Prediction of Patient Activation during Technology Enabled Continuity of Care Intervention
40 Pages Posted: 3 Mar 2016 Last revised: 29 Jan 2019
Date Written: March 1, 2018
Patients’ skills, knowledge, and motivation to actively engage in their healthcare are assessed with the Patient Activation Measure (PAM). The literature on predicting PAM, when patient counseling is coupled with a technology enabled continuity of care intervention, is scant. By drawing upon organizational learning theory, we develop a double loop model of patient/ healthcare provider feedback loops and learning. We test the model using data from a randomized, controlled field experiment and find a mediated relationship between technology enabled continuity of care and increased PAM and reductions in hospital readmissions. 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 behavioral biases admitted in the double loop learning theory.
Keywords: Behavioral Operations, Controlled Studies, Double Loop Learning, Machine Learning, Patient Activation, Patient Engagement, System Neglect, Telemonitoring
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