Health Services and Performance Research EA 7425, Université Claude Bernard Lyon 1, Lyon, France
Health Services and Performance Research EA 7425, Université Claude Bernard Lyon 1, Lyon, France.
BMJ Open. 2020 Mar 18;10(3):e033573. doi: 10.1136/bmjopen-2019-033573.
Chronic conditions require long periods of care and often involve repeated interactions with multiple healthcare providers. Faced with increasing illness burden and costs, healthcare systems are currently working towards integrated care to streamline these interactions and improve efficiency. To support this, one promising resource is the information on routine care delivery stored in various electronic healthcare databases (EHD). In chronic conditions, care delivery pathways (CDPs) can be constructed by linking multiple data sources and extracting time-stamped healthcare utilisation events and other medical data related to individual or groups of patients over specific time periods; CDPs may provide insights into current practice and ways of improving it. Several methods have been proposed in recent years to quantify and visualise CDPs. We present the protocol for a systematic review aiming to describe the content and development of CDP methods, to derive common recommendations for CDP construction.
This protocol followed the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. A literature search will be performed in PubMed (MEDLINE), Scopus, IEEE, CINAHL and EMBASE, without date restrictions, to review published papers reporting data-driven chronic CDPs quantification and visualisation methods. We will describe them using several characteristics relevant for EHD use in long-term care, grouped into three domains: (1) clinical (what clinical information does the method use and how was it considered relevant?), (2) data science (what are the method's development and implementation characteristics?) and (3) behavioural (which behaviours and interactions does the method aim to promote among users and how?). Data extraction will be performed via deductive content analysis using previously defined characteristics and accompanied by an inductive analysis to identify and code additional relevant features. Results will be presented in descriptive format and used to compare current CDPs and generate recommendations for future CDP development initiatives.
Database searches will be initiated in May 2019. The review is expected to be completed by February 2020. Ethical approval is not required for this review. Results will be disseminated in peer-reviewed journals and conference presentations.
CRD42019140494.
慢性病需要长期护理，通常涉及与多个医疗保健提供者的反复互动。面对不断增加的疾病负担和成本，医疗保健系统目前正在努力实现综合护理，以简化这些互动并提高效率。为此，一个有前途的资源是存储在各种电子医疗保健数据库（EHD）中的常规护理提供信息。在慢性病中，可以通过链接多个数据源并提取时间戳的医疗保健利用事件以及与特定时间段内个体或患者群体相关的其他医学数据来构建护理提供途径（CDP）；CDP 可以提供对当前实践的深入了解，并为改进实践提供思路。近年来，已经提出了几种量化和可视化 CDP 的方法。我们提出了一项系统评价的方案，旨在描述 CDP 方法的内容和发展，为 CDP 的构建得出共同建议。
本方案遵循系统评价和荟萃分析报告标准的建议。将在 2019 年 5 月对 PubMed（MEDLINE）、Scopus、IEEE、CINAHL 和 EMBASE 中的已发表文献进行无日期限制的检索，以审查报告数据驱动的慢性 CDP 量化和可视化方法的文献。我们将使用与 EHD 在长期护理中的使用相关的几个特征对其进行描述，分为三个领域：（1）临床（该方法使用哪些临床信息，以及如何认为这些信息是相关的？），（2）数据科学（该方法的开发和实施特征是什么？）和（3）行为（该方法旨在促进用户的哪些行为和交互，以及如何促进？）。将通过使用先前定义的特征进行演绎内容分析来提取数据，并通过归纳分析来识别和编码其他相关特征。结果将以描述性格式呈现，并用于比较当前的 CDP 并为未来的 CDP 开发计划生成建议。
数据库搜索将于 2019 年 5 月开始。预计将于 2020 年 2 月完成审查。本审查不需要伦理批准。结果将发表在同行评议的期刊和会议论文集中。
PROSPERO 注册号：CRD42019140494。
