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It is not clear how to target patients who are most likely to benefit fromdigital care management programs ex-ante, a shortcoming of current risk scorebased approaches. This study focuses on defining impactability by identifyingthose patients most likely to benefit from technology enabled care management,delivered through a digital health platform, including a mobile app andclinician web dashboard. Anonymized insurance claims data were used from acommercially insured population across several U.S. states and combined withinferred sociodemographic data and data derived from the patient-held mobileapplication itself. Our approach involves the creation of two models and thecomparative analysis of the methodologies and performances therein. We firsttrain a cost prediction model to calculate the differences in predicted(without intervention) versus actual (with onboarding onto digital healthplatform) healthcare expenditure for patients (N = 1,242). This enables theclassification of impactability if differences in predicted versus actual costsmeet a predetermined threshold. A random forest machine learning model was thentrained to accurately categorize new patients as impactable versus notimpactable, reaching an overall accuracy of 71.9%. We then modify theseparameters through grid search to define the parameters that deliver optimalperformance. A roadmap is proposed to iteratively improve the performance ofthe model. As the number of newly onboarded patients and length of usecontinues to increase, the accuracy of predicting impactability will improvecommensurately as more advanced machine learning techniques such as deeplearning become relevant. This approach is generalizable to analyzing theimpactability of any intervention and is a key component of realising closedloop feedback systems for continuous improvement in healthcare.

A Framework for Predicting Impactability of Healthcare Interventions  Using Machine Learning Methods, Administrative Claims, Sociodemographic and  App Generated Data