title: From Value Assessment to Value Cocreation: Informing Clinical Decision-Making with Medical Claims Data.
abstract: Big data and advances in analytical processes represent an opportunity for the healthcare industry to make better evidence-based decisions on the value generated by various tests, procedures, and interventions. Value-based reimbursement is the process of identifying and compensating healthcare providers based on whether their services improve quality of care without increasing cost of care or maintain quality of care while decreasing costs. In this article, we motivate and illustrate the potential opportunities for payers and providers to collaborate and evaluate the clinical and economic efficacy of different healthcare services. We conduct a case study of a firm that offers advanced biomarker and disease state management services for cardiovascular and cardiometabolic conditions. A value-based analysis that comprised a retrospective case/control cohort design was conducted, and claims data for over 7000 subjects who received these services were compared to a matched control cohort. Study subjects were commercial and Medicare Advantage enrollees with evidence of CHD, diabetes, or a related condition. Analysis of medical claims data showed a lower proportion of patients who received biomarker testing and disease state management services experienced a MI (p < 0.01) or diabetic complications (p < 0.001). No significant increase in cost of care was found between the two cohorts. Our results illustrate the opportunity healthcare payers such as Medicare and commercial insurance companies have in terms of identifying value-creating healthcare interventions. However, payers and providers also need to pursue system integration efforts to further automate the identification and dissemination of clinically and economically efficacious treatment plans to ensure at-risk patients receive the treatments and interventions that will benefit them the most.
authors.affiliation: 1 Robins School of Business, University of Richmond , Richmond, Virginia.
authors.forename: Steven
authors.initials: S
authors.lastname: Thompson
authors.affiliation: 2 Salveo Diagnostics, Inc. , Glen Allen, Virginia.
authors.forename: Stephen
authors.initials: S
authors.lastname: Varvel
authors.affiliation: 3 Venebio, LLC , Richmond, Virginia.
authors.forename: Maciek
authors.initials: M
authors.lastname: Sasinowski
authors.affiliation: 4 Health Economics and Outcomes Research , Optum, Eden Prairie, Minnesota.
authors.forename: James P
authors.initials: JP
authors.lastname: Burke
mesh_terms: D000328:Adult; D000368:Aged; D016022:Case-Control Studies; D000066491:Clinical Decision-Making; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007345:Insurance Claim Review; D008297:Male; D006278:Medicare; D008875:Middle Aged; D012189:Retrospective Studies; D014481:United States
keywords: big data analytics, big data industry standards; business intelligence