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BACKGROUND Clinical Decision Support Systems (CDSS) have the potential to play a crucial role in enhancing healthcare quality by providing evidence-based information to clinicians at the point of care. Despite their increasing popularity, there is a lack of comprehensive research exploring their design characterisation and trends. This limits our understanding and ability to optimise their functionality, usability, and adoption in healthcare settings. OBJECTIVE This systematic review aims to analyse the design characteristics of CDSS, identify design-related challenges, and provide insight on the implications for future design. METHODS This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) recommendations and used a Grounded Theory analytical approach to guide the conduct, data analysis, and synthesis. A search of five major electronic databases (PubMed, Web of Science, Scopus, IEEE Xplore, and the Journal of Decision Systems) was conducted for articles published between 2013 and 2023, using predefined design-focused keywords (design, user experience, implementation, evaluation, usability, and architecture). Out of 1922 initially identified articles, 40 passed screening and eligibility checks (by two researchers) for a full review and analysis. RESULTS Analysis of the studies revealed that User-Centred Design (UCD) is the most widely adopted approach for designing CDSS, with all design processes incorporating functional or usability evaluation mechanisms. The CDSS reported were mainly clinician-facing and mostly standalone systems, with their design lacking consideration for integration with existing clinical information systems and workflows. Through a UCD lens, four key categories of challenges relevant to CDSS design were identified: 1) usability and user experience, 2) reliability and effectiveness, 3) data access, and 4) context and clinical complexities. CONCLUSIONS While CDSS show promise in enhancing health care delivery, identified challenges have implications for their future design, efficacy, and utilisation. Adopting pragmatic UCD design approaches that actively involve users is essential for enhancing usability and addressing identified user experience challenges. Integrating with clinical systems is crucial for interoperability and presents opportunities for AI-enabled CDSS that rely on large patient data. Incorporating emerging technologies like explainable AI can boost trust and acceptance. Enabling functionality for CDSS to support both clinicians and patients can create opportunities for effective use in virtual care settings.
Bayor et al. (Fri,) studied this question.