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Prediction of Patient Activation during Technology Enabled Continuity of Care Intervention

38 Pages Posted: 3 Mar 2016 Last revised: 3 Dec 2016

Carrie Queenan

University of South Carolina - Department of Management Science

Kellas Cameron

Boston University - Department of Operations and Technology Management

Nitin Joglekar

Boston University - Department of Operations and Technology Management

Date Written: December 1, 2016

Abstract

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

Suggested Citation

Queenan, Carrie and Cameron, Kellas and Joglekar, Nitin, Prediction of Patient Activation during Technology Enabled Continuity of Care Intervention (December 1, 2016). Available at SSRN: https://ssrn.com/abstract=2739457 or http://dx.doi.org/10.2139/ssrn.2739457

Carrie Queenan (Contact Author)

University of South Carolina - Department of Management Science ( email )

United States

Kellas Cameron

Boston University - Department of Operations and Technology Management ( email )

United States

Nitin Joglekar

Boston University - Department of Operations and Technology Management ( email )

United States

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