Academic Journal
Artificial intelligence applications using patient-generated health data for pre-care processes in elective healthcare: a systematic review.
| Τίτλος: | Artificial intelligence applications using patient-generated health data for pre-care processes in elective healthcare: a systematic review. |
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| Συγγραφείς: | Warren T; IQ Health Science Department, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Research and Development Department, Interactive Studios, Parallelweg 27, 5223 AL 's-Hertogenbosch, the Netherlands. Electronic address: tristan.warren@radboudumc.nl., van der Weegen W; Department of Orthopedic Surgery, Sports & Orthopedics Research Centre, Anna Hospital, Bogardeind 2, 5664 BE Geldrop, the Netherlands., Kool RB; IQ Health Science Department, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, the Netherlands., Hoogendoorn M; Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, the Netherlands., Timmers T; IQ Health Science Department, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Research and Development Department, Interactive Studios, Parallelweg 27, 5223 AL 's-Hertogenbosch, the Netherlands. |
| Πηγή: | International journal of medical informatics [Int J Med Inform] 2026 Sep 15; Vol. 218, pp. 106525. Date of Electronic Publication: 2026 Jun 03. |
| Τύπος έκδοσης: | Journal Article; Systematic Review; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 9711057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8243 (Electronic) Linking ISSN: 13865056 NLM ISO Abbreviation: Int J Med Inform Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Shannon, Co. Clare, Ireland : Elsevier Science Ireland Ltd., c1997- |
| Ιατρικοί όροι (MeSH): | Artificial Intelligence* , Patient Generated Health Data* , Decision Support Systems, Clinical*, Humans ; Digital Health |
| Περίληψη: | Purpose: Artificial intelligence (AI) can leverage patient-generated health data (PGHD) to support pre-care processes such as triage, symptom assessment, and history-taking. Existing systematic reviews have examined AI clinical decision support, PGHD use, and AI for specific data modalities as separate domains, but none addresses their intersection for pre-care. We aimed to map AI methods and PGHD modalities, synthesize outcomes across technical, clinical, operational, user experience, and equity domains, and identify barriers to deployment and gaps in reporting. Methods: This systematic review was conducted in accordance with the PRISMA 2020 statement and prospectively registered with PROSPERO (CRD420251134235). We searched PubMed, MEDLINE, and Web of Science (January 2020-June 2025) for studies evaluating AI applications using PGHD to support pre-care processes in elective care. Risk of bias was assessed using validated tools appropriate to each study design. Narrative synthesis addressed heterogeneity across outcome domains. Results: Twenty-one studies analyzed PGHD from free text (38%), questionnaires (33%), voice recordings (14%), wearables (10%), and images (5%). Most used classical machine learning (67%), with deep learning present in 43% of studies and large language models emerging recently (14%). Model performance appeared promising, with area under the curve values ranging from 0.64 to 0.98 (median 0.78). However, this evidence has serious limitations: risk of bias was high in 95% of studies, external validation occurred in only 6% of evaluations, and clinical outcomes were measured in just one study. Equity was assessed in only 14% of studies. No study demonstrated patient benefit or described routine clinical deployment. Conclusion: Current evidence establishes proof-of-concept but not proof-of-benefit. The field requires a methodological shift from algorithm development toward prospective validation, clinical outcome measurement, and equity assessment before deployment can be justified. (Copyright © 2026 The Author(s). Published by Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: TW and TT work at the research and development department of Interactive Studios. Interactive Studios is a company that developed a platform to collect medical data from patients. The other coauthors declare that the research was conducted in the absence of any other commercial or financial relationships that could be construed as a potential conflict of interest. |
| Contributed Indexing: | Keywords: Artificial intelligence; Digital health; Patient-generated health data; Pre-care; Systematic review; Triage |
| Entry Date(s): | Date Created: 20260606 Date Completed: 20260627 Latest Revision: 20260627 |
| Update Code: | 20260628 |
| DOI: | 10.1016/j.ijmedinf.2026.106525 |
| PMID: | 42250455 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1872-8243 |
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| DOI: | 10.1016/j.ijmedinf.2026.106525 |