AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication.

Importance: Timely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts. Objective: To investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies. Design, Setting, and Participants: Randomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023. Exposure: Access to GenAI-drafted replies for patient messages. Main Outcomes and Measures: Time spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used. Results: Fifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement. Conclusions and Relevance: In this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.

Resource Document on Best Practices in Synchronous Videoconferencing-Based Telemental Health.

Background: This document represents an updated collaboration between the American Psychiatric Association (APA) and the American Telemedicine Association (ATA) to create a consolidated update of the previous APA and ATA official documents and resources in telemental health, to provide a single guide on clinical best practices for providing mental health services through synchronous videoconference. Methods: A joint writing committee drawn from the APA Committee on Telepsychiatry and the ATA TMH Special Interest Group (TMH SIG). was convened to draft and finalize the guidelines. This document draws directly from the 2018 APA/ATA guide and the ATA s previous guidelines, selecting from key statements/guidelines, consolidating them across documents, and then updating them where indicated. Guideline approval was provided following internal review by the APA, the ATA, the Board of Directors of the ATA, and the Joint Reference Committee of the APA. Results: The guidelines contain requirements, recommendations, and actions that are identified by text containing the keywords "shall," "should," or "may." Conclusions: Compliance with these recommendations will not guarantee accurate diagnoses or successful outcomes. The purpose of this guide is to assist providers in providing effective and safe medical care founded on expert consensus, research evidence, available resources, and patient needs.

Serious Games and Gamification: Health Care Workers' Experience, Attitudes, and Knowledge.

OBJECTIVE: With the rapid advancement of digital technology due to COVID-19, the health care field is embracing the use of digital technologies for learning, which presents an opportunity for teaching methods such as serious games to be developed and improved. Technology offers more options for these educational approaches. The goal of this study was to assess health care workers' experiences, attitudes, and knowledge regarding serious games in training. METHODS: The convenience sample consisted of 223 participants from the specialties of internal medicine and psychiatry who responded to questions regarding sociodemographic data, experience, attitudes, and knowledge regarding serious games. This study used an ordinal regression model to analyze the relationship between knowledge, attitudes, and experiences and the idea or wish to implement serious games. RESULTS: The majority of healthcare workers were not familiar with serious games or gamification. The results show gender and age differences regarding familiarity and willingness to use serious games. With increasing age, the respondents preferred conventional and traditional learning methods to playful teaching elements; younger generations were significantly more motivated than older generations when envisioning using elements of serious games in the future. CONCLUSIONS: The COVID-19 pandemic has encouraged the use of new technologies and digitalization. This study describes positive attitudes toward serious games, mainly in younger people working in health care. Serious games present an opportunity to develop new approaches for postgraduate medical teachings and continuing medical education.