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Cited by 29Venous Thromboembolism Diagnosis and ManagementElectronic Health Records SystemsArtificial Intelligence in Healthcare and Education

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Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study

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Huang, Xiaoyan, Zhou, Shuai, Ma, Xudong, Jiang, Songyi, Xu, Yuanyuan, You, Yi, Qu, Jieming, Shang, Hanbing, Lu, Yong·2023·BMJ Open Quality
DOI 10.1136/bmjoq-2023-002267PMC PMC10582876

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Abstract

Background: Thromboprophylaxis has been determined to be safe, effective and cost-effective for hospitalised patients at venous thromboembolism (VTE) risk. However, Chinese medical institutions have not yet fully used or improperly used thromboprophylaxis. The effectiveness of information technology applied to thromboprophylaxis in hospitalised patients has been proved in many retrospective studies, lacking of prospective research evidence. Methods: All hospitalised patients aged >18 years not discharged within 24 hours from 1 September 2020 to 31 May 2021 were prospectively enrolled. Patients were randomly assigned to the control (9890 patients) or intervention group (9895 patients). The control group implemented conventional VTE prevention programmes; the intervention group implemented an Artificial Intelligence Clinical Assistant Decision Support System (AI-CDSS) on the basis of conventional prevention. Intergroup demographics, disease status, hospital length of stay (LOS), VTE risk assessment and VTE prophylaxis were compared using the χ 2 test, Fisher’s exact test, t-test or Wilcoxon rank-sum test. Univariate and multivariate logistic regressions were used to explore the risk factor of VTE. Results: The control and intervention groups had similar baseline characteristics. The mean age was 58.32±15.41 years, and mean LOS was 7.82±7.07 days. In total, 5027 (25.40%) and 2707 (13.67%) patients were assessed as having intermediate-to-high VTE risk and high bleeding risk, respectively. The incidence of hospital-associated VTE (HA-VTE) was 0.38%, of which 86.84% had deep vein thrombosis. Compared with the control group, the incidence of HA-VTE decreased by 46.00%, mechanical prophylaxis rate increased by 24.00% and intensity of drug use increased by 9.72% in the intervention group. However, AI-CDSS use did not increase the number of clinical diagnostic tests, prophylaxis rate or appropriate prophylaxis rate. Conclusions: Thromboprophylaxis is inadequate in hospitalised patients with VTE risk. The role of AI-CDSS in VTE risk management is unknown and needs further in-depth study. Trial registration number: ChiCTR2000035452.

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Figure 1 Flow of patient inclusion and exclusion criteria. Control group, conventional prevention; Intervention group, AI-CDSS on the basis of conventional prevention. AI-CDSS, Artificial Intelligence Clinical Assistant Decision Support System; VTE, venous thromboembolism.
Figure 1 Flow of patient inclusion and exclusion criteria. Control group, conventional prevention; Intervention group, AI-CDSS on the basis of conventional prevention. AI-CDSS, Artificial Intelligence Clinical Assistant Decision Support System; VTE, venous thromboembolism.CC BY-NC · PubMed Central

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Metadata

Authors
Huang, X., Zhou, S., Ma, X., Jiang, S., Xu, Y., You, Y., Qu, J., Shang, H., & Lu, Y.
Affiliations
  • Shanghai Jiao Tong University (CN)
  • National Health Commission of the People's Republic of China (CN)
  • Solution Center For Quality Improvement, Beijing Huimei Cloud Technology Co. Ltd, Beijing, China
  • Shanghai First People's Hospital (CN)
  • Shanghai Venous Thromboembolism Alliance, Shanghai, China
Corresponding author
Shang, Hanbing
ORCID
Venue
BMJ Open Quality
Publisher
BMJ
Published
2023-10
Volume
12
Issue
4
Pages
e002267
ISSN
2399-6641 (ISSN-L)
DOI
10.1136/bmjoq-2023-002267
PMC
PMC10582876
OpenAlex
W4387618747
Cited by
29
References
0
Citations by year
2026: 14 · 2025: 7 · 2024: 8
FWCI
7.01
Type
journal article
Language
en
Open access
Licence
Locations
  • BMJ Open Quality (publishedVersion, cc-by-nc)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • Suzhou Cooperative Medical and Health Foundation Research Fund for Prevention and Treatment of Venous Thromboembolism (P089)
  • the Science and Technology Innovation Project of Shanghai Jiao Tong University School of Medicine (WK2110)
  • 2022 Huangpu District Health Commission policy research research projects (HLY202201)
  • Hospital Management Research Project of Shanghai Hospital Association (X2022143 and Q2022063)
  • 2022 Health Policy Research Project of Shanghai Municipal Health Commission (2022HP64)
  • Shanghai 2023 "Science and Technology Innovation Action Plan" Science Popularization Special Project (23DZ2301400)
Topics
  • Health Sciences › Medicine › Internal Medicine › Venous Thromboembolism Diagnosis and Management
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence, clinical decision support systems, venous thromboembolism, thromboprophylaxis, hospital-acquired venous thromboembolism, deep vein thrombosis, hospitalized patients, mechanical prophylaxis, VTE risk assessment, bleeding risk, VTE risk factors, length of hospital stay, Chinese hospitals, information technology
MeSH terms
Adult, Aged, Anticoagulants, Artificial Intelligence, Hospitals, Humans, Middle Aged, Prospective Studies, Retrospective Studies, Incidence, Venous Thromboembolism

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CoChat. (2026). Citation check: Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study. https://app.cochat.ai/papers/10.1136/bmjoq-2023-002267
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Huang, X., Zhou, S., Ma, X., Jiang, S., Xu, Y., You, Y., Qu, J., Shang, H., & Lu, Y. (2023). Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study. BMJ Open Quality. https://doi.org/10.1136/bmjoq-2023-002267