Transformative Role of Generative Artificial Intelligence in Inpatient Medicine: Real-World Implementations and Future Directions
This presentation explores the transformative potential of Generative Artificial Intelligence (GenAI) in inpatient medicine, focusing on real-world implementations at NYU Langone Health. Attendees will learn about successful applications, including automatic hospital course generation, patient-friendly discharge narratives, secure chat classification and using GPT to improve medication safety. The session will also address responsible use, safety measures, and end-user education, aiming to enhance patient care and clinician efficiency.
Learning Objectives
- Understand how specific applications of GenAI can be used to improve patient care, provider efficiency, and hospital operations
Speaker
- Paawan Punjabi, MD, MSc (New York University School of Medicine/NYU Langone Health System)
AI-Driven Automation of Procedural Case Log Documentation
Physician case logs are crucial for certification, credentialing, and monitoring medical training, yet their documentation in radiology remains a labor-intensive, manual process. Residents often record over 6,000 procedures manually during their training, which not only diverts time from clinical activities but also increases the likelihood of errors. This inefficiency hampers both the residents and their training programs, highlighting the need for automated solutions. To address this, we employed large language models (LLMs) to automate case log documentation by processing procedural reports and answering predefined questions across three sections: Vascular Diagnosis, Vascular Intervention, and Nonvascular Intervention. Using Meditron-70B and MedLLaMA2-7B, we evaluated their performance against gold-standard annotations from a trained physician. Meditron-70B demonstrated superior accuracy, achieving an F1-score of 72.21% compared to MedLLaMA2's 30.90%. It excelled in precision (>88%) and recall (>59%), particularly in vascular tasks, underscoring its potential for automating this critical process. Future work will focus on fine-tuning LLMs using ground truth data to enhance alignment with case log requirements. Additionally, we plan to expand the dataset beyond the initial 60 reports to thousands of cases, enabling more comprehensive evaluations and improvements. By integrating more robust architectures and scaling the analysis, we aim to create a reliable solution that reduces documentation errors, saves time, and improves the overall efficiency of medical training workflows. This approach paves the way for transforming case log documentation into an efficient and error-free process.
Learning Objectives
- Identify the limitations of manual procedural case logging in radiology training and the need for automated, scalable solutions.
Speaker
- Nafiz Imtiaz Khan, Student (University of California - Davis)
Implementing AI-Driven Patient Summarization in Electronic Health Records: Early Insights, Best Practices, and Impact Evaluation
The integration of generative artificial intelligence (AI) in healthcare promises to alleviate documentation burdens and enhance patient care. At the Children’s Hospital of Philadelphia (CHOP), we integrated an "AI Note Summarization" tool into our electronic health record to automate the extraction of patient histories from existing notes. This pilot, involving 22 clinical staff in an outpatient setting, leveraged ChatGPT-4o within a HIPAA-compliant framework to generate concise narrative summaries. Since June of 2024, our pilot group has been assisting our AI-working group in assessing the tool's effectiveness, safety, and overall experience. The tool has been live in production environments since August of 2024, with ongoing pilot user testing and feedback. Our pilot results have been compelling. Over 2,370 summaries were generated from more than 32,426 notes with a minimal cancellation rate of 0.5%. User feedback indicates a high 70% satisfaction rate, with 90% of users affirming enhanced clinical workflows and 50% reporting increased insights into their patient's histories. Continuous bi-monthly reviews by the CHOP AI team help refine the tool, focusing on the tool's reliability and utility in clinical practice.
Learning Objectives
- Evaluate the process of implementing and assessing AI tools in clinical settings, focusing on governance, pilot testing, and feedback collection.
Speaker
- Osvaldo Mercado, MD (Children's Hospital of Philadelphia)
About CME/CNE Credit
The following information pertains to individual sessions included in the AMIA 2025 Clinical Informatics Conference On Demand product. A total of 16.75 CME/CNE credits may be earned if all sessions are completed.
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 16.75 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than within two years of the release date or within one year of your purchase date, whichever is sooner.
ANNC Accreditation Statement
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
- Nurse Planner (Content): Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN
- Approved Contact Hours: 16.75 participant maximum CME/CNE
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2025 CIC sessions attended for ACHIPsTM Recertification.
Claim credit no later than within two years of the release date or within one year of your purchase date, whichever is sooner.