Reaching Optimal Health: The Voice of Clinicians from a Roleplay Simulation

Helping patients to reach optimal health entails a holistic approach of complex interventions including clinical decision support systems, patient decision aids, and self-management tools. In real-world settings, understanding the human factors in technological interventions is the core of HCI research; however, it requires a considerable amount of time to run experimental procedures, especially for patients with mental disorders. We conducted a roleplay simulation over a period of two weeks that comprised observations, and semi-structured interviews with eight health care professionals participated in the simulated use of a health optimization system. The study revealed the SWING model of enabling interventions towards optimal health as i) Sharing feelings, ii) Weaving of information, iii) Improving awareness, iv) Nurturing trust v) Giving support. This model establishes a common path from research to practice for researchers and practitioners in eHealth and HCI.

[1]  Soudabeh Khodambashi,et al.  The development and feasibility of a personal health-optimization system for people with bipolar disorder , 2017, BMC Medical Informatics and Decision Making.

[2]  Danny Chiang Choon Poo,et al.  Designing "Living" Evidence Networks for Health Optimisation: Knowledge Extraction of Patient-Relevant Outcomes in Mental Disorders , 2018, DESRIST.

[3]  G Lanzola,et al.  Personalization and Patient Involvement in Decision Support Systems: Current Trends , 2015, Yearbook of Medical Informatics.

[4]  Emily Gerth-Guyette,et al.  Impact of mHealth Chronic Disease Management on Treatment Adherence and Patient Outcomes: A Systematic Review , 2015, Journal of medical Internet research.

[5]  Danny Chiang Choon Poo,et al.  Designing a Social Mobile Platform for Diabetes Self-management: A Theory-Driven Perspective , 2015, HCI.

[6]  Hui Zhang,et al.  Analysis and Design of an mHealth Intervention for Community-Based Health Education: An Empirical Evidence of Coronary Heart Disease Prevention Program Among Working Adults , 2017, DESRIST.

[7]  Mark Matthews,et al.  Taking part: role-play in the design of therapeutic systems , 2014, CHI.

[8]  Hoang D. Nguyen,et al.  Analysis and design of mobile health interventions towards informed shared decision making: an activity theory-driven perspective , 2016, J. Decis. Syst..

[9]  Alan E Kazdin,et al.  Evidence-based treatment and practice: new opportunities to bridge clinical research and practice, enhance the knowledge base, and improve patient care. , 2008, The American psychologist.

[10]  R. Thomson,et al.  Decision aids for people facing health treatment or screening decisions. , 2003, The Cochrane database of systematic reviews.

[11]  R Brian Haynes,et al.  Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials , 2013, BMJ : British Medical Journal.

[12]  Danny Chiang Choon Poo,et al.  In-Situ Simulation in Design Science Research: Evaluation of Complex Design Artifacts , 2018, ICIS.