Theses and Dissertations

Date of Award

12-2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Business Administration

First Advisor

Dr. Francis Kofi Andoh-Baidoo

Second Advisor

Dr. Babajide Osatuyi

Third Advisor

Dr. Nan Xiao

Abstract

Reminder systems have great potential to enhance healthcare outcome if it can facilitate collaborative appointment management with accessible mobile communication technology in patient-centered care. Yet, Current appointment reminder systems are effective but not optimal (McLean, et al. 2016). Following the design science process delineated by Peffers et al. (2007) and other requirements, this study proposes a design of reciprocal reminder system that automates the process of appointment rescheduling for healthcare providers and patients in addition to confirmation and cancellation. Based on the premises of media synchronicity theory, media naturalness theory and stakeholder theory as kernel theories, this study develops a design theory that covers platform design, communication design and service design. Design principles of new mobile appointment reminders are proposed to cater to the different requirements of provider and patient users. Situation adaptivity and privacy sensitivity are identified as the major design features that need to strike a balance between different user requirements. An experiment investigates how the variation in design may influence user behavior, and the findings suggest that situation adaptivity and privacy sensitivity have positive effects on users’ system experiences in terms of performance expectancy, effort expectancy and subjective consonance. Further survey results on the final design confirm that the reciprocal reminder system adaptive to patient situations and sensitive to privacy concerns has the expected effects on user behavior.

Comments

Copyright 2016 Ying Wang. All Rights Reserved.

https://www.proquest.com/dissertations-theses/mobile-appointment-reminders-patient-centered/docview/1878220335/se-2?accountid=7119

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