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

TL;DR

This paper presents a machine learning framework that predicts which patients will benefit most from digital health interventions by analyzing claims, sociodemographic, and app data, enabling targeted care and continuous improvement.

Contribution

It introduces a novel impactability prediction model combining cost prediction and classification, demonstrating improved accuracy for targeting digital health interventions.

Findings

Achieved 71.9% accuracy in impactability classification

Demonstrated potential for iterative model improvement with more data

Proposed a generalizable approach for impactability analysis

Abstract

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.