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Intelligent virtual case learning system based on real medical records and natural language processing.

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Title: Intelligent virtual case learning system based on real medical records and natural language processing.
Authors: Wang M; Information Management and Big Data Center, Peking University Third Hospital, Beijing, China., Sun Z; Information Management and Big Data Center, Peking University Third Hospital, Beijing, China., Jia M; Information Management and Big Data Center, Peking University Third Hospital, Beijing, China., Wang Y; Education Section, Peking University Third Hospital, Beijing, China., Wang H; Education Section, Peking University Third Hospital, Beijing, China., Zhu X; Goodwill Hessian Health Technology Co. Ltd, Beijing, China., Chen L; Goodwill Hessian Health Technology Co. Ltd, Beijing, China., Ji H; Information Management and Big Data Center, Peking University Third Hospital, Beijing, China. puh3_imc@bjmu.edu.cn.
Source: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2022 Mar 04; Vol. 22 (1), pp. 60. Date of Electronic Publication: 2022 Mar 04.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE
Imprint Name(s): Original Publication: London : BioMed Central, [2001-
MeSH Terms: Natural Language Processing* , Students, Medical*, Artificial Intelligence ; Humans ; Learning ; Medical Records
Abstract: Background: Modernizing medical education by using artificial intelligence and other new technologies to improve the clinical thinking ability of medical students is an important research topic in recent years. Prominent medical universities are actively conducting research and exploration in this area. In particular, given the shortage of human resources, the need to maintain social distancing to prevent the spread of the epidemics, and the increase in the cost of medical education, it is critical to harness online learning to promote medical education. A virtual case learning system that uses natural language processing technology to process and present a hospital's real medical records and evaluate student responses can effectively improve medical students' clinical thinking abilities.
Objective: The purpose of this study is to develop a virtual case system, AIteach, based on actual complete hospital medical records and natural language processing technology, and achieve clinical thinking ability improvement through a contactless, self-service, trial-and-error system application.
Methods: Case extraction is performed on a hospital's case data center and the best-matching cases are produced through natural language processing, word segmentation, synonym conversion, and sorting. A standard clinical questioning data module, virtual case data module, and student learning difficulty module are established to achieve simulation. Students can view the objective examination and inspection data of actual cases, including details of the consultation and physical examination, and automatically provide their learning response via a multi-dimensional evaluation system. In order to assess the changes in students' clinical thinking after using AIteach, 15 medical graduate students were subjected to two simulation tests before and after learning through the virtual case system. The tests, which included the full-process case examination of cases having the same difficulty level, examined core clinical thinking test points such as consultation, physical examination, and disposal, and generated multi-dimensional evaluation indicators (rigor, logic, system, agility, and knowledge expansion). Thus, a complete and credible evaluation system is developed.
Results: The AIteach system used an internal and external double-cycle learning model. Students collect case information through online inquiries, physical examinations, and other means, analyze the information for feedback verification, and generate their detailed multi-dimensional clinical thinking after learning. The feedback report can be evaluated and its knowledge gaps analyzed. Such learning based on real cases is in line with traditional methods of disease diagnosis and treatment, and addresses the practical difficulties in reflecting actual disease progression while keeping pace with recent research. Test results regarding short-term learning showed that the average score (P < 0.01) increased from 69.87 to 85.6, the five indicators of clinical thinking evaluation improved, and there was obvious logical improvement, reaching 47%.
Conclusion: By combining real cases and natural language processing technology, AIteach can provide medical students (including undergraduates and postgraduates) with an online learning tool for clinical thinking training. Virtual case learning helps students to cultivate clinical thinking abilities even in the absence of clinical tutor, such as during pandemics or natural disasters.
(© 2022. The Author(s).)
References: BMC Med Educ. 2020 May 8;20(1):144. (PMID: 32384888)
BMC Med Educ. 2019 May 22;19(1):165. (PMID: 31118004)
J Surg Educ. 2012 Jan-Feb;69(1):105-12. (PMID: 22208841)
J Korean Med Sci. 2019 Oct 14;34(39):e255. (PMID: 31602825)
Stud Health Technol Inform. 2008;132:101-5. (PMID: 18391266)
Eur Radiol. 2005 Apr;15(4):792-7. (PMID: 15146292)
J Dent Educ. 2020 Aug 26;:. (PMID: 32844402)
Med Teach. 2015;37(9):813-8. (PMID: 25523009)
BMC Med Res Methodol. 2008 Oct 30;8:70. (PMID: 18973665)
BMJ. 2009 May 14;338:b1802. (PMID: 19443914)
JAMA. 2020 Sep 15;324(11):1033-1034. (PMID: 32857137)
Comput Educ. 2020 Feb;145:103726. (PMID: 32565611)
JAMA. 2020 Jun 2;323(21):2131-2132. (PMID: 32232420)
Am J Pharm Educ. 2013 Oct 14;77(8):172. (PMID: 24159213)
Stud Health Technol Inform. 2016;220:219-26. (PMID: 27046582)
Mt Sinai J Med. 2009 Aug;76(4):330-43. (PMID: 19642147)
Adv Health Sci Educ Theory Pract. 2019 Dec;24(5):959-969. (PMID: 31641941)
JMIR Med Inform. 2021 Apr 9;9(4):e24073. (PMID: 33720840)
BMC Med Educ. 2020 Oct 2;20(1):341. (PMID: 33008392)
Med Educ. 2009 Apr;43(4):303-11. (PMID: 19335571)
Proc AMIA Annu Fall Symp. 1996;:483-7. (PMID: 8947713)
J Surg Educ. 2021 Sep-Oct;78(5):1492-1499. (PMID: 33814338)
JMIR Med Inform. 2020 Jan 20;8(1):e16912. (PMID: 31958069)
J Adv Nurs. 1998 Feb;27(2):414-8. (PMID: 9515655)
Patient Educ Couns. 2021 Jan;104(1):85-91. (PMID: 32631649)
Contributed Indexing: Keywords: Architectures for educational technology system; Artificial intelligence; Clinical thinking ability; Distance education and online learning; Virtual medical records
Entry Date(s): Date Created: 20220305 Date Completed: 20220502 Latest Revision: 20220502
Update Code: 20260130
PubMed Central ID: PMC8895690
DOI: 10.1186/s12911-022-01797-7
PMID: 35246134
Database: MEDLINE
Description
ISSN:1472-6947
DOI:10.1186/s12911-022-01797-7