Medical Informatics, Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
J Healthc Eng. 2023 May 29;2023:8550905. doi: 10.1155/2023/8550905. eCollection 2023.
Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.
在基于技术的解决方案中，临床决策支持系统 (CDSS) 具有以智能方式跟上临床医生最新证据的能力。因此，我们研究的主要目的是调查 CDSS 在慢性病方面的适用性和特点。从 2000 年 1 月至 2023 年 2 月，我们使用关键词在 Web of Science、Scopus、OVID 和 PubMed 数据库中进行了搜索。该综述是根据系统评价和荟萃分析的首选报告项目清单完成的。然后，对 CDSS 的特点和适用性进行了分析。使用混合方法评估工具清单 (MMAT) 评估评估质量。系统数据库搜索产生了 206 条引文。最终，来自 16 个国家的 38 篇文章符合纳入标准并被接受进行最终分析。所有研究的主要方法可以分为以下几类：遵循循证医学 (84.2%)、早期和准确诊断 (81.6%)、识别高危患者 (50%)、预防医疗差错 (47.4%)、向医疗保健提供者提供最新信息 (36.8%)、远程提供患者护理 (21.1%)和规范护理 (71.1%)。基于知识的 CDSS 中最常见的特征包括为医生提供指导和建议 (92.11%)、生成针对患者的建议 (84.21%)、集成到电子病历中 (60.53%)和使用警报或提醒 (60.53%)。在将证据知识转化为机器可解释知识的十三种不同方法中，34.21%的研究使用基于规则的逻辑技术，而 26.32%的研究使用基于规则的决策树建模。为了开发 CDSS 和翻译知识，应用了多种方法和技术。因此，信息学家应该考虑为基于知识的决策支持系统的开发制定一个标准框架。
