Bertl Markus, Metsallik Janek, Ross Peeter
Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, Tallinn, Estonia.
Front Psychiatry. 2022 Aug 9;13:923613. doi: 10.3389/fpsyt.2022.923613. eCollection 2022.
Over the last decade, an increase in research on medical decision support systems has been observed. However, compared to other disciplines, decision support systems in mental health are still in the minority, especially for rare diseases like post-traumatic stress disorder (PTSD). We aim to provide a comprehensive analysis of state-of-the-art digital decision support systems (DDSSs) for PTSD.
Based on our systematic literature review of DDSSs for PTSD, we created an analytical framework using thematic analysis for feature extraction and quantitative analysis for the literature. Based on this framework, we extracted information around the medical domain of DDSSs, the data used, the technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level. Extracting data for all of these framework dimensions ensures consistency in our analysis and gives a holistic overview of DDSSs.
Research on DDSSs for PTSD is rare and primarily deals with the algorithmic part of DDSSs ( = 17). Only one DDSS was found to be a usable product. From a data perspective, mostly checklists or questionnaires were used ( = 9). While the median sample size of 151 was rather low, the average accuracy was 82%. Validation, excluding algorithmic accuracy (like user acceptance), was mostly neglected, as was an analysis concerning possible user groups.
Based on a systematic literature review, we developed a framework covering all parts (medical domain, data used, technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level) of DDSSs. Our framework was then used to analyze DDSSs for post-traumatic stress disorder. We found that DDSSs are not ready-to-use products but are mostly algorithms based on secondary datasets. This shows that there is still a gap between technical possibilities and real-world clinical work.
在过去十年中，医学决策支持系统的研究有所增加。然而，与其他学科相比，心理健康领域的决策支持系统仍然较少，尤其是针对创伤后应激障碍（PTSD）等罕见疾病。我们旨在对PTSD的最新数字决策支持系统（DDSS）进行全面分析。
基于对PTSD的DDSS的系统文献综述，我们创建了一个分析框架，使用主题分析进行特征提取，并对文献进行定量分析。基于此框架，我们提取了围绕DDSS医学领域、使用的数据、数据收集技术、用户交互、决策、用户群体、验证、决策类型和成熟度水平的信息。提取所有这些框架维度的数据可确保我们分析的一致性，并对DDSS进行全面概述。
关于PTSD的DDSS的研究很少，主要涉及DDSS的算法部分（n = 17）。仅发现一个DDSS是可用产品。从数据角度来看，大多使用清单或问卷（n = 9）。虽然151的中位数样本量相当低，但平均准确率为82%。除算法准确性（如用户接受度）外，验证大多被忽视，对可能的用户群体的分析也是如此。
基于系统的文献综述，我们开发了一个涵盖DDSS所有部分（医学领域、使用的数据、数据收集技术、用户交互、决策、用户群体、验证、决策类型和成熟度水平）的框架。然后我们使用该框架分析创伤后应激障碍的DDSS。我们发现DDSS不是现成可用的产品，大多是基于二次数据集的算法。这表明技术可能性与实际临床工作之间仍存在差距。
