Algoritmi Research Center, University of Minho, Braga, Portugal.
PLoS One. 2022 Aug 15;17(8):e0272846. doi: 10.1371/journal.pone.0272846. eCollection 2022.
Clinical Decision Support Systems (CDSS) are used to assist the decision-making process in the healthcare field. Developing an effective CDSS is an arduous task that can take advantage from prior assessment of the most promising theories, techniques and methods used at the present time.
To identify the features of Clinical Decision Support Systems and provide an analysis of their effectiveness. Thus, two research questions were formulated: RQ1-What are the most common trend characteristics in a CDSS? RQ2-What is the maturity level of the CDSS based on the decision-making theory proposed by Simon?
AIS e-library, Decision Support Systems journal, Nature, PlosOne and PubMed were selected as information sources to conduct this systematic literature review. Studies from 2000 to 2020 were chosen covering search terms in CDSS, selected according to defined eligibility criteria. The data were extracted and managed in a worksheet, based on the defined criteria. PRISMA statements were used to report the systematic review.
The outcomes showed that rule-based module was the most used approach regarding knowledge management and representation. The most common technological feature adopted by the CDSS were the recommendations and suggestions. 19,23% of studies adopt the type of system as a web-based application, and 51,92% are standalone CDSS. Temporal evolution was also possible to visualize. This study contributed to the development of a Maturity Staging Model, where it was possible to verify that most CDSS do not exceed level 2 of maturity.
The trend characteristics addressed in the revised CDSS were identified, compared to the four predefined groups. A maturity stage model was developed based on Simon's decision-making theory, allowing to assess the level of maturity of the most common features of the CDSS. With the application of the model, it was noticed that the phases of choice and implementation are underrepresented. This constitutes the main gap in the development of an effective CDSS.
临床决策支持系统（CDSS）用于协助医疗保健领域的决策过程。开发有效的 CDSS 是一项艰巨的任务，可以利用当前最有前途的理论、技术和方法的预先评估。
确定临床决策支持系统的特征，并对其有效性进行分析。因此，提出了两个研究问题：RQ1-CDSS 最常见的趋势特征是什么？RQ2-根据 Simon 提出的决策理论，CDSS 的成熟度水平如何？
选择 AIS e-library、Decision Support Systems 杂志、Nature、PlosOne 和 PubMed 作为信息来源，进行这项系统文献综述。选择 2000 年至 2020 年的研究，涵盖 CDSS 中的搜索词，根据定义的合格标准进行选择。根据定义的标准，在工作表中提取和管理数据。使用 PRISMA 语句报告系统评价。
结果表明，基于规则的模块是知识管理和表示方面最常用的方法。CDSS 采用的最常见技术特征是建议和建议。19.23%的研究采用基于网络的应用程序类型，51.92%是独立的 CDSS。还可以可视化时间演变。本研究有助于开发成熟度阶段模型，其中可以验证大多数 CDSS 不超过成熟度级别 2。
确定了修订后的 CDSS 中解决的趋势特征，与四个预定义组进行了比较。根据 Simon 的决策理论开发了一个成熟度阶段模型，用于评估 CDSS 最常见特征的成熟度水平。通过应用该模型，注意到选择和实施阶段代表性不足。这是开发有效的 CDSS 的主要差距。
