Musculoskeletal conditions are the dominant source of chronic pain worldwide, and the basis for the most common pain complaints presented to primary care. Conditions such as low back pain (LPB) and osteoarthritis (OA) have a significant impact on individuals and healthcare systems.
One of the case studies in this project focuses on physiotherapy practice on patients with rheumatological conditions. Our aim is to build a decision support model that evaluates the biopsychosocial state of the patient based on Patient Reported Outcome Measures (PROMs) and predicts the outcome of alternative therapies for improving the patient’s condition. We use Bayesian Network (BN) technology to enhance PROMs, enable them to ask patient-specific questions and deal with missing inputs. The PROM we used for this study is called BETY-Biopsychosocial Questionnaire and it is designed for patients with rheumatological conditions. It is composed of 30 questions and it measures function, pain, anxiety, depression and sexuality factors. The BN also aims to make patient-specific predictions of potential improvement for these factors with different therapy alternatives including group exercise, self-exercise and pain management.