fit to the PCC 2013 data, to 2014 data yielded high R2s in the 2014 data (52.3% in PCC 2014 data and 56.6% in MCO 2014 data). These figures are increases in R2 of 25% and 4% respectively, from BASE model predictions. All variables used in the models are shown in Table 2. FULL model predictions make better predictions for subgroups. Children, for example, cost at least 20% more in both the PCC and MCO populations than was predicted by the BASE model (Table 3a, Female and Males in the 0-17 age strata). The FULL model modestly over- reimburses both the PCC and MCO plans for children. We offer advice about how to use all (or most) of the variables in the FULL model to finalize a risk-adjustment strategy for global payments in 2017.en_US
dc.language.isoen_USen_US
dc.publisherUMass Medical School, Center for Health Policy and Researchen_US
dc.rightsCopyright © 2016 UMass Medical Schoolen_US
dc.subjectMassHealthen_US
dc.subjectsocial determinants of healthen_US
dc.subject.otherAlternative payment modelsen_US
dc.subject.otherHealth policyen_US
dc.subject.otherMedicaiden_US
dc.subject.otherRisk adjustmenten_US
dc.titleUMass Risk Adjustment Project for MassHealth Payment and Care Delivery Reform June 2016 Reporten_US
dc.typeReporten_US
refterms.dateFOA2023-11-06T15:52:24Z
dc.contributor.departmentBiostatistics and Health Services Researchen_US
dc.contributor.departmentForHealth Consultingen_US
dc.contributor.departmentPopulation and Quantitative Health Sciencesen_US


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