A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods.

Heather Mattie^1,2, Patrick Reidy², Patrik Bachtiger¹, Emily Lindemer², Nikolay Nikolaev², Mohammad Jouni², Joann Schaefer³, Michael Sherman⁴, Trishan Panch^1,2

¹Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

²Wellframe, Inc., Boston, Massachusetts, USA.

³BlueCross BlueShield Nebraska, Omaha, Nebraska, USA.

⁴Harvard Pilgrim Health Care, Wellesley, Massachusetts, USA.

PubMed DOI

Abstract

Digital care management programs can reduce health care costs and improve quality of care. However, it is unclear how to target patients who are most likely to benefit from these programs ex ante, a shortcoming of current "risk score"-based approaches across many interventions. This study explores a framework to define impactability by using machine learning (ML) models to identify those patients…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.