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Source: Image created by the authors; Copyright: Rachel Umoren; URL: https:\/\/mededu.jmir.org\/2024\/1\/e50705\/; License: Creative Commons Attribution (CC-BY).","subscriptionId":"467","themeSequence":null,"_data":{"pubId":"25787","issueId":"974","datePublished":"2024-02-01 09:15:03","seq":"11","views":"0","accessStatus":"0","publicArticleId":"mededu_v10i1e50705","galleys":[{"_data":{"galleyId":"1","articleId":"50705","fileId":"860326","label":"HTML","sequence":"1","fileName":"50705-860326-21-PB.xml","originalFileName":"mededu_v10i1e50705.xml","fileType":"text\/xml","fileSize":"113610","status":"","dateModified":"2024-02-01 12:55:42","dateUploaded":"2024-02-01 12:55:42","type":"public","styleFileId":null,"styleFile":null,"images":[],"xml_galley_id":"1","actual_galley_id":"26389"}},{"_data":{"galleyId":"2","articleId":"50705","fileId":"860326","label":"PDF","sequence":"1","fileName":"50705-860326-21-PB.xml","originalFileName":"mededu_v10i1e50705.xml","fileType":"text\/xml","fileSize":"113610","status":"","dateModified":"2024-02-01 12:55:42","dateUploaded":"2024-02-01 12:55:42","type":"public","styleFileId":null,"styleFile":null,"images":[],"xml_galley_id":"2","actual_galley_id":"26389"}}],"articleId":"50705","userId":"339985","journalId":"20","sectionId":"1302","publication_deadline":"2024-01-08 23:59:59","sectionTitle":"Theme Issue: ChatGPT and Generative Language Models in Medical Education","sectionAbbrev":"ChatGPT in Med Ed","title":"Increasing Realism and Variety of Virtual Patient Dialogues for Prenatal Counseling Education Through a Novel Application of ChatGPT: Exploratory Observational Study","abstract":"Background: Using virtual patients, facilitated by natural language processing, provides a valuable educational experience for learners. Generating a large, varied sample of realistic and appropriate responses for virtual patients is challenging. Artificial intelligence (AI) programs can be a viable source for these responses, but their utility for this purpose has not been explored. Objective: In this study, we explored the effectiveness of generative AI (ChatGPT) in developing realistic virtual standardized patient dialogues to teach prenatal counseling skills. Methods: ChatGPT was prompted to generate a list of common areas of concern and questions that families expecting preterm delivery at 24 weeks gestation might ask during prenatal counseling. ChatGPT was then prompted to generate 2 role-plays with dialogues between a parent expecting a potential preterm delivery at 24 weeks and their counseling physician using each of the example questions. The prompt was repeated for 2 unique role-plays: one parent was characterized as anxious and the other as having low trust in the medical system. Role-play scripts were exported verbatim and independently reviewed by 2 neonatologists with experience in prenatal counseling, using a scale of 1-5 on realism, appropriateness, and utility for virtual standardized patient responses. Results: ChatGPT generated 7 areas of concern, with 35 example questions used to generate role-plays. The 35 role-play transcripts generated 176 unique parent responses (median 5, IQR 4-6, per role-play) with 268 unique sentences. Expert review identified 117 (65%) of the 176 responses as indicating an emotion, either directly or indirectly. Approximately half (98\/176, 56%) of the responses had 2 or more sentences, and half (88\/176, 50%) included at least 1 question. More than half (104\/176, 58%) of the responses from role-played parent characters described a feeling, such as being scared, worried, or concerned. The role-plays of parents with low trust in the medical system generated many unique sentences (n=50). Most of the sentences in the responses were found to be reasonably realistic (214\/268, 80%), appropriate for variable prenatal counseling conversation paths (233\/268, 87%), and usable without more than a minimal modification in a virtual patient program (169\/268, 63%). Conclusions: Generative AI programs, such as ChatGPT, may provide a viable source of training materials to expand virtual patient programs, with careful attention to the concerns and questions of patients and families. Given the potential for unrealistic or inappropriate statements and questions, an expert should review AI chat outputs before deploying them in an educational program. 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Patient knowledge about AF and its management is paramount but often limited. Patients need to be appropriately informed about treatment options, medicinal adherence, and potential consequences of nonadherence, while also understanding treatment goals and expectations from it. Mobile health apps have experienced an explosion both in their availability and acceptance as \u201csoft interventions\u201d for patient engagement and education; however, the prolific nature of such solutions revealed a gap in the evidence base regarding their efficacy and impact. Virtual patients (VPs), interactive computer simulations, have been used as learning activities in modern health care education. VPs demonstrably improved cognitive and behavioral skills; hence, they have been effectively implemented across undergraduate and postgraduate curricula. However, their application in patient education has been rather limited so far.\n<strong>Objective:<\/strong> This work aims to implement and evaluate the efficacy of a mobile-deployed VP regimen for the education and engagement of patients with AF on crucial topics regarding their condition. A mobile VP app is being developed with the goal of each VP being a simple scenario with a set goal and very specific messages and will be subsequently attempted and evaluated.\n<strong>Methods:<\/strong> A mobile VP player app is being developed so as to be used for the design of 3 educational scenarios for AF management. A pseudorandomized controlled trial for the efficacy of VPs is planned to be executed at 3 sites in Greece, Ukraine, and Kazakhstan for patients with AF. The Welch <i>t<\/i> test will be used to demonstrate the performance of patients\u2019 evaluation of the VP experience.\n<strong>Results:<\/strong> Our study is at the development stage. A preliminary study regarding the system\u2019s development and feasibility was initiated in December 2022. The results of our study are expected to be available in 2024 or when the needed sample size is achieved.\n<strong>Conclusions:<\/strong> This study aims to evaluate and demonstrate the first significant evidence for the value of VP resources in outreach and training endeavors for empowering and patients with AF and fostering healthy habits among them.\n","subjectClass":"atrial fibrillation;virtual patient;scenario based learning;technology enhanced learning;mHealth;mobile health;patient engagement;patient education;cardiac arrhythmia;mortality;mobile application;mobile app;health education;randomized control trial;cardiology;cardiac;heart;Greece;Ukraine;Kazakhstan;clinical decision support systems;CDSS;virtual patient scenario;myocardial infarction;arrhythmia;stroke","language":"en","dateSubmitted":"2023-01-30 18:56:36","dateStatusModified":"2024-01-23 08:30:08","lastModified":"2024-01-23 08:30:10","status":"3","granularStatusId":"022_010","submissionProgress":"0","currentRound":"2","submissionFileId":"725998","revisedFileId":"826630","reviewFileId":"726014","copyeditFileId":"860155","conflictOfInterest":"GG received fees for lecture travels or research from Bayer, Boehringer Ingelheim, Pfizer, and LeoPharma. 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