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Citations

References

Citations and source material referenced across CarePathIQ pathways, modules, and blog posts.


References

1. Adams, et al. (2024). Evaluating the Quality of Discharge Letters Generated by GPT-4… Journal of Medical Internet Research. Retrieved from https://www.jmir.org/2024/1/e57721/

2. de Ruiter, et al. (2024). AI-generated patient discharge information in the emergency department: a pilot study. PMC. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC12023409/

3. Hartman, et al. (2024). Developing and Evaluating Large Language Model–Generated Emergency Medicine Handoff Notes. JAMA Network Open. Retrieved from https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2827327

4. Kim, et al. (2023). Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers. ACM Digital Library. Retrieved from https://dl.acm.org/doi/10.1145/3589955

5. Kojima, et al. (2022). A Study on the Zero-shot Information Extraction Capability of a Large Language Model. arXiv. Retrieved from https://arxiv.org/abs/2205.12689

6. Ray, et al. (2025). Evaluating a Large Language Model in Translating Patient Instructions to Spanish Using a Standardized Framework. JAMA Pediatrics. (Link not available for future publication)

7. Reis, et al. (2024). LLM Supported Instructions for Medication Use. Mayo Clinic Proceedings: Digital Health. Retrieved from https://doi.org/10.1016/j.mcpdig.2024.09.006

8. Verberne, et al. (2024). Clinical natural language processing applications along the patient journey: a systematic review. BMC Medical Informatics and Decision Making. Retrieved from https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02641-w

9. Williams, et al. (2025). Evaluating large language models for drafting emergency department encounter summaries. PLOS Digital Health. Retrieved from https://doi.org/10.1371/journal.pdig.0000899

10. Zhang, et al. (2023). EHRTutor: A Large Language Model-based, EHR-integrated Conversational Question Answering System… arXiv. Retrieved from https://arxiv.org/abs/2310.19212

11. Maughan, B. C., et al. (2019). Improving perceptions of patient safety through standardizing handoffs from the emergency department to the inpatient setting: a systematic review. Journal of the American College of Emergency Physicians Open. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493549/

12. Wang, H. E., et al. (2020). Evaluation of Process Improvement Interventions on Handoff Times between the Emergency Department and Observation Unit. The American Journal of Emergency Medicine. Retrieved from https://pubmed.ncbi.nlm.nih.gov/33223270/

13. Kreimeyer, K., et al. (2024). Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial. BMC Medical Education. Retrieved from https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-024-06399-7

14. Li, D., et al. (2025). From Questions to Clinical Recommendations: Large Language Models Driving Evidence-Based Clinical Decision Making. arXiv. Retrieved from https://arxiv.org/abs/2505.10282

15. Liu, X., et al. (2025). Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations. Journal of Medical Internet Research. Retrieved from https://pubmed.ncbi.nlm.nih.gov/40384063/

16. Sallam, M. (2024). ChatGPT for medical note-taking in the intensive care unit: a case study. Artificial Intelligence Review. Retrieved from https://link.springer.com/article/10.1007/s10462-024-10921-0

17. Oniani, D., et al. (2024). Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines. arXiv. Retrieved from https://arxiv.org/abs/2401.11120