Importance

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Abstract

The present disclosure is directed to methods and systems for predicting patient dropout from a remote patient monitoring (RPM) program, as well as root dropout causes, based on clinical features using a “dropout prediction engine”. As described herein, the methods and systems address the clinical challenge of early detection of dropout risk of patients from these virtual care programs through a data-driven approach that accurately identifies the likely root cause(s) of the dropout and enables the prevention of the dropout by applying timely interventions targeting the root causes of the dropout. As a result, dropout prevention effectuated through targeted interventions will promote continued engagement with virtual care, thereby leading to lower costs of care, better health outcomes, and better patient and staff experience.

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First Claim