A decision support system for lower back pain diagnosis: Uncertainty management and clinical evaluations

Abstract

Lower back pain (LBP) is a common medical problem that deprives many individuals of their normal lifestyles and keeps them from routine activities. Diagnosing LBP is challenging because it requires highly specialized knowledge involving a complex anatomical and physiological structure as well as diverse clinical considerations. Although a handful of studies have proposed or developed systems to support LBP diagnosis and improve knowledge sharing, these systems have limited scope, lack systematic evaluations, and/or ignore diagnoses that consist of multiple parts (i.e., decision outcomes), each of which corresponds to a particular medical condition, disease, or abnormality. In this study, we design, implement, and evaluate a Web-based decision support system that employs an intuitive and easy-to-use framework to assess the patient's information and recommend a diagnosis consisting of one or multiple parts. Our system design addresses the challenging characteristics of a LBP diagnosis and uses verbal probability estimation to represent and reason about the associated uncertainty. Our evaluations are systematic, including knowledge base verification, system validation using a modified Turing test, and clinical efficacy assessment involving 5 clinicians and 180 real-world cases collected from geographically dispersed clinics. Our evaluation design is more thorough than those used by most previous studies, and the proposed system is relatively ready for clinical deployment. Therefore, this study both contributes to decision support systems research and has advanced clinical support for LBP diagnosis. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.