Subproject 1 uses innovative machine learning methods (case-based reasoning; CBR) to optimize personalized treatment for patients with common musculoskeletal disorders in primary care physiotherapy. During the first year of the project the CBR system was completed, where patient data is integrated with the machine learning system. Furthermore, the software ("user interface") that produces the patient data and the decision support system for clinicians and patients has been completed. The randomized controlled trial (RCT 1) in physiotherapy practice began in February 2021. Despite covid challenges, status per early September 2021 is that 630 of the planned 720 patients are recruited. We expect that the 3-month follow-up of the last patient will be in April 2022, which is according to plan.
A pilot study including process evaluation with interviews of users was conducted in November 2020 and provided useful information that was incorporated into the decision support system before the start of the RCT study.
Subproject 2 groups patients with common musculoskeletal disorders among GPs into equal prognostic groups ("strata"). The groups represent different phenotypes based on prognostic factors for long-term ailments. The randomized controlled trial (RCT 2) that will examine the effect on patient-reported outcomes is being planned with partners at Keele University, UK. However, this sub-project has been affected by covid restrictions where the hired PhD student from Mexico did not receive entry permit before May 2021, which was 9 months later than planned. The planned start-up for this RCT is now March 1, 2022.
Musculoskeletal disorders (MSDs) is the number one cause of years lived with disability andreduced health worldwide. In Norway, every fourth patient in primary care suffers from MSDs. Treatment effects are however modest and knowledge
of best practice limited. The SUPPORTPRIM project will address these challenges in two main steps:1) To optimize person-centered care, we will employ innovative methods from artificial intelligence in terms of Case-Based Reasoning to build a clinical decision support system (cDSS ) based on patient data already collected in primary care physiotherapy. Case-Based Reasoning aims to solve new problems based on the solutions to similar problems in the past. In other words, previous MSD cases will be used to help similar cases in the future, just as humans learn from their own experience. We will then assess the efficacy of the cDSS in physiotherapy practice in a randomised controlled trial.2) This effort will be expanded to general practice by implementing The STarT MSK screening Tool as basis for stratified care for MSD patients. The efficacy of the stratified care will be assessed in a randomized controlled trial in general practice. Finally, the cDSS from physiotherapy practice will be extended and adapted to fit general practice. State-of-the-art personalized treatment plans are envisioned to benefit a much larger proportion of MSD patients than a "one-size-fits-all" approach. In addition, SupportPRIM facilitates and emphasizes the co-decision process between the patient and physiotherapist. The innovative approach using artificial intelligence to develop personalised decision support systems are far beyond current state of art, is highly relevant for other patient groups in health care, and can be integrated in future medical record systems.