A Knowledge-based Diagnostic Clinical Decision Support System for Musculoskeletal Disorders of the Shoulder for Use in a Primary Care Setting

Abstract

Background Twenty percent of cases seen by primary care clinicians (general practitioners; GPs) are musculoskeletal in nature, and approximately one-quarter of these are shoulder complaints. GPs are increasingly overloaded with clinical information and unfamiliarity with current research can easily lead to misdiagnosis and, in turn, to unnecessary test requests or onward specialist referrals. Well-designed diagnostic clinical decision support systems (CDSS) have been shown to facilitate clinical decision-making and reduce diagnostic errors. However, no CDSS have been developed or tested for musculoskeletal disorders. Methods We have developed a prototype knowledge-based diagnostic CDSS for musculoskeletal shoulder conditions. The CDSS uses Bayesian reasoning to diagnose six common musculoskeletal shoulder pathologies, based on current evidence and expert opinion. The CDSS was tested by comparing its diagnostic outcome against 50 case studies with known diagnosis by radiological imaging. Results The CDSS diagnostic validity and reliability was shown to be 88% with a Kappa value of 0.85 to a confidence level of 99% compared to known diagnosis by radiological imaging. Conclusions The results suggest that a Bayesian network-based CDSS is a promising instrument in the diagnosis of musculoskeletal shoulder conditions, having been shown to be valid and reliable for 50 case studies.