This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (481) Clinical Information and Decision Making (1407) Knowledge Translation and Implementation Science (398) Machine Learning (1465) Clinical Informatics (1022) Digital Health, Telehealth and e-Innovation in Clinical Settings (401) Artificial Intelligence (1371) Machine Learning (49)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50475, first published .
Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach

Authors of this article:

Michael Shulha1, 2 Author Orcid Image ;   Jordan Hovdebo3 Author Orcid Image ;   Vinita D’Souza1, 2 Author Orcid Image ;   Francis Thibault4 Author Orcid Image ;   Rola Harmouche4 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Fernando M, Abell B, McPhail S, Tyack Z, Tariq A, Naicker S. Applying the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability Framework Across Implementation Stages to Identify Key Strategies to Facilitate Clinical Decision Support System Integration Within a Large Metropolitan Health Service: Interview and Focus Group Study. JMIR Medical Informatics 2024;12:e60402 View