Hill Adele, Joyner Christopher H, Keith-Jopp Chloe, Yet Barbaros, Tuncer Sakar Ceren, Marsh William, Morrissey Dylan
Sport and Exercise Medicine, Queen Mary University of London, London, United Kingdom.
Electronics, Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.
JMIR Res Protoc. 2021 Jan 15;10(1):e21804. doi: 10.2196/21804.
Low back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with consistent demonstration of increasing persistent pain and disability. Previous decision support tools for LBP management have focused on a subset of factors owing to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian network, which will entail constructing a clinical reasoning model elicited from experts.
This paper proposes a method for conducting a modified RAND appropriateness procedure to elicit the knowledge required to construct a Bayesian network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure.
We propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialties, such as orthopedics, rheumatology, and sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face-to-face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face-to-face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian network. Stage 4 is a rudimentary validation of the Bayesian network.
Ethical approval has been obtained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of three remote activities and two in-person meetings was required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms, even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a Bayesian network. The use of the internal pilot was recognized as being a methodological necessity.
We have proposed a method to construct Bayesian networks that are representative of expert clinical reasoning for a musculoskeletal condition in this case. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process to model clinical reasoning for a range of conditions.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21804.
腰痛（LBP）对患者和医疗专业人员来说都是日益沉重的负担，持续疼痛和残疾的情况不断增加。由于时间限制和临床医生使用方便，以前用于腰痛管理的决策支持工具只关注了一部分因素。随着对机器学习工具的兴趣激增以及西方政府引入这项技术的承诺，有机会开发智能决策支持工具。我们将使用贝叶斯网络针对腰痛开展此项工作，这需要构建一个从专家那里获取的临床推理模型。
本文提出一种方法，用于进行改良的兰德适宜性程序，以从一组腰痛领域专家那里获取构建贝叶斯网络所需的知识，并报告从该程序的内部试点中吸取的经验教训。
我们提议从骨科、风湿病学和运动医学等一系列医学专业中招募对腰痛有特殊兴趣的专家临床医生。该程序将包括四个阶段。第一阶段是在线征集模型要考虑的变量，随后是面对面研讨会。第二阶段是在线征集模型的结构，随后是面对面研讨会。第三阶段包括一个在线阶段，以获取填充贝叶斯网络的概率。第四阶段是对贝叶斯网络进行初步验证。
已获得伦敦玛丽女王大学研究伦理委员会的伦理批准。该程序已与研究团队的临床同事进行了内部试点。这表明需要三个远程活动和两次面对面会议交替进行的过程才能完成征集，而不会给参与者造成过重负担。吸取的经验教训包括需要一个定制的在线征集工具在面对面会议之间运行，以及对描述性术语进行仔细的操作定义，即使这些术语在临床上广泛使用。此外，需要工具来远程提供关于各种形式认知偏差自我识别的培训，并解释贝叶斯网络的基本原理。内部试点的使用被认为是一种方法上的必要。
我们提出了一种构建贝叶斯网络的方法，在此案例中该网络代表了针对肌肉骨骼疾病的专家临床推理。我们在部署之前通过内部试点对该方法进行了测试以完善流程，这表明该流程可能会成功。内部试点还揭示了为一系列病症的临床推理建模的征集过程所需的软件支持要求。
国际注册报告识别号（IRRID）：DERR1-10.2196/21804。
