Technology-supported shared decision making in the treatment and management of chronic conditions: a systematic review protocol

Background: In current healthcare, there can be a transition observed towards more preventive and patient-centered care in which technology plays an increasing role. [1-4]. For decision-making specifically this could entail an active role of patients by shared decision making (SDM) and/or active use of technology by i.e. clinical decision support systems (CDSSs) [5, 6]. A systematic review will be conducted to explore how decision-making is structured in both SDM and CDSSs, and to determine how effective they are for adults with a chronic condition. This abstract reports on the protocol that adheres to the PRISMA-P guidelines [7], and will be registered in PROSPERO. Methods: Studies will be eligible when including: (P) adults with a chronic condition (i.e. COPD, IHD, CHF, diabetes, depression or anxiety), (I) SDM processes or CDSSs that assist in decision-making, (C) control groups receiving usual care, (O) patient-reported, patient-experience or clinical outcomes, and (S) controlled clinical trials. Studies will be identified through PubMed, Scopus, Embase, Web of Science, Cinahl and PsychINFO. Search strings will be conducted in English and limited to studies published from the 1st of January 2011 until the 31st of December 2021. Endnote (Clarivate Analytics, USA) will be used for de-duplication, ASReview (Utrecht University, The Netherlands) for title and abstract screening, and Covidence (Veritas Health Innovation, Australia) for full-text screening, inter-rater reliability, data extraction and risk-of-bias assessment. Finally, RevMan (Nordic Cochrane Centre, Denmark) will be used for data synthesis, if applicable, and data visualization. Two reviewers will independently execute the protocol, whereby for unresolved disagreements a third reviewer will be consulted. Results: Roles of patients and physicians in SDM and CDSSs are described based on a SDM coding system by Clayman et al., whereas the role of technology in SDM and CDSSs are described based on five primary interaction styles defined by Shneiderman [8, 9]. The effectiveness of SDM and CDSSs are evaluated based on patient and clinical outcomes. Preliminary results are expected in January 2022. Discussion: This review will allow for interpreting the effects of SDM and CDSSs critically and identify knowledge gaps in technology-supported shared decision making for future research and design. REFERENCES 1. Thimbleby, H., Technology and the future of healthcare. Journal of public health research, 2013. 2(3). 2. Azodo, I., et al., Opportunities and Challenges Surrounding the Use of Data From Wearable Sensor Devices in Health Care: Qualitative Interview Study. J Med Internet Res, 2020. 22(10): p. e19542. 3. Grady, P.A. and L.L. Gough, Self-management: a comprehensive approach to management of chronic conditions. American journal of public health, 2014. 104(8): p. e25-e31. 4. Tinetti, M.E., et al., Patient Priority-Directed Decision Making and Care for Older Adults with Multiple Chronic Conditions. Clin Geriatr Med, 2016. 32(2): p. 261-75. 5. Abbasgholizadeh Rahimi, S., et al., Are mobile health applications useful for supporting shared decision making in diagnostic and treatment decisions? Glob Health Action, 2017. 10(sup3): p. 1332259. 6. Sutton, R.T., et al., An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med, 2020. 3: p. 17. 7. Shamseer, L., et al., Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ, 2015. 350: p. g7647. 8. Clayman, M.L., et al., Development of a shared decision making coding system for analysis of patient-healthcare provider encounters. Patient Educ Couns, 2012. 88(3): p. 367-72. 9. Shneiderman, B., A taxonomy and rule base for the selection of interaction styles, in Readings in Human–Computer Interaction. 1995, Elsevier. p. 401-410.