SupportPrim CDSS: A clinical decision support system architecture based on microservices for non-specific musculoskeletal disorders.

Background And Objective: Non-specific musculoskeletal disorders (MSDs) pose significant challenges in primary care due to ambiguous symptoms and diverse etiologies. This research presents the SupportPrim clinical decision support system (CDSS), an innovative approach that combines case-based reasoning (CBR) with a scalable microservice framework, aiming to improve personalized treatment and clinical decision processes in MSD care.

Methods: The SupportPrim CDSS is engineered using a modular microservice architecture designed for scalability, reliability, and seamless clinical integration. Subjective patient-reported questionnaires and demographic data are processed through an optimized CBR engine that retrieves precedent cases to inform current clinical decisions. The system leverages rigorous evaluation through iterative experiments and a randomized controlled trial (RCT) in Norwegian primary care, thereby assessing its usability, clinical utility, and operational performance.

Results: The system demonstrates high reliability, characterized by negligible downtime and a mean case retrieval response time of 0.18 seconds. Clinicians reported favorable user interactions, emphasizing the system's ability to facilitate shared decision making and personalized care. While the SupportPrim study intentionally maintained a static casebase, the system possesses the ability to incorporate active learning to boost adaptability and precision. Extensive validation and verification from associated studies confirm considerable performance of both the CBR engine and the CDSS.

Conclusion: The SupportPrim CDSS effectively leverages CBR within a microservice-based framework to aid clinicians in delivering evidence-based, personalized patient care for patients with non-specific MSDs. Its robust design, coupled with comprehensive verification and validation across multiple associated studies, underscores its potential for broader healthcare applications and improved clinical decision support.