A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support

• Published in: Journal of biomedical informatics 2023-06, Vol.142, p.104395-104395, Article 104395
• Main Authors: Van Woensel, William, Tu, Samson W., Michalowski, Wojtek, Sibte Raza Abidi, Syed, Abidi, Samina, Alonso, Jose-Ramon, Bottrighi, Alessio, Carrier, Marc, Edry, Ruth, Hochberg, Irit, Rao, Malvika, Kingwell, Stephen, Kogan, Alexandra, Marcos, Mar, Martínez Salvador, Begoña, Michalowski, Martin, Piovesan, Luca, Riaño, David, Terenziani, Paolo, Wilk, Szymon, Peleg, Mor
• Format: Article
• Language: English
• Subjects: Benchmarking; Clinical; Computer-interpretable clinical guidelines; Decision support systems; Evaluation study; Multimorbidity
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2023.104395

[Display omitted] The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans. Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods—research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods. Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support. Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods. The proposed evaluation methodology includes features o