Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, California, USA.
Pain Pract. 2024 Jul;24(6):856-865. doi: 10.1111/papr.13364. Epub 2024 Mar 11.
The learning healthcare system (LHS) has been developed to integrate patients' clinical data into clinical decisions and improve treatment outcomes. Having little guidance on this integration process, we aim to explain (a) an applicable analytic tool for clinicians to evaluate the clinical outcomes at a group and an individual level and (b) our quality improvement (QI) project, analyzing the outcomes of a new outpatient pain rehabilitation program ("Back-in-Action": BIA) and applying the analysis results to modify our clinical practice.
Through our LHS (CHOIR; https://choir.stanford.edu), we administered the Pain Catastrophizing Scale (PCS), Chronic Pain Acceptance Questionnaire (CPAQ), and Patient-Reported Outcomes Measures (PROMIS)® before and after BIA. After searching for appropriate analytic tools, we decided to use the Reliable Change Index (RCI) to determine if an observed change in the direction of better (improvement) or worse (deterioration) would be beyond or within the measurement error (no change).
Our RCI calculations revealed that at least a 9-point decrease in the PCS scores and 10-point increase in the CPAQ scores would indicate reliable improvement. RCIs for the PROMIS measures ranged from 5 to 8 T-score points (i.e., 0.5-0.8 SD). When evaluating change scores of the PCS, CPAQ, and PROMIS measures, we found that 94% of patients showed improvement in at least one domain after BIA and 6% showed no reliable improvement.
Our QI project revealed RCI as a useful tool to evaluate treatment outcomes at a group and an individual level, and RCI could be incorporated into the LHS to generate a progress report automatically for clinicians. We further explained how clinicians could use RCI results to modify a clinical practice, to improve the outcomes of a pain program, and to develop individualized care plans. Lastly, we suggested future research areas to improve the LHS application in pain practice.
学习型医疗保健系统（LHS）旨在将患者的临床数据纳入临床决策中，以改善治疗效果。由于对这一整合过程几乎没有指导，我们旨在解释（a）一种适用于临床医生的分析工具，用于评估群体和个体层面的临床结果，以及（b）我们的质量改进（QI）项目，分析新的门诊疼痛康复计划（“回归行动”：BIA）的结果，并将分析结果应用于修改我们的临床实践。
通过我们的 LHS（CHOIR；https://choir.stanford.edu），我们在 BIA 前后分别使用疼痛灾难化量表（PCS）、慢性疼痛接受问卷（CPAQ）和患者报告的结果测量（PROMIS）®进行评估。在寻找合适的分析工具后，我们决定使用可靠变化指数（RCI）来确定观察到的改善（进步）或恶化（恶化）方向的变化是否超出或在测量误差范围内（无变化）。
我们的 RCI 计算结果表明，PCS 评分至少降低 9 分，CPAQ 评分至少增加 10 分，表明可靠的改善。PROMIS 测量的 RCI 范围为 5 到 8 个 T 分数点（即 0.5-0.8 个标准差）。当评估 PCS、CPAQ 和 PROMIS 测量的变化分数时，我们发现 94%的患者在 BIA 后至少在一个领域有所改善，6%的患者没有可靠的改善。
我们的 QI 项目表明 RCI 是一种有用的工具，可以评估群体和个体层面的治疗效果，并且 RCI 可以纳入 LHS 中，为临床医生自动生成进展报告。我们进一步解释了临床医生如何使用 RCI 结果来修改临床实践，以改善疼痛计划的结果，并制定个性化的护理计划。最后，我们提出了未来的研究领域，以改善 LHS 在疼痛实践中的应用。
