Background: We have developed a clinical decision support system (CDSS) based on methods from artificial intelligence to support physiotherapists and patients in the decision-making process of managing musculoskeletal (MSK) pain disorders in primary care. The CDSS finds the most similar successful patients from the past to give treatment recommendations for a new patient. Using previous similar patients with successful outcomes to advise treatment moves management of MSK pain patients from one-size fits all recommendations to more individually tailored treatment. This study aimed to summarise the development and explore the acceptance and use of the CDSS for MSK pain patients.
Methods: This qualitative study was carried out in the Norwegian physiotherapy primary healthcare sector between October and November 2020, ahead of a randomised controlled trial. We included four physiotherapists and three of their patients, in total 12 patients, with musculoskeletal pain in the neck, shoulder, back, hip, knee or complex pain. We conducted semi-structured telephone interviews with all participants. The interviews were analysed using the Framework Method.
Results: Overall, both the physiotherapists and patients found the system acceptable and usable. Important findings from the analysis of the interviews were that the CDSS was valued as a preparatory and exploratory tool, facilitating the therapeutic relationship. However, the physiotherapists used the system mainly to support their previous and current practice rather than involving patients to a greater extent in decisions and learning from previous successful patients.
Conclusions: The CDSS was acceptable and usable to both the patients and physiotherapists. However, the system appeared not to considerably influence the physiotherapists' clinical reasoning and choice of treatment based on information from most similar successful patients. This could be due to a smaller than optimal number of previous patients in the CDSS or insufficient clinical implementation. Extensive training of physiotherapists should not be underestimated to build understanding and trust in CDSSs.
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http://dx.doi.org/10.1186/s12911-023-02399-7 | DOI Listing |
Cureus
November 2024
Radiology, St. Vincent's University Hospital, Dublin, IRL.
The number of citations an article receives is reflective of its impact on the scientific community. The top 100 most cited articles were identified using the Web of Science database. Data relating to the publication year, publishing journal, number of citations, primary institution, journal impact factor, authorship, country of origin, radiological modality, and keywords were collected.
View Article and Find Full Text PDFRheumatology (Oxford)
December 2024
NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Objective: To propose a new definition for SLEDAI arthritis informed by imaging.
Methods: We performed a planned secondary analysis of observational data from a multicentre study evaluating SLE patients with inflammatory joint pain (swelling not required) using various clinical instruments, laboratory tests and ultrasound. For SLEDAI arthritis, assessors (blinded to ultrasound) were asked which of the glossary terms for arthritis in any version of the SLEDAI drove their decision to score for arthritis.
SSM Qual Res Health
December 2024
Department of Clinical Research, Kilimanjaro Clinical Research Institute, Moshi, United Republic of Tanzania.
The increased prevalence of non-communicable diseases (NCDs) in recent years has led many Low- and Middle-Income Countries (LMICs), including Tanzania, to develop policies to manage their burden. Musculoskeletal (MSK) conditions, such as arthritis, account for 20% of all years lived with disability in LMICs, but the NCD strategies rarely address them. There is substantial research on the disruption MSK conditions cause to people's lives within High-Income Countries, but very little is known about the lived experiences in LMICs.
View Article and Find Full Text PDFBraz J Phys Ther
December 2024
Noordwest Ziekenhuisgroep Alkmaar, Rehabilitation Department, Alkmaar, the Netherlands.
Background: The McKenzie Method of Mechanical Diagnosis and Therapy (MDT) is used worldwide to classify and manage musculoskeletal (MSK) problems. The assessment includes a detailed patient history and a specific physical examination. Research has investigated the reliability of the MDT spinal classification system (Derangement syndrome, Dysfunction syndrome, Postural syndrome, and OTHER), however no study has assessed the reliability of the 10 classifications grouped together as OTHER.
View Article and Find Full Text PDFCureus
November 2024
Department of Orthopedics, College of Medicine, King Saud University, Riyadh, SAU.
Background And Objectives: Orthopedic surgeons' demanding work may negatively affect their health. This study examines the prevalence of musculoskeletal (MSK) issues, specifically back and neck pain, among orthopedic surgeons in Riyadh, Saudi Arabia, and explores contributing sociodemographic factors.
Materials And Methods: We conducted an observational study that assessed the prevalence of back and neck pain among certified orthopedic surgeons using an online survey, which included Logistic regression for risk factors, one-way ANOVA for disability-contributing factors, and Tukey's post-hoc test for subgroup analysis.
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