Healthcare practitioner involvement in data-driven clinical decision support development and evaluation: Critical narrative review of recommendations
Abstract
• A range of guidance is available describing when and where healthcare practitioners (HCPs) should be involved in data-driven clinical decision support (CDSS) development. • Although current recommendations mention acceptability, communication, and human/AI interaction, there are gaps in key phases of development. • Frameworks routinely lack detail on complex challenges like risk communication. • Clearer recommendations for HCP involvement as end users and expert advisors could enhance trustworthiness of data-driven CDSS. While healthcare practitioner (HCP) involvement is widely acknowledged as essential for the development and evaluation of trustworthy data-driven clinical decision support systems (CDSS), practical guidance remains limited. This critical narrative review examines existing frameworks, highlights HCP-related considerations, and identifies areas where further guidance is warranted. We combined searches of Ovid Medline, the EQUATOR Network Library, and relevant reviews to September 2024, seeking frameworks for developing or evaluating data-driven CDSS. Framework characteristics, coverage across the data-driven CDSS lifecycle, and details of HCP-related recommendations were extracted for analysis. 165 publications were screened, and 32 met inclusion criteria. Nine frameworks made no recommendations relating to HCP involvement. In the other 23, HCP-related recommendations were found for most phases of the data-driven CDSS development and evaluation lifecycle. Recommendations relating to HCP end users included themes of acceptability, communication, and human-AI interaction. Expert clinical input was suggested for various phases, but not required by any reporting guidelines. Existing guidance lacks comprehensive methods for including HCPs throughout data-driven CDSS development and evaluation. Reporting guidelines do not position HCPs as experts, which may lead to clinical expertise being overlooked. Frameworks lack detail on complex challenges such as risk communication. No frameworks suggested HCP involvement in data preparation or post-market surveillance, yet HCPs could usefully contribute to these phases. HCPs should be included in data-driven CDSS development and evaluation, but there is scope to better understand how to incorporate more clinical insight, and how this might improve trustworthiness of these tools.
Fields of Study & Subjects
Artificial Intelligence in Healthcare and Education
Electronic Health Records Systems
Machine Learning in Healthcare
Publication details
- Journal / source
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Volume
- 212
- Pages
- 106360–106360
- Publication date
- 18 February 2026
- Language
- EN