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

Autor: Van Woensel W; Telfer School of Management, University of Ottawa, Ottawa, ON, Canada. Electronic address: wvanwoen@uottawa.ca., Tu SW; Center for BioMedical Informatics Research, Stanford University, Stanford, CA 94305, USA., Michalowski W; Telfer School of Management, University of Ottawa, Ottawa, ON, Canada., Sibte Raza Abidi S; Faculty of Computer Science, Dalhousie University, Halifax, Canada., Abidi S; Faculty of Computer Science, Dalhousie University, Halifax, Canada., Alonso JR; Hospital Clinic Barcelona, Barcelona, Spain., Bottrighi A; DISIT, Università del Piemonte Orientale, Alessandria, Italy., Carrier M; The Ottawa Hospital, Ottawa, ON, Canada., Edry R; Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel; Rambam Medical Center, Haifa, Israel., Hochberg I; Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel; Rambam Medical Center, Haifa, Israel., Rao M; Telfer School of Management, University of Ottawa, Ottawa, ON, Canada., Kingwell S; The Ottawa Hospital, Ottawa, ON, Canada., Kogan A; Department of Information Systems, University of Haifa, Haifa 3498838, Israel., Marcos M; Universitat Jaume I, Castelló de la Plana, Spain., Martínez Salvador B; Universitat Jaume I, Castelló de la Plana, Spain., Michalowski M; School of Nursing, University of Minnesota, Minneapolis, MN, USA., Piovesan L; DISIT, Università del Piemonte Orientale, Alessandria, Italy., Riaño D; Universitat Rovira i Virgili, Tarragona, Spain; Institut d'Investigació Sanitària Pere Virgili, Tarragona, Spain., Terenziani P; DISIT, Università del Piemonte Orientale, Alessandria, Italy., Wilk S; Institute of Computing Science, Poznan University of Technology, Poznan, Poland., Peleg M; Department of Information Systems, University of Haifa, Haifa 3498838, Israel.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2023 Jun; Vol. 142, pp. 104395. Date of Electronic Publication: 2023 May 16.
DOI: 10.1016/j.jbi.2023.104395
Abstrakt: Objective: 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.
Materials and Methods: 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.
Results: 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.
Discussion: The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS.
Conclusion: We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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