  It is not clear how to target patients who are most likely to benefit from
digital care management programs ex-ante, a shortcoming of current risk score
based approaches. This study focuses on defining impactability by identifying
those patients most likely to benefit from technology enabled care management,
delivered through a digital health platform, including a mobile app and
clinician web dashboard. Anonymized insurance claims data were used from a
commercially insured population across several U.S. states and combined with
inferred sociodemographic data and data derived from the patient-held mobile
application itself. Our approach involves the creation of two models and the
comparative analysis of the methodologies and performances therein. We first
train a cost prediction model to calculate the differences in predicted
(without intervention) versus actual (with onboarding onto digital health
platform) healthcare expenditure for patients (N = 1,242). This enables the
classification of impactability if differences in predicted versus actual costs
meet a predetermined threshold. A random forest machine learning model was then
trained to accurately categorize new patients as impactable versus not
impactable, reaching an overall accuracy of 71.9%. We then modify these
parameters through grid search to define the parameters that deliver optimal
performance. A roadmap is proposed to iteratively improve the performance of
the model. As the number of newly onboarded patients and length of use
continues to increase, the accuracy of predicting impactability will improve
commensurately as more advanced machine learning techniques such as deep
learning become relevant. This approach is generalizable to analyzing the
impactability of any intervention and is a key component of realising closed
loop feedback systems for continuous improvement in healthcare.
