Predicting Electronic Health Record Usability: Scoping Review of Adoption Models, Metrics, and Future Directions

Clinical Snapshot

Key Findings

• P Value: Not applicable for scoping review

• Effect Size: No predictive modeling or AI applications found for EHR usability prediction

• Primary Outcome: 47 studies identified from 2323 screened papers examining EHR adoption and usability prediction

• Nnt Or Sensitivity: Research gap identified: 100% of studies focused on adoption rather than operational usability prediction

• Confidence Interval: Not applicable for scoping review

Clinical Application

High feasibility for informing EHR implementation strategies and research priorities in healthcare organizations Highly relevant to Australian healthcare given similar EHR implementation challenges, TGA medical device regulations, and RACGP digital health initiatives Healthcare organizations implementing or optimizing EHR systems, health informatics researchers, and clinical decision-makers

Abstract

BACKGROUND: Electronic health records (EHRs) play an essential role in modern health care, enabling data sharing and improving patient safety; however, even though vendors must adhere to International Organization for Standardization-related usability standards for EHR certification, persistent usability issues continue to undermine efficiency, contribute to clinician burden, and increase the risk of preventable errors. OBJECTIVE: This scoping review synthesizes existing research on EHR adoption and usability, emphasizing theoretical models, measurement approaches, factors, and analytic methods used to assess or predict usability. We identify gaps and opportunities for integrating predictive analytics and artificial intelligence (AI) to advance research and improve the usability of EHRs. METHODS: Following Joanna Briggs Institute and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched MEDLINE, Web of Science, IEEE Xplore, and Scopus library databases for studies published between January 1, 2009, and April 9, 2025. Inclusion criteria focused on empirical research using predictive methods or models related to EHR usability. Data were charted and synthesized thematically. RESULTS: Of the 2323 screened papers, 47 studies met inclusion criteria. Most research examined or predicted EHR adoption (not usability) using dominant frameworks, such as the technology acceptance model, unified theory of acceptance and use of technology, and the information system success model, which comprised usability. Factors related to usability-particularly perceived usefulness, perceived ease of use, effort expectancy, and facilitating conditions via the EHR adoption models-appeared frequently. Regression-based methods and structural equation modeling were the most common analytic techniques. No studies applied predictive modeling or AI to predict EHR usability. CONCLUSIONS: The focus of this study on the prediction of EHR usability and adoption for the past 15 years is distinctive in the literature. It extends prior usability reviews (mostly focusing on adoption, not prediction of usability). Predictive modeling for EHR usability remains underdeveloped throughout 2009 to 2025. Dominant frameworks in the EHR literature continue to prioritize predicting adoption over operational usability. These models rely heavily on self-reported, cross-sectional measures captured at a single postimplementation time point, embedding systematic bias and obscuring longitudinal usability dynamics. Despite the application of increasingly sophisticated predictive techniques-primarily variants of regression and structural equation modeling-usability has remained analytically subordinate to adoption and acceptance constructs for more than 15 years. As a result, widely used models, such as the technology acceptance model and unified theory of acceptance and use of technology, position usability merely as an antecedent to intention or use, rather than as an independent, system-level property that can be empirically measured, modeled, and predicted. Therefore, there is substantial opportunity to integrate predictive analytics, AI, and longitudinal usability measures to build dynamic models.

References

1. 1.Chrimes, D., Thomo, A., Kuo, M. H. A., Borycki, E. M., & Kushniruk, A. (2026). Predicting Electronic Health Record Usability: Scoping Review of Adoption Models, Metrics, and Future Directions. JMIR Human Factors. https://doi.org/10.2196/86076