We developed the SupportPrim PT clinical decision support system (CDSS) using the artificial intelligence method case-based reasoning to support personalised musculoskeletal pain management. The aim of this study was to evaluate the effectiveness of the CDSS for patients in physiotherapy practice. A cluster randomised controlled trial was conducted in primary care in Norway. We randomised 44 physiotherapists to (1) use the CDSS alongside usual care or (2) usual care alone. The CDSS provided personalised treatment recommendations based on a case base of 105 patients with positive outcomes. During the trial, the case-based reasoning system did not have an active learning capability; therefore, the case base size remained the same throughout the study. We included 724 patients presenting with neck, shoulder, back, hip, knee, or complex pain (CDSS; n = 358, usual care; n = 366). Primary outcomes were assessed with multilevel logistic regression using self-reported Global Perceived Effect (GPE) and Patient-Specific Functional Scale (PSFS). At 12 weeks, 165/298 (55.4%) patients in the intervention group and 176/321 (54.8%) in the control group reported improvement in GPE (odds ratio, 1.18; confidence interval, 0.50-2.78). For PSFS, 173/290 (59.7%) patients in the intervention group and 218/310 (70.3%) in the control group reported clinically important improvement in function (odds ratio, 0.41; confidence interval, 0.20-0.85). No significant between-group differences were found for GPE. For PSFS, there was a significant difference favouring the control group, but this was less than the prespecified difference of 15%. We identified several study limitations and recommend further investigation into artificial intelligence applications for managing musculoskeletal pain.

中文翻译：

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在初级物理治疗护理中管理肌肉骨骼疼痛患者的个性化决策支持：一项整群随机对照试验（SupportPrim 项目）。


我们使用人工智能方法基于案例的推理开发了 SupportPrim PT 临床决策支持系统 （CDSS），以支持个性化的肌肉骨骼疼痛管理。本研究的目的是评估 CDSS 对物理治疗实践患者的有效性。在挪威的初级保健机构进行了一项整群随机对照试验。我们随机分配了 44 名物理治疗师 （1） 将 CDSS 与常规护理一起使用或 （2） 单独使用常规护理。CDSS 根据 105 名具有积极结果的患者的病例基础提供个性化的治疗建议。在试验期间，基于案例的推理系统不具备主动学习能力;因此，病例基数在整个研究过程中保持不变。我们纳入了 724 例表现为颈部、肩部、背部、髋部、膝关节或复杂疼痛的患者 （CDSS;n = 358，常规护理;n = 366）。使用自我报告的整体感知效应 （GPE） 和患者特异性功能量表 （PSFS） 通过多水平 logistic 回归评估主要结局。在 12 周时，干预组 165/298 （55.4%） 患者和对照组 176/321 （54.8%） 患者报告 GPE 改善 （比值比，1.18;置信区间，0.50-2.78）。对于 PSFS，干预组 173/290 （59.7%） 患者和对照组 218/310 （70.3%） 患者报告了临床上重要的功能改善 （比值比，0.41;置信区间，0.20-0.85）。GPE 未发现显著的组间差异。对于 PSFS，对照组存在显著差异，但这小于预先指定的 15% 差异。我们确定了几个研究局限性，并建议进一步研究人工智能在管理肌肉骨骼疼痛中的应用。