Studies from Harvard T.H. Chan School of Public Health in the Area of Machine Learning Described (A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods)

2020 AUG 26 (NewsRx) -- By a News Reporter-Staff News Editor at Ivy League Daily News -- Investigators discuss new findings in Machine Learning. According to news reporting out of Boston, Massachusetts, by NewsRx editors, research stated, “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.”

Our news journalists obtained a quote from the research from the Harvard T.H. Chan School of Public Health, “This study explores a framework to define impactability by using machine learning (ML) models to identify those patients most likely to benefit from a digital health intervention for care management. Anonymized insurance claims data were used from a commercially insured population across several US states and combined with inferred sociodemographic data. The approach involves creating 2 models and the comparative analysis of the methodologies and performances therein. The authors first train a cost prediction model to calculate the differences in predicted (without intervention) versus actual (with onboarding onto digital health platform) health care expenditures for patients (N = 5600). This enables classification impactability if differences in predicted versus actual costs meet a predetermined threshold. Several random forest and logistic regression machine learning models were then trained to accurately categorize new patients as impactable versus not impactable. These parameters are modified through grid search to define the parameters that deliver optimal performance, reaching an overall sensitivity of 0.77 and specificity of 0.65 among all models. This approach shows that impactability for a digital health intervention can be successfully defined using ML methods, thus enabling efficient allocation of resources.”

According to the news editors, the research concluded: “This framework is generalizable to analyzing impactability of any intervention and can contribute to realizing closed-loop feedback systems for continuous improvement in health care.”

For more information on this research see: A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods. Population Health Management, 2020;23(4):319-325. Population Health Management can be contacted at: Mary Ann Liebert, Inc, 140 Huguenot Street, 3RD Fl, New Rochelle, NY 10801, USA. (Mary Ann Liebert, Inc. - www.liebertpub.com; Population Health Management - http://www.liebertpub.com/overview/population-health-management-formerly-disease-management/301/)

Our news journalists report that additional information may be obtained by contacting Trishan Panch, Harvard T.H. Chan School of Public Health, Dept. of Health Policy and Management, Boston, MA, United States. Additional authors for this research include Heather Mattie, Patrik Bachtiger, Patrick Reidy, Emily Lindemer, Nikolay Nikolaev, Mohammad Jouni, Joann Schaefer and Michael Sherman.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1089/pop.2019.0132. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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