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From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.

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Τίτλος: From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.
Συγγραφείς: Zhang T; College of Business and Economics, Sejong University, Gwangjin-gu, Seoul, Republic of Korea.; Institute of Neurology, Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Brain Function and Disorders, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, 200040, China., Shin N; College of Business and Economics, Sejong University, Gwangjin-gu, Seoul, Republic of Korea. ninashin@sejong.ac.kr.
Πηγή: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2026 May 22; Vol. 26 (1). Date of Electronic Publication: 2026 May 22.
Τύπος έκδοσης: Journal Article; Systematic Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE
Imprint Name(s): Original Publication: London : BioMed Central, [2001-
Ιατρικοί όροι (MeSH): Electronic Health Records*/standards , Artificial Intelligence* , Decision Support Systems, Clinical* , Medical Informatics* , User-Computer Interface*, Humans ; Digital Health
Περίληψη: Background: Electronic health records (EHRs) are central to clinical documentation and care coordination, yet long-standing studies report persistent socio-technical problems such as poor usability, slow response, alert burden, interoperability gaps, and clinician cognitive burden. As EHRs increasingly incorporate decision support and other AI-related functionalities, the evidence base now spans both traditional EHR design/use and EHR-linked advanced functions. However, findings are often reported in ways that make it difficult to trace how interface and information conditions relate to clinicians' interaction experiences and, where reported, downstream outcomes.
Methods: We conducted an integrative literature review of medical informatics research published between 2005 and 2025 on EHR design and use, including EHR-linked decision support and AI-related functionalities when explicitly described. Seventy eligible studies were synthesized using a four-layer socio-technical architecture, Interface, Information, Interaction, and Outcome (4I). For readability, "4I" can be read equivalently as "3I + O" (3I plus an explicit Outcome layer). We developed study-level labels and cross-layer evidence links to derive an evidence-traceable set of 29 variables and to summarize how studies characterized each variable as facilitating, constraining, or mixed for adoption and ongoing use.
Results: The 29 variables clustered across the 4I layers, with 5 primarily interface-related variables, 5 information-related variables, 10 interaction-related variables, and 9 outcome-related variables. Interface and Information conditions (e.g., access/usability, interoperability, trust, governance, and task-function fit) were most often connected to Interaction experiences, including workflow fit, training and support, trust calibration, and cognitive workload. Across the included studies, adoption was described as more favorable when reliable system performance, interpretable outputs, workflow-aligned training, and organizational support co-occurred. Where studies reported downstream effects, these cross-layer patterns were linked to documentation burden and burnout, patient-safety risks, workflow standardization, and perceived augmentation of clinical decision-making.
Conclusions: The 4I framework provides a structured synthesis that connects established EHR evidence to EHR-linked decision support and AI-related functionalities where documented, while keeping outcome implications explicit. The resulting 29-variable dictionary supports clearer reporting and cross-layer interpretation, and it provides a practical basis for subsequent expert elicitation and empirical assessment as evidence on AI-linked EHR functions continues to develop.
(© 2026. The Author(s).)
Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. This study is an integrative review of published literature and did not involve human participants, human data, or human tissue. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
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Contributed Indexing: Keywords: 4I framework; Artificial intelligence (AI); Clinical decision support systems (CDSS); Clinician workload; EHRs; Governance; Socio-technical framework; Trust; Usability; Workflow
Entry Date(s): Date Created: 20260523 Date Completed: 20260715 Latest Revision: 20260728
Update Code: 20260728
PubMed Central ID: PMC13371159
DOI: 10.1186/s12911-026-03562-6
PMID: 42174575
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1472-6947
DOI:10.1186/s12911-026-03562-6