用于支持肩部疾病患者诊断和分诊的数字临床决策支持系统的识别：一项范围综述方案。
Identification of digital clinical decision support systems for supporting diagnosis and triage of patients with shoulder disorders: A scoping review protocol.
作者信息
Schamerhorn Cheyenne R, Peñas Nathaniel M, Fletcher Jared R, Hayman Richard, Eubank Breda H F
机构信息
Department of Health and Physical Education, Faculty of Health, Community, & Education, Mount Royal University, Calgary, Alberta, Canada.
Department of Mathematics & Computing, Faculty of Science & Technology, Mount Royal University, Calgary Alberta, Canada.
出版信息
PLoS One. 2025 Jul 1;20(7):e0327192. doi: 10.1371/journal.pone.0327192. eCollection 2025.
BACKGROUND
Clinical decision support systems (CDSSs) are computerized tools that support clinical decision-making processes. Primary care decision-making is complex and has the potential to influence quality of care provided and patient outcomes. CDSS not only assist providers with clinical decision-making to ensure quality standards are met, reflect evidence-informed practice, and reduce variation in care, but also help patients navigate and receive an appropriate care pathway amidst numerous, often complex, options. Therefore, this scoping review will aim to identify existing CDSSs for supporting primary point-of-care providers, directing patients to appropriate management pathways, and supporting the clinical examination (i.e., medical history-taking and physical examination) process for patients with shoulder disorders. At the primary point-of-care system level, a CDSS for shoulder disorders will improve clinical efficiency and support decision-making.
METHODS
Scoping review methodology and reporting will be conducted according to Arksey and O'Malley's 6-step framework, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P), and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews (PRISMA-ScR) reporting guide. A robust search strategy will be applied across four databases: MEDLINE (Ovid), EMBASE (Ovid), CINAHL (Ebsco), and Scopus (Elsevier). Two blinded reviewers will independently evaluate all titles and corresponding abstracts based on pre-specified inclusion and exclusion criteria. Inter-rater reliability (IRR) agreement will be established during an initial pilot-screening phase against a random selection of 20 records (minimum) until reaching Cohen's Kappa ≥ 0.81. Data extraction will be completed by one reviewer and validated by a second.
DISCUSSION
An effective and high-quality CDSS that is affordable, easy to use, easily accessible, compatible with existing clinical processes, and generalizable across diverse settings will help to support primary point-of-care providers in diagnosing and managing patients presenting with shoulder disorders, thus improving quality of care for patients.
背景
临床决策支持系统（CDSS）是支持临床决策过程的计算机化工具。初级保健决策复杂，有可能影响所提供的医疗质量和患者预后。CDSS不仅协助医疗服务提供者进行临床决策，以确保达到质量标准、反映循证实践并减少护理差异，还帮助患者在众多通常很复杂的选项中找到并接受合适的护理途径。因此，本范围综述旨在识别现有的CDSS，以支持基层医疗服务提供者，引导患者选择合适的管理途径，并支持肩部疾病患者的临床检查（即病史采集和体格检查）过程。在基层医疗系统层面，针对肩部疾病的CDSS将提高临床效率并支持决策。
方法
将根据阿克西和奥马利的六步框架、系统评价和Meta分析方案的首选报告项目（PRISMA-P）以及系统评价和Meta分析的首选报告项目（PRISMA）扩展版范围综述报告指南（PRISMA-ScR）进行范围综述方法和报告。将在四个数据库中应用强大的检索策略：MEDLINE（Ovid）、EMBASE（Ovid）、CINAHL（Ebsco）和Scopus（爱思唯尔）。两名盲法评审员将根据预先指定的纳入和排除标准独立评估所有标题和相应摘要。在初始预筛选阶段，将针对随机选择的至少20条记录建立评分者间信度（IRR）一致性，直至达到科恩kappa系数≥0.81。数据提取将由一名评审员完成，并由另一名评审员进行验证。
讨论
一个有效且高质量、价格合理、易于使用、易于获取、与现有临床流程兼容且可在不同环境中推广的CDSS，将有助于支持基层医疗服务提供者诊断和管理肩部疾病患者，从而提高患者的护理质量。
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