• Early clinical implementation...

*Result*: Early clinical implementation and evaluation of an NLP-Based AI system for thrombophilia assessment using electronic health records.

Title:
Early clinical implementation and evaluation of an NLP-Based AI system for thrombophilia assessment using electronic health records.
Authors:
Alnor AB; Department of Clinical Biochemistry, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark. Electronic address: anne.alnor@rsyd.dk., Højer Adelhelm JB; Department of Clinical Biochemistry, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense C, Denmark., Pedersen LE; Department of Clinical Biochemistry, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense C, Denmark., Faurø KK; Department of Clinical Biochemistry, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense C, Denmark., Laursen MS; Department of Clinical Biochemistry, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense C, Denmark., Lynggaard RB; Department of Clinical Biochemistry, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense C, Denmark., Vinholt PJ; Department of Clinical Biochemistry, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense C, Denmark; Department of Clinical Research, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark.
Source:
International journal of medical informatics [Int J Med Inform] 2026 Feb; Vol. 206, pp. 106135. Date of Electronic Publication: 2025 Oct 10.
Publication Type:
Journal Article
Language:
English
Journal Info:
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-
MeSH Terms:
Thrombophilia*/diagnosis , Electronic Health Records* , Natural Language Processing* , Artificial Intelligence*, Humans ; Retrospective Studies ; Denmark
Contributed Indexing:
Keywords: Electronic Health Records; Eye-Tracking Technology; Natural Language Processing; Thrombophilia
Entry Date(s):
Date Created: 20251014 Date Completed: 20251124 Latest Revision: 20251124
Update Code:
20260130
DOI:
10.1016/j.ijmedinf.2025.106135
PMID:
41086641
Database:
MEDLINE

*Further Information*

*Background: Thrombophilia evaluation requires integration of biochemical findings with clinical history, much of which is embedded in unstructured electronic health record (EHR) text. Manual chart review is labour-intensive and prone to omissions. Natural language processing (NLP) offers a potential alternative by automatically identifying and highlighting relevant information to support more efficient and accurate assessments.
Methods: We developed a transformer-based NLP model for thrombosis and integrated it with previously validated bleeding (transformer-based), anticoagulant, and antithrombotics (rule-based) models in an AI system that highlights key phrases in unstructured Danish EHRs to expedite clinician chart review. The system was implemented in routine clinical care to support thrombophilia evaluations. We retrospectively evaluated its performance based on 50 real-world EHRs reviewed by clinicians using the system. Eye-tracking was used to assess information-seeking behaviour during simulated reviews, and semi-structured interviews explored adoption potential post-implementation.
Results: The thrombosis model achieved high sentence-level performance (sensitivity 98.8%, specificity 99.8%). In retrospective review, AI-assisted clinicians identified all previously documented findings, and identified additional relevant information in 68% of cases. Eye-tracking showed a 33% reduction in review time and improved identification of relevant content. Users adopted two distinct navigation styles: AI-guided scanning and manual scrolling, reflecting varied trust and cognitive strategies. Interviews revealed strong support for system accuracy and efficiency, with integration and training identified as key challenges.
Conclusion: This evaluation highlights the potential of NLP tools to support clinical decision-making. Clear model design, sentence-level transparency, and user-centred evaluation are essential for safe and effective integration into clinical workflows.
(Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.)*

*Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*