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 AI-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 AI intervention group showed significantly higher scores on medical interviews than the control group (AI 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 AI-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.

Aim: To investigate the distribution of nurse practitioners (NPs) across Japan and their perceived facilitators and barriers to NP implementation in Japan.Background: NP certification examinations have been conducted in Japan since 2011, and by 2020, there were 487 NPs in the country. The momentum of NP implementation is slower in Japan compared with other countries.Methods: A cross‐sectional descriptive study, following the STROBE guidelines, was conducted. Web‐based survey questionnaires, developed by the authors, were administered to 248 NPs whose email addresses were maintained by the certification management body.Results: Valid responses were obtained from 101 NPs (response rate: 40.7%), of which 34% were male. The respondents had more than 12 years of registered nurse experience on an average before enrolling in the graduate NP program. 53% were employed as NPs from the beginning, while 25% were initially employed as registered nurses and later advanced to NPs, and 11% still worked as RNs. A majority worked in hospitals with beds. Many NPs perceived the lack of NP national licensure and reimbursement benefits as barriers to NP implementation, whereas recognition from superiors and organizations was considered facilitators.Conclusions: Despite their small numbers in Japan, NPs take on crucial tasks and contribute to the appropriate distribution of medical resources. The NP licensure system and recognition from superiors and organizations may be necessary to promote NP roles in Japan.Implications for nursing and health policy: Some certified NPs still work as registered nurses. Recognition from nursing and organization administrators is critical to implementing NPs. To this end, a reimbursement system benefiting the organizations and a legislation facilitating NP employment are required.

The recent introduction of Chat Generative Pre-trained Transformer 4 Vision (ChatGPT-4V) has expanded the capabilities of language models to include image input features, potentially broadening their application in the medical field. This Research Letter evaluates the performance of ChatGPT-4V in interpreting clinical images and tables through the Japanese Medical Licensing Exam (JMLE). Employing the September 25, 2023, version of ChatGPT-4V, the study compared the program’s responses to the 117th JMLE against the passing criteria and the average scores of human examinees. While ChatGPT-4V surpassed the passing threshold with an 85.1% correct response rate in essential knowledge and 76.5% in other areas, it fell short in image-based (71.9%) and table-based questions (35.0%), indicating a significant gap compared to human performance. This suggests limitations in the model’s image and table interpretation, exacerbated by its lower proficiency in non-Latin characters and potential overreliance on text information. Despite its success in passing the JMLE, the study highlights the need for further development of ChatGPT-4V to enhance its reliability for medical applications, including diagnostics.

Suicide prevention is a crucial policy issue in Japan to be addressed nationally. Nevertheless, if there are regional differences in suicide, even in adjacent sub-regions, measures may need to be taken at the sub-regional level. Previous studies have not compared regional differences in suicide based on the size of policy units, such as prefectures, secondary medical areas, and municipalities. This study used the number of suicides from open data for 10 years from 2009 to 2018 to obtain shrinkage estimates of the standardized mortality ratio (SMR) using the Bayesian hierarchical model. We visualized and compared the regional disparities in suicide for each policy unit. For each gender and policy unit, adjacent regions had similar clusters of SMRs and positive spatial autocorrelation of global Moran’s I (p < 0.001 for each). Comparisons between each policy unit showed that even if the SMR was low for the prefectural units, there were regions with high SMRs in municipalities and secondary medical areas, and vice versa. It was found that assessing suicide solely on a prefecture-by-prefecture basis may overlook regional disparities in suicide. This research emphasizes the need to establish suicide indicators at the secondary medical or municipal level and execute individual suicide prevention interventions in neighboring communities. Prefectures can also play a role in developing collaborative cooperation between neighboring regions by acting as actors.

There is insufficient research on the usefulness of psychological interventions, such as psychological first aid (PFA), during outbreaks. We searched for and critically appraised systematic reviews that examined the effectiveness of PFA during infectious disease outbreaks, such as the novel coronavirus disease (COVID‐19). Systematic reviews that examined the efficacy of PFA in the severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus, Ebola virus disease, and COVID‐19 outbreaks were searched through PubMed on February 19, 2021. The three included systematic reviews were critically appraised and assessed using AMSTAR‐2. One review's overall confidence in its findings was evaluated as “high,” which suggested that PFA training had a favorable effect on healthcare personnel. Furthermore, the review also demonstrated that PFA was commonly used during outbreaks and could be delivered through multiple methods, such as a phone or video call. Although it was anticipated that PFA would improve subjective well‐being, reports showed no evidence of reduced depression or insomnia. Future studies should examine additional numbers of PFA recipients and conduct quasi‐experimental studies to better understand the effectiveness of PFA. Evidence on its effectiveness in infectious disease outbreaks is still lacking, along with research and evaluation methods. Quasi‐experimental studies, such as comparisons with other psychological interventions, are required to better understand the effectiveness of PFA.

Introduction: The COVID-19 pandemic precipitated substantial disruptions in healthcare utilization globally. In Japan, reduced healthcare utilization during the pandemic’s early phases had been documented previously. However, few studies have investigated the impact of the pandemic’s later stages (2022–2023) on healthcare utilization rates, particularly in the Japanese context. Methods: We employed a quasi-Poisson regression model, adapted from the FluMOMO framework, to analyze temporal trends in Japanese healthcare utilization throughout the pandemic until November 2023. We estimated inpatient and outpatient volumes and hospital length of stay by bed type (general and psychiatric). Results: In general hospital beds, inpatient volumes remained significantly below pre-pandemic levels for every month until November 2023, with a reduction of 7.8 percent in 2023 compared to pre-pandemic levels. Psychiatric inpatient volumes, which had been declining before the pandemic, continued this downward trend, with the average occupancy rate decreasing by approximately 5.3% to 81.3% in 2023 compared to pre-pandemic levels. Significantly reduced outpatient volumes for both general and psychiatric care, in addition to prolonged lengths of hospital stay for psychiatric beds, were observed sporadically for several months in 2022 and 2023, persisting beyond the cessation of state of emergency and quasi-state of emergency declarations. Conclusion: The COVID-19 pandemic fundamentally altered healthcare utilization patterns in Japan. We observed a sustained reduction in general and psychiatric inpatient volumes relative to pre-pandemic baselines nationwide. The prolonged impact on healthcare utilization patterns, persisting beyond emergency measures, warrants continued monitoring of service delivery.