Poor performance of ChatGPT in clinical rule-guided dose interventions in hospitalized patients with renal dysfunction
- PMID: 38592470
- DOI: 10.1007/s00228-024-03687-5
Poor performance of ChatGPT in clinical rule-guided dose interventions in hospitalized patients with renal dysfunction
Abstract
Purpose: Clinical decision support systems (CDSS) are used to identify drugs with potential need for dose modification in patients with renal impairment. ChatGPT holds the potential to be integrated in the electronic health record (EHR) system to give such dosing advices. In this study, we aim to evaluate the performance of ChatGPT in clinical rule-guided dose interventions in hospitalized patients with renal impairment.
Methods: This cross-sectional study was performed at Tergooi Medical Center, the Netherlands. CDSS alerts regarding renal dysfunction were collected from the electronic health record (EHR) during a 2-week period and were presented to ChatGPT and an expert panel. Alerts were presented with and without patient variables. To evaluate the performance, suggested medication interventions were compared.
Results: In total, 172 CDDS alerts were generated for 80 patients. Indecisive responses by ChatGPT to alerts were excluded. For alerts presented without patient variables, ChatGPT provided "correct and identical" responses to 19.9%, "correct and different" responses to 26.7%, and "incorrect responses to 53.4% of the alerts. For alerts including patient variables, ChatGPT provided "correct and identical" responses to 16.7%, "correct and different" responses to 16.0%, and "incorrect responses to 67.3% of the alerts. Accuracy was better for newer drugs such as direct oral anticoagulants.
Conclusion: The performance of ChatGPT in clinical rule-guided dose interventions in hospitalized patients with renal dysfunction was poor. Based on these results, we conclude that ChatGPT, in its current state, is not appropriate for automatic integration into our EHR to handle CDSS alerts related to renal dysfunction.
Keywords: CDSS; ChatGPT; Clinical rule-guided dose interventions; Language model; Renal dysfunction.
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Similar articles
-
Clinical decision support system supported interventions in hospitalized older patients: a matter of natural course and adequate timing.BMC Geriatr. 2024 Mar 14;24(1):256. doi: 10.1186/s12877-024-04823-7. BMC Geriatr. 2024. PMID: 38486200 Free PMC article.
-
Clinical rule-guided pharmacists' intervention in hospitalized patients with hypokalaemia: A time series analysis.J Clin Pharm Ther. 2020 Jun;45(3):520-529. doi: 10.1111/jcpt.13101. Epub 2019 Dec 24. J Clin Pharm Ther. 2020. PMID: 31873951
-
Renal medication-related clinical decision support (CDS) alerts and overrides in the inpatient setting following implementation of a commercial electronic health record: implications for designing more effective alerts.J Am Med Inform Assoc. 2021 Jun 12;28(6):1081-1087. doi: 10.1093/jamia/ocaa222. J Am Med Inform Assoc. 2021. PMID: 33517413 Free PMC article.
-
Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workflow.J Biomed Inform. 2020 Jun;106:103453. doi: 10.1016/j.jbi.2020.103453. Epub 2020 May 14. J Biomed Inform. 2020. PMID: 32417444 Review.
-
Electronic Health Record Tools to Identify Child Maltreatment: Scoping Literature Review and Key Informant Interviews.Acad Pediatr. 2022 Jul;22(5):718-728. doi: 10.1016/j.acap.2022.01.017. Epub 2022 Feb 4. Acad Pediatr. 2022. PMID: 35131505 Free PMC article.
Cited by
-
ChatGPT does make up scientific references: should it be currently renamed CheatGPT?Eur J Clin Pharmacol. 2024 Dec;80(12):1995-1996. doi: 10.1007/s00228-024-03758-7. Epub 2024 Sep 10. Eur J Clin Pharmacol. 2024. PMID: 39251431 Free PMC article. No abstract available.
-
Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support.Front Med (Lausanne). 2025 Jan 23;12:1527864. doi: 10.3389/fmed.2025.1527864. eCollection 2025. Front Med (Lausanne). 2025. PMID: 39917061 Free PMC article.
-
Testing and Evaluation of Generative Large Language Models in Electronic Health Record Applications: A Systematic Review.medRxiv [Preprint]. 2025 Jun 22:2024.08.11.24311828. doi: 10.1101/2024.08.11.24311828. medRxiv. 2025. PMID: 39228726 Free PMC article. Preprint.
-
Performance of 3 Conversational Generative Artificial Intelligence Models for Computing Maximum Safe Doses of Local Anesthetics: Comparative Analysis.JMIR AI. 2025 May 13;4:e66796. doi: 10.2196/66796. JMIR AI. 2025. PMID: 40605845 Free PMC article.
References
-
- Plana D, Shung DL, Grimshaw AA et al (2022) Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw Open 5:e2233946. https://doi.org/10.1001/jamanetworkopen.2022.33946 - DOI - PubMed - PMC
-
- OpenIA ChatGPT. https://openai.com/blog/chatgpt . Accessed 26 Jan 2024
-
- Roosan D, Padua P, Khan R et al (2003) Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management. J Am Pharm Assoc. https://doi.org/10.1016/j.japh.2023.11.023 - DOI
-
- Al-Dujaili Z, Omari S, Pillai J, Al Faraj A (2023) Assessing the accuracy and consistency of ChatGPT in clinical pharmacy management: a preliminary analysis with clinical pharmacy experts worldwide. Res Social Adm Pharm 19:1590–1594. https://doi.org/10.1016/j.sapharm.2023.08.012 - DOI - PubMed
-
- Morath B, Chiriac U, Jaszkowski E et al (2023) Performance and risks of ChatGPT used in drug information: an exploratory real-world analysis. Eur J Hosp Pharm. https://doi.org/10.1136/ejhpharm-2023-003750 - DOI - PubMed
MeSH terms
LinkOut - more resources
Full Text Sources