O'Sullivan Dympna, Woensel William Van, Wilk Szymon, Tu Samson W, Michalowski Wojtek, Abidi Samina, Carrier Marc, Edry Ruth, Hochberg Irit, Kingwell Stephen, Kogan Alexandra, Michalowski Martin, O'Sullivan Hugh, Peleg Mor
ASCNet Research Group, Technological University Dublin, Dublin, Ireland.
NICHE Research Group, Dalhousie University, Halifax, Canada.
AMIA Annu Symp Proc. 2022 Feb 21;2021:920-929. eCollection 2021.
Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework.
多重疾病，即两种或更多健康状况的并存，随着许多国家死亡率下降和人口老龄化而变得更加普遍。多重疾病给临床决策支持系统（CDSS）带来了重大困难，尤其是在相关临床指南的建议相互冲突的情况下。一些研究小组正在开发计算机可解释指南（CIG）建模形式，将来自多个临床实践指南（CPG）的建议整合起来，用于基于知识的多重疾病决策支持。在本文中，我们描述了为开发一个比较基于多重疾病CIG的临床决策支持（MGCDS）不同方法的框架所做的工作。我们展示了（1）一组MGCDS的特征，这些特征是通过文献综述得出并由医生通过调查进行评估的，以及（2）一组基准案例研究，这些案例说明了这些特征的临床应用。这项工作是一个更广泛研究计划的第一步，该计划旨在开发一个基准框架，以实现标准化和可比的MGCDS评估，这将有助于评估MGCDS的功能，并突出当前技术水平中的重要差距。我们还概述了我们在开发该框架方面的未来工作，具体而言，（3）为基准案例研究报告MGCDS解决方案的标准，以及（4）评估这些MGCDS解决方案的标准。我们计划基于该比较框架对现有的MGCDS进行大规模比较研究。
