Analytic and learning framework for quantifying Value in Value Based Care

Value Based Care (VBC) is changing the way health care is administered, to cut costs and improve outcomes. There are no tools available to quantitatively assess Value. We present a fully quantitative analytic and learning framework using machine learning for assessing Value in VBC as a direct function of the Quality outcomes and Cost metrics, specific to a particular medical treatment. It can be used by various Stakeholders to plan, track, assess and agree upon the Value of a variety of healthcare services. Results are presented to demonstrate the proposed framework for the Total Hip Replacement (THR) treatment. Multivariate Regression and Logistic Regression with Dummy Coding are shown to be useful for predicting Value, using the basic algorithms proposed here.