Decision Support for Managing Common Musculoskeletal PainDisorders:Development of a Case-Based Reasoning Application

Background: Common interventions for musculoskeletal pain disorders either lack evidence to support their use or have small to modest or short-term effects. Given the heterogeneity of patients with musculoskeletal pain disorders, treatment guidelines and systematic reviews have limited transferability to clinical practice. A problem-solving method in artificial intelligence, case-based reasoning (CBR), where new problems are solved based on experiences from past similar problems, might offer guidance in such situations. Objective: This study aims to use CBR to build a decision support system for patients with musculoskeletal pain disorders seeking physiotherapy care. This study describes the development of the CBR system Support Prim PT and demonstrates its ability to identify similar patients. Methods: Data from physiotherapy patients in primary care in Norway were collected to build a case base for Support Prim PT. We used the local-global principle in CBR to identify similar patients. The global similarity measures are attributes used to identify similar patients and consisted of prognostic attributes. They were weighted in terms of prognostic importance and choice of treatment, where the weighting represents the relevance of the different attributes. For the local similarity measures, the degree of similarity within each attribute was based on minimal clinically important differences and expert knowledge. The Support PrimPT's ability to identify similar patients was assessed by comparing the similarity scores of all patients in the case base with the scores on an established screening tool (the short form & Ouml;rebro Musculoskeletal Pain Screening Questionnaire [& Ouml;MSPQ]) and anoutcome measure (the Musculoskeletal Health Questionnaire [MSK-HQ]) used in musculoskeletal pain. We also assessed the same in a more extensive case base. Results: The original case base contained 105 patients with musculoskeletal pain (mean age 46, SD 15 years; 77/105, 73.3%women). The Support Prim PT consisted of 29 weighted attributes with local similarities. When comparing the similarity scores for all patients in the case base, one at a time, with the & Ouml;MSPQ and MSK-HQ, the most similar patients had a mean absolute difference from the query patient of 9.3 (95% CI 8.0-10.6) points on the & Ouml;MSPQ and a mean absolute difference of 5.6 (95% CI4.6-6.6) points on the MSK-HQ. For both & Ouml;MSPQ and MSK-HQ, the absolute score difference increased as the rank of most similar patients decreased. Patients retrieved from a more extensive case base (N=486) had a higher mean similarity score and were slightly more similar to the query patients in & Ouml;MSPQ and MSK-HQ compared with the original smaller case base. Conclusions: This study describes the development of a CBR system, Support Prim PT, for musculoskeletal pain in primary care. The Support Prim PT identified similar patients according to an established screening tool and an outcome measure for patients with musculoskeletal pain.