Hoang Minh Trang, Donnelly Candice, Igasto Christina, Shetty Amith, Pradhan Malcolm, Shaw Tim
Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia, 61 401333970.
Service NSW, Sydney, Australia.
J Med Internet Res. 2026 Feb 9;28:e79937. doi: 10.2196/79937.
Best practice standards aim to standardize care and improve outcomes. However, variation in clinical practice exists, and not all deviations are inappropriate. Measuring adherence to best practice standards remains challenging due to limitations in representation methods and data fidelity.
This scoping review aims to survey and synthesize the existing literature on the computable representation of guideline recommendations and to explore methods for detecting and quantifying deviations from best practice standards.
We followed the Arksey and O'Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Five databases (Ovid Medline, EMBASE, IEEE Xplore, Web of Science, and Scopus) were searched in November 2025. Studies were included if they either (1) described a computer representation of best practice standards or (2) assessed adherence to such standards using patient data, including patient data derived from electronic medical records or event logs. Screening was done using Covidence (Veritas Health Innovation). Data were extracted on representation, clinical context, data sources, adherence metrics, and modeling techniques. A narrative synthesis was conducted to identify themes.
Twenty-four studies were included. Most studies were published as conference proceedings (13/24, 54%). Fourteen studies (14/24, 58%) included measurement of adherence to best practice standards. Cardiovascular conditions were the most common focus (13/24, 54%). Data sources included Health Level Seven (HL7) messages, structured electronic medical record data, event logs, and Fast Healthcare Interoperability Resources (FHIR)-transformed data. Best practice standards were formalized using Business Process Model and Notation (BPMN; 6/24, 25%), ontologies (7/24, 29%), FHIR (4/24, 17%), or hybrid approaches (4/24, 17%). The most common method for adherence measurement was rule-based alignment. Several studies incorporated weighted scoring to differentiate the severity of deviations. Process mining was used in a subset to detect sequence and timing variations. However, most models lacked contextual sensitivity and rarely incorporated patient-specific factors, such as comorbidities, patient acuity, or clinician rationale. Consequently, although deviations can be automatically identified, determining whether they were clinically warranted remained largely unresolved.
Despite promising advances, challenges persist in computer-interpretable representation and measuring adherence in a clinically meaningful way. Current approaches predominantly assess technical alignment rather than clinical relevance and are limited by data quality and standardization, thereby limiting real-world utility. This scoping review offers an innovative contribution by synthesizing evidence from 2 separate domains-the computable representation of best practice standards and the measurement of adherence. The findings emphasize the need for context-aware, standardized modeling and integration with clinical workflows to distinguish warranted from unwarranted deviations. Such advances are essential for scalable, transparent, and real-time adherence monitoring-ultimately driving safer, patient-centered care delivery.
最佳实践标准旨在规范医疗服务并改善治疗效果。然而，临床实践中存在差异，并非所有偏差都是不适当的。由于表示方法和数据保真度的限制，衡量对最佳实践标准的依从性仍然具有挑战性。
本范围综述旨在调查和综合关于指南建议的可计算表示的现有文献，并探索检测和量化与最佳实践标准偏差的方法。
我们遵循了阿克西和奥马利框架以及PRISMA-ScR（系统评价和元分析扩展的范围综述的首选报告项目）指南。2025年11月对五个数据库（Ovid Medline、EMBASE、IEEE Xplore、Web of Science和Scopus）进行了检索。如果研究符合以下条件之一，则纳入研究：（1）描述了最佳实践标准的计算机表示；（2）使用患者数据评估对这些标准的依从性，包括从电子病历或事件日志中获取的患者数据。使用Covidence（Veritas Health Innovation）进行筛选。提取了关于表示、临床背景、数据源、依从性指标和建模技术的数据。进行了叙述性综合以确定主题。
纳入了24项研究。大多数研究以会议论文形式发表（13/24，54%）。14项研究（14/24，58%）包括对最佳实践标准依从性的测量。心血管疾病是最常见的关注焦点（13/24，54%）。数据源包括健康级别七（HL7）消息、结构化电子病历数据、事件日志和快速医疗保健互操作性资源（FHIR）转换的数据。最佳实践标准使用业务流程模型和符号（BPMN；6/24，25%）、本体（7/24，29%）、FHIR（4/24，17%）或混合方法（4/24，17%）进行形式化。最常见的依从性测量方法是基于规则的对齐。几项研究采用加权评分来区分偏差的严重程度。在一部分研究中使用了过程挖掘来检测序列和时间变化。然而，大多数模型缺乏上下文敏感性，很少纳入患者特异性因素，如合并症、患者 acuity或临床医生的理由。因此，尽管可以自动识别偏差，但确定它们在临床上是否合理在很大程度上仍未得到解决。
尽管取得了有希望的进展，但在计算机可解释表示和以临床有意义的方式测量依从性方面仍然存在挑战。当前方法主要评估技术对齐而非临床相关性，并受到数据质量和标准化的限制，从而限制了实际应用。本范围综述通过综合来自两个不同领域的证据——最佳实践标准的可计算表示和依从性测量，提供了创新性贡献。研究结果强调了需要上下文感知、标准化建模以及与临床工作流程集成，以区分合理偏差和不合理偏差。这些进展对于可扩展、透明和实时的依从性监测至关重要——最终推动更安全、以患者为中心的医疗服务提供。
