Identification of digital clinical decision support systems for supporting diagnosis and triage of patients with...

Clinical decision support systems (CDSSs) are computerized tools that support clinical decision-making processes. Primary care decision-making is complex and has the potential to influence quality of care provided and patient outcomes. CDSS not only assist providers with clinical decision-making to ensure quality standards are met, reflect evidence-informed practice, and reduce variation in care, but also help patients navigate and receive an appropriate care pathway amidst numerous, often complex, options. Therefore, this scoping review will aim to identify existing CDSSs for supporting primary point-of-care providers, directing patients to appropriate management pathways, and supporting the clinical examination (i.e., medical history-taking and physical examination) process for patients with shoulder disorders. At the primary point-of-care system level, a CDSS for shoulder disorders will improve clinical efficiency and support decision-making.

Scoping review methodology and reporting will be conducted according to Arksey and O'Malley's 6-step framework, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P), and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews (PRISMA-ScR) reporting guide. A robust search strategy will be applied across four databases: MEDLINE (Ovid), EMBASE (Ovid), CINAHL (Ebsco), and Scopus (Elsevier). Two blinded reviewers will independently evaluate all titles and corresponding abstracts based on pre-specified inclusion and exclusion criteria. Inter-rater reliability (IRR) agreement will be established during an initial pilot-screening phase against a random selection of 20 records (minimum) until reaching Cohen's Kappa ≥ 0.81. Data extraction will be completed by one reviewer and validated by a second.

An effective and high-quality CDSS that is affordable, easy to use, easily accessible, compatible with existing clinical processes, and generalizable across diverse settings will help to support primary point-of-care providers in diagnosing and managing patients presenting with shoulder disorders, thus improving quality of care for patients.