AscleAI: A LLM-based Clinical Note Management System for Enhancing Clinician Productivity
Authors: 
Jiyeon Han, Jimin Park, Jinyoung Huh, 
Uran Oh, Jaeyoung Do, Daehee KimAuthors Info & Claims
Jiyeon Han, Jimin Park, Jinyoung Huh, Uran Oh, Jaeyoung Do, Daehee KimAuthors Info & ClaimsArticle No.: 50, Pages 1 - 7
Abstract
While clinical notes are essential to the field of healthcare, they pose several challenges for clinicians since it is difficult to write down medical information, review prior notes, and extract the desired information at the same time while examining a patient. Thus, we designed a system that can automatically generate clinical notes from dialogues between patients and clinicians and provide specific information upon clinicians’ query using a Large Language Model (LLM) both in real-time. To explore how this system can be used to support clinicians in practice, we conducted an interview with six clinicians followed by a design probe study with the current version of our system for feedback. Findings suggest that our system has the potential to enable clinicians to write and access clinical notes and examine the patients simultaneously with reduced cognitive loads and increased efficiency and accuracy.
Supplemental Material
MP4 File
Talk Video
- Download
- 24.20 MB
- Transcript
References
[1]
Chiwon Ahn. 2023. Exploring ChatGPT for information of cardiopulmonary resuscitation. Resuscitation 185 (2023).
[2]
Stephen R Ali, Thomas D Dobbs, Hayley A Hutchings, and Iain S Whitaker. 2023. Using ChatGPT to write patient clinic letters. The Lancet Digital Health 5, 4 (2023), e179–e181.
[3]
Brian G Arndt, John W Beasley, Michelle D Watkinson, Jonathan L Temte, Wen-Jan Tuan, Christine A Sinsky, and Valerie J Gilchrist. 2017. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. The Annals of Family Medicine 15, 5 (2017), 419–426.
[4]
Omar Ayaad, Aladeen Alloubani, Eyad Abu ALhajaa, Mohammad Farhan, Sami Abuseif, Ahmad Al Hroub, and Laila Akhu-Zaheya. 2019. The role of electronic medical records in improving the quality of health care services: Comparative study. International journal of medical informatics 127 (2019), 63–67.
[5]
Kevin J Bennett and Christian Steen. 2010. Electronic medical record customization and the impact upon chart completion rates. Fam Med 42, 5 (2010), 338–42.
[6]
B Briggs and H Carter-Templeton. 2014. Electronic health record customization: a quality improvement project. On-Line Journal of Nursing Informatics 18, 3 (2014).
[7]
L Byrne, J Bottomley, and A Turk. 2016. British Medical association survey of GPs in England. London: British Medical Association (2016).
[8]
Gunther Eysenbach 2023. The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Medical Education 9, 1 (2023), e46885.
[9]
Carrie Lee Gardner and Patricia F Pearce. 2013. Customization of electronic medical record templates to improve end-user satisfaction. CIN: Computers, Informatics, Nursing 31, 3 (2013), 115–121.
[10]
John Giorgi, Augustin Toma, Ronald Xie, Sondra Chen, Kevin An, Grace Zheng, and Bo Wang. 2023. Wanglab at mediqa-chat 2023: Clinical note generation from doctor-patient conversations using large language models. In Proceedings of the 5th Clinical Natural Language Processing Workshop. 323–334.
[11]
Takanobu Hirosawa, Yukinori Harada, Masashi Yokose, Tetsu Sakamoto, Ren Kawamura, and Taro Shimizu. 2023. Diagnostic accuracy of differential-diagnosis lists generated by generative pretrained transformer 3 chatbot for clinical vignettes with common chief complaints: A pilot study. International journal of environmental research and public health 20, 4 (2023), 3378.
[12]
Gretchen M Hultman, Jenna L Marquard, Elizabeth Lindemann, Elliot Arsoniadis, Serguei Pakhomov, and Genevieve B Melton. 2019. Challenges and opportunities to improve the clinician experience reviewing electronic progress notes. Applied clinical informatics 10, 03 (2019), 446–453.
[13]
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, 2023. Mistral 7B. arXiv preprint arXiv:2310.06825 (2023).
[14]
Eunkyung Jo, Daniel A Epstein, Hyunhoon Jung, and Young-Ho Kim. 2023. Understanding the benefits and challenges of deploying conversational AI leveraging large language models for public health intervention. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–16.
[15]
Jeffrey G Klann and Peter Szolovits. 2009. An intelligent listening framework for capturing encounter notes from a doctor-patient dialog. BMC medical informatics and decision making 9 (2009), 1–10.
[16]
Linfeng Li, Peng Wang, Jun Yan, Yao Wang, Simin Li, Jinpeng Jiang, Zhe Sun, Buzhou Tang, Tsung-Hui Chang, Shenghui Wang, 2020. Real-world data medical knowledge graph: construction and applications. Artificial intelligence in medicine 103 (2020), 101817.
[17]
Steven Y Lin, Tait D Shanafelt, and Steven M Asch. 2018. Reimagining clinical documentation with artificial intelligence. In Mayo Clinic Proceedings, Vol. 93. Elsevier, 563–565.
[18]
Jialin Liu, Changyu Wang, and Siru Liu. 2023. Utility of ChatGPT in clinical practice. Journal of Medical Internet Research 25 (2023), e48568.
[19]
Lientje Maas, Mathan Geurtsen, Florian Nouwt, Stefan Schouten, Robin Van De Water, Sandra Van Dulmen, Fabiano Dalpiaz, Kees Van Deemter, and Sjaak Brinkkemper. 2020. The Care2Report System: Automated Medical Reporting as an Integrated Solution to Reduce Administrative Burden in Healthcare. In HICSS. 1–10.
[20]
Gregory Makoul, Raymond H Curry, and Paul C Tang. 2001. The use of electronic medical records: communication patterns in outpatient encounters. Journal of the American Medical Informatics Association 8, 6 (2001), 610–615.
[21]
Nir Menachemi and Taleah H Collum. 2011. Benefits and drawbacks of electronic health record systems. Risk management and healthcare policy (2011), 47–55.
[22]
Stéphane M Meystre, Guergana K Savova, Karin C Kipper-Schuler, and John F Hurdle. 2008. Extracting information from textual documents in the electronic health record: a review of recent research. Yearbook of medical informatics 17, 01 (2008), 128–144.
[23]
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, 2023. Can generalist foundation models outcompete special-purpose tuning? case study in medicine. arXiv preprint arXiv:2311.16452 (2023).
[24]
Aditya kumar Purohit, Aditya Upadhyaya, and Adrian Holzer. 2023. Chatgpt in healthcare: Exploring ai chatbot for spontaneous word retrieval in aphasia. In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing. 1–5.
[25]
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. 2023. Robust speech recognition via large-scale weak supervision. In International Conference on Machine Learning. PMLR, 28492–28518.
[26]
Arya S Rao, Michael Pang, John Kim, Meghana Kamineni, Winston Lie, Anoop K Prasad, Adam Landman, Keith Dryer, and Marc D Succi. 2023. Assessing the utility of ChatGPT throughout the entire clinical workflow. medRxiv (2023), 2023–02.
[27]
Mary Sabry Abdel-Messih and Maged N Kamel Boulos. 2023. ChatGPT in clinical toxicology. JMIR Medical Education 9 (2023), e46876.
[28]
Benjamin Schloss and Sandeep Konam. 2020. Towards an automated SOAP note: classifying utterances from medical conversations. In Machine Learning for Healthcare Conference. PMLR, 610–631.
[29]
Christine Sinsky, Lacey Colligan, Ling Li, Mirela Prgomet, Sam Reynolds, Lindsey Goeders, Johanna Westbrook, Michael Tutty, and George Blike. 2016. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Annals of internal medicine 165, 11 (2016), 753–760.
[30]
Ming Tai-Seale, Cliff W Olson, Jinnan Li, Albert S Chan, Criss Morikawa, Meg Durbin, Wei Wang, and Harold S Luft. 2017. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health affairs 36, 4 (2017), 655–662.
[31]
Paul C Tang, Michael P LaRosa, and Susan M Gorden. 1999. Use of computer-based records, completeness of documentation, and appropriateness of documented clinical decisions. Journal of the American Medical Informatics Association 6, 3 (1999), 245–251.
[32]
Augustin Toma, Senthujan Senkaiahliyan, Patrick R Lawler, Barry Rubin, and Bo Wang. 2023. Generative AI could revolutionize health care—but not if control is ceded to big tech. Nature 624, 7990 (2023), 36–38.
[33]
Changyu Wang, Siru Liu, Hao Yang, Jiulin Guo, Yuxuan Wu, and Jialin Liu. 2023. Ethical considerations of using ChatGPT in health care. Journal of Medical Internet Research 25 (2023), e48009.
[34]
Lawrence L. Weed. 1964. Medical records, patient care, and medical education. Irish Journal of Medical Science 39, 6 (June 1964), 271–282. https://doi.org/10.1007/bf02945791
[35]
Saul J Weiner, Shiyuan Wang, Brendan Kelly, Gunjan Sharma, and Alan Schwartz. 2020. How accurate is the medical record? A comparison of the physician’s note with a concealed audio recording in unannounced standardized patient encounters. Journal of the American Medical Informatics Association 27, 5 (2020), 770–775.
[36]
Sharon M Weyer, Nancy Konrad, Diane Esola, Meredith A Goodwin, Kurt C Stange, and Susan A Flocke. 2005. Features of medical records in community practices and their association with preventive service delivery. Medical care 43, 1 (2005), 28–33.
Index Terms
- AscleAI: A LLM-based Clinical Note Management System for Enhancing Clinician Productivity
Recommendations
ShennongMGS: An LLM-based Chinese Medication Guidance System
The rapidly evolving field of Large Language Models (LLMs) holds immense promise for healthcare, particularly in medication guidance and adverse drug reaction prediction. Despite their potential, existing LLMs face challenges in dealing with complex ...
Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care
AbstractWe aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain ...
Comments
Information & Contributors
Information
Published In
May 2024
4761 pages
ISBN:9798400703317
DOI:10.1145/3613905
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 May 2024
Check for updates
Author Tags
Qualifiers
- Work in progress
- Research
- Refereed limited
Data Availability
Funding Sources
- Institute of Information & communications Technology Planning & Evaluation
- National Research Foundation of Korea
Conference
Acceptance Rates
Overall Acceptance Rate 6,164 of 23,696 submissions, 26%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 477Total Downloads
- Downloads (Last 12 months)477
- Downloads (Last 6 weeks)107
Reflects downloads up to 10 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderFull Text
View this article in Full Text.
Full TextHTML Format
View this article in HTML Format.
HTML Format