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Poor performance of ChatGPT in clinical rule-guided dose interventions in hospitalized patients with renal dysfunction

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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.

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References

  1. 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

    Article  PubMed  PubMed Central  Google Scholar 

  2. OpenIA ChatGPT. https://openai.com/blog/chatgpt. Accessed 26 Jan 2024

  3. 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

    Article  Google Scholar 

  4. 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

    Article  PubMed  Google Scholar 

  5. 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

    Article  PubMed  Google Scholar 

  6. Huang X, Estau D, Liu X et al (2023) Evaluating the performance of ChatGPT in clinical pharmacy: a comparative study of ChatGPT and clinical pharmacists. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.15896

    Article  PubMed  Google Scholar 

  7. Fournier A, Fallet C, Sadeghipour F, Perrottet N (2023) Assessing the applicability and appropriateness of ChatGPT in answering clinical pharmacy questions. Ann Pharm Fr. https://doi.org/10.1016/j.pharma.2023.11.001

    Article  PubMed  Google Scholar 

  8. Lee P, Bubeck S, Petro J (2023) Benefits, limits, and risks of GPT-4 as an AI Chatbot for medicine. N Engl J Med 388:1233–1239. https://doi.org/10.1056/NEJMsr2214184

    Article  PubMed  Google Scholar 

  9. Wasylewicz ATM, Scheepers-Hoeks AMJW (2019) Clinical decision support systems. In: Fundamentals of Clinical Data Science, 1st edn. Springer, Cham, pp 153–170

  10. Drenth-van Maanen AC, van Marum RJ, Jansen PAF et al (2015) Adherence with dosing guideline in patients with impaired renal function at hospital discharge. PLoS One 10:e0128237. https://doi.org/10.1371/journal.pone.0128237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. McCann A (2023) Microsoft and Epic expand strategic collaboration with integration of Azure OpenAI Service. Microsoft News Center. https://news.microsoft.com/2023/04/17/microsoft-and-epic-expand-strategic-collaboration-with-integration-of-azure-openai-service/. Accessed 26 Jan 2024

  12. Levey AS, Stevens LA, Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604–612. https://doi.org/10.7326/0003-4819-150-9-200905050-00006

    Article  PubMed  PubMed Central  Google Scholar 

  13. Datt M, Sharma H, Aggarwal N, Sharma S (2023) Role of ChatGPT-4 for medical researchers. Ann Biomed Eng. https://doi.org/10.1007/s10439-023-03336-5

    Article  PubMed  Google Scholar 

  14. Briganti G (2024) How ChatGPT works: a mini review. Eur Arch Otorhinolaryngol 281:1565–1569. https://doi.org/10.1007/s00405-023-08337-7

    Article  PubMed  Google Scholar 

  15. Dave T, Athaluri SA, Singh S (2023) ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell 6:1169595. https://doi.org/10.3389/frai.2023.1169595

    Article  PubMed  PubMed Central  Google Scholar 

  16. UK Renal Pharmacy Group (UKRPG) The renal drug database. https://renaldrugdatabase.com/. Accessed 26 Jan 2024

  17. De Koninklijke Nederlandse Maatschappij ter bevordering der Pharmacie (KNMP) KNMP Kennisbank. www.kennisbank.knmp.nl. Accessed 26 Jan 2024

  18. European Medicines Agency (EMA) (2004) Note for guidance on the evaluation of the pharmacokinetics of medicinal products in patients with impaired renal function. https://www.ema.europa.eu/en/documents/scientific-guideline/note-guidance-evaluation-pharmacokinetics-medical-products-patients-impaired-renal-function_en.pdf . Accessed 26 Jan 2024

  19. Food and Drug Administration (2019) Pharmaceutical science and clinical pharmacology advisory committee meeting. https://www.fda.gov/advisory-committees/advisory-committee-calendar/may-7-2019-meeting-pharmaceutical-science-and-clinical-pharmacology-advisory-committee-meeting. Accessed 26 Jan 2024

  20. Food and Drug Administration (2020) Pharmacokinetics in patients with impaired renal function - study design, data analysis, and impact on dosing and labeling. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pharmacokinetics-patients-impaired-renal-function-study-design-data-analysis-and-impact-dosing. Accessed 26 Jan 2024

  21. Ayers JW, Poliak A, Dredze M et al (2023) Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med. https://doi.org/10.1001/jamainternmed.2023.1838

    Article  PubMed  Google Scholar 

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Funding

No datasets were generated or analysed during the current study.

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by MvN, JS, AE, and RW. Interpretation of the data was performed by MvN, AE, TE, and PvdL. The first draft of the manuscript was written by MvN and AE and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Merel van Nuland.

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van Nuland, M., Snoep, J.D., Egberts, T. et al. Poor performance of ChatGPT in clinical rule-guided dose interventions in hospitalized patients with renal dysfunction. Eur J Clin Pharmacol (2024). https://doi.org/10.1007/s00228-024-03687-5

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