start-ver=1.4 cd-journal=joma no-vol=10 cd-vols= no-issue= article-no= start-page=e58753 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240923 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Enhancing Medical Interview Skills Through AI-Simulated PatientInteractions:Nonrandomized Controlled Trial en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background: Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce.
Objective: This study aimed to investigate whether medical students' interview skills could be improved by engaging with Al-simulated patients using large language models, including the provision of feedback.
Methods: This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study.
Results: The Al intervention group showed significantly higher scores on medical interviews than the control group (Al group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient-2.0 to-2.1). No significant safety concerns were observed.
Conclusions: Education through medical interviews using Al-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education. en-copyright= kn-copyright= en-aut-name=YamamotoAkira en-aut-sei=Yamamoto en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KodaMasahide en-aut-sei=Koda en-aut-mei=Masahide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=OgawaHiroko en-aut-sei=Ogawa en-aut-mei=Hiroko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=MiyoshiTomoko en-aut-sei=Miyoshi en-aut-mei=Tomoko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=MaedaYoshinobu en-aut-sei=Maeda en-aut-mei=Yoshinobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=OtsukaFumio en-aut-sei=Otsuka en-aut-mei=Fumio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=InoHideo en-aut-sei=Ino en-aut-mei=Hideo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= affil-num=1 en-affil=Department of Hematology and Oncology, Okayama University Hospital kn-affil= affil-num=2 en-affil=Co-learning Community Healthcare Re-innovation Office, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Primary Care and Medical Education, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine kn-affil= affil-num=4 en-affil=Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Department of Hematology and Oncology, Okayama University Hospital kn-affil= affil-num=6 en-affil=Department of General Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=7 en-affil=Center for Education in Medicine and Health Sciences, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= en-keyword=medical interview kn-keyword=medical interview en-keyword=generative pretrained transformer kn-keyword=generative pretrained transformer en-keyword=large language model kn-keyword=large language model en-keyword=simulation-based learning kn-keyword=simulation-based learning en-keyword=OSCE kn-keyword=OSCE en-keyword=artificial intelligence kn-keyword=artificial intelligence en-keyword=medical education kn-keyword=medical education en-keyword=simulated patients kn-keyword=simulated patients en-keyword=nonrandomized controlled trial kn-keyword=nonrandomized controlled trial END start-ver=1.4 cd-journal=joma no-vol=7 cd-vols= no-issue= article-no= start-page=e47798 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230810 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Assessing Vulnerability to Surges in Suicide-Related Tweets Using Japan Census Data: Case-Only Study en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background: As the use of social media becomes more widespread, its impact on health cannot be ignored. However, limited research has been conducted on the relationship between social media and suicide. Little is known about individuals’ vulnerable to suicide, especially when social media suicide information is extremely prevalent.
Objective: This study aims to identify the characteristics underlying individuals’ vulnerability to suicide brought about by an increase in suicide-related tweets, thereby contributing to public health.
Methods: A case-only design was used to investigate vulnerability to suicide using individual data of people who died by suicide and tweet data from January 1, 2011, through December 31, 2014. Mortality data were obtained from Japanese government statistics, and tweet data were provided by a commercial service. Tweet data identified the days when suicide-related tweets surged, and the date-keyed merging was performed by considering 3 and 7 lag days. For the merged data set for analysis, the logistic regression model was fitted with one of the personal characteristics of interest as a dependent variable and the dichotomous exposure variable. This analysis was performed to estimate the interaction between the surges in suicide-related tweets and personal characteristics of the suicide victims as case-only odds ratios (ORs) with 95% CIs. For the sensitivity analysis, unexpected deaths other than suicide were considered.
Results: During the study period, there were 159,490 suicides and 115,072 unexpected deaths, and the number of suicide-related tweets was 2,804,999. Following the 3-day lag of a highly tweeted day, there were significant interactions for those who were aged 40 years or younger (OR 1.09, 95% CI 1.03-1.15), male (OR 1.12, 95% CI 1.07-1.18), divorced (OR 1.11, 95% CI 1.03 1.19), unemployed (OR 1.12, 95% CI 1.02-1.22), and living in urban areas (OR 1.26, 95% CI 1.17 1.35). By contrast, widowed individuals had significantly lower interactions (OR 0.83, 95% CI 0.77-0.89). Except for unemployment, significant relationships were also observed for the 7-day lag. For the sensitivity analysis, no significant interactions were observed for other unexpected deaths in the 3-day lag, and only the widowed had a significantly larger interaction than those who were married (OR 1.08, 95% CI 1.02-1.15) in the 7-day lag.
Conclusions: This study revealed the interactions of personal characteristics associated with susceptibility to suicide-related tweets. In addition, a few significant relationships were observed in the sensitivity analysis, suggesting that such an interaction is specific to suicide deaths. In other words, individuals with these characteristics, such as being young, male, unemployed, and divorced, may be vulnerable to surges in suicide-related tweets. Thus, minimizing public health strain by identifying people who are vulnerable and susceptible to a surge in suicide-related information on the internet is necessary. en-copyright= kn-copyright= en-aut-name=MitsuhashiToshiharu en-aut-sei=Mitsuhashi en-aut-mei=Toshiharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= affil-num=1 en-affil=Center for Innovative Clinical Medicine, Okayama University Hospital kn-affil= en-keyword=case-only approach kn-keyword=case-only approach en-keyword=mass media kn-keyword=mass media en-keyword=public health kn-keyword=public health en-keyword=social media kn-keyword=social media en-keyword=suicidal risk kn-keyword=suicidal risk en-keyword=suicide prevention kn-keyword=suicide prevention en-keyword=suicide kn-keyword=suicide en-keyword=suicide-related tweets kn-keyword=suicide-related tweets en-keyword=Twitter kn-keyword=Twitter END start-ver=1.4 cd-journal=joma no-vol=23 cd-vols= no-issue=2 article-no= start-page=e25232 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2021 dt-pub=20210218 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Impact of the COVID-19 Pandemic on the Psychological Distress of Medical Students in Japan: Cross-sectional Survey Study en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background:
The COVID-19 pandemic has negatively affected medical education. However, little data are available about medical students’ distress during the pandemic.
Objective:
This study aimed to provide details on how medical students have been affected by the pandemic.
Methods:
A cross-sectional study was conducted. A total of 717 medical students participated in the web-based survey. The survey included questions about how the participants’ mental status had changed from before to after the Japanese nationwide state of emergency (SOE).
Results:
Out of 717 medical students, 473 (66.0%) participated in the study. In total, 29.8% (141/473) of the students reported concerns about the shift toward online education, mostly because they thought online education would be ineffective compared with in-person learning. The participants’ subjective mental health status significantly worsened after the SOE was lifted (P<.001). Those who had concerns about a shift toward online education had higher odds of having generalized anxiety and being depressed (odds ratio [OR] 1.97, 95% CI 1.19-3.28) as did those who said they would request food aid (OR 1.99, 95% CI 1.16-3.44) and mental health care resources (OR 3.56, 95% CI 2.07-6.15).
Conclusions:
Given our findings, the sudden shift to online education might have overwhelmed medical students. Thus, we recommend that educators inform learners that online learning is not inferior to in-person learning, which could attenuate potential depression and anxiety. en-copyright= kn-copyright= en-aut-name=NishimuraYoshito en-aut-sei=Nishimura en-aut-mei=Yoshito kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OchiKanako en-aut-sei=Ochi en-aut-mei=Kanako kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=TokumasuKazuki en-aut-sei=Tokumasu en-aut-mei=Kazuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=ObikaMikako en-aut-sei=Obika en-aut-mei=Mikako kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=HagiyaHideharu en-aut-sei=Hagiya en-aut-mei=Hideharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KataokaHitomi en-aut-sei=Kataoka en-aut-mei=Hitomi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=OtsukaFumio en-aut-sei=Otsuka en-aut-mei=Fumio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= affil-num=1 en-affil=Department of General Medicine, Okayama University Hospital kn-affil= affil-num=2 en-affil=Department of General Medicine, Okayama University Hospital kn-affil= affil-num=3 en-affil=Department of General Medicine, Okayama University Hospital kn-affil= affil-num=4 en-affil=Department of General Medicine, Okayama University Hospital kn-affil= affil-num=5 en-affil=Department of General Medicine, Okayama University Hospital kn-affil= affil-num=6 en-affil=Department of General Medicine, Okayama University Hospital kn-affil= affil-num=7 en-affil=Department of General Medicine, Okayama University Hospital kn-affil= en-keyword=COVID-19 kn-keyword=COVID-19 en-keyword=online education kn-keyword=online education en-keyword=depression kn-keyword=depression en-keyword=pandemic kn-keyword=pandemic en-keyword=anxiety kn-keyword=anxiety en-keyword=medical student kn-keyword=medical student END