Prediction of Patient Activation during Technology Enabled Continuity of Care Intervention
38 Pages Posted: 3 Mar 2016 Last revised: 3 Dec 2016
Date Written: December 1, 2016
Patients’ skills, knowledge, and motivation to actively engage in their healthcare are assessed with the Patient Activation Measure (PAM) – a metric associated with positive healthcare outcomes. The literature on predicting PAM, when patient counseling is coupled with a technology enabled continuity of care intervention, is scant. This proof-of-concept study employs a two-phase framework to (i) posit a relationship between a technology enabled continuity of care intervention and enhanced patient activation; and (ii) link healthcare providers’ operating decisions and patients’ willingness to change with prediction of PAM. We test the framework using data from a randomized, controlled field experiment and find a relationship between technology enabled continuity of care and increased PAM. Further, the relevant PAM levels are predicted as a function of the strength of the information signals using a machine learning methodology. We show that these predictions are subject to under/over estimation biases, consistent with the behavioral concept of system neglect in signal detection theory.
Keywords: Behavioral Operations, Controlled Studies, Double Loop Learning, Machine Learning, Patient Activation, Patient Engagement, System Neglect, Telemonitoring
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