BACKGROUND: The working population encounters unique work-related stressors. Despite these challenges, accessibility to mental healthcare remains limited. Digital technology-enabled mental wellness tools can offer much-needed access to mental healthcare. However, existing literature has given limited attention to their relevance and user engagement, particularly for the working population.
OBJECTIVE: This study aims to assess user perceptions and feature utilisation of mindline at work, a nationally developed AI-enabled digital platform designed to improve mental wellness in the working population.
METHODS: This study adopted a mixed-methods design comprising a survey (n=399) and semistructured interviews (n=40) with office-based working adults. Participants were asked to use mindline at work for 4 weeks. We collected data about utilisation of the platform features, intention for sustained use and perceptions of specific features.
RESULTS: Participants under 5 years of work experience reported lower utilisation of multimedia resources but higher utilisation of emotion self-assessment tools and the AI chatbot compared with their counterparts (p<0.001). The platform received a moderate level of satisfaction (57%) and positive intention for sustained use (58%). Participants regarded mindline at work as an \'essential\' safeguard against workplace stress, valuing its secure and non-judgmental space and user anonymity. However, they wanted greater institutional support for office workers\' mental wellness to enhance the uptake. The AI chatbot was perceived as useful for self-reflection and problem-solving, despite limited maturity.
CONCLUSIONS: Identifying the unique benefits of specific features for different segments of working adults can foster a personalised user experience and promote mental well-being. Increasing workplace awareness is essential for platform adoption.

OBJECTIVE:  Though public health is an information-intense profession, there is a paucity of workforce with Public Health Informatics and Technology (PHIT) skills, which was evident during the coronavirus disease 2019 (COVID-19) pandemic. This need is addressed through the PHIT workforce program (2021-2025) by the Office of the National Coordinator for training and to increase racial and ethnic diversity in the PHIT workforce. The objective is to share details on the Training in Informatics for Underrepresented Minorities in Public Health (TRIUMPH) consortium, funded by the PHIT workforce program.
METHODS:  The TRIUMPH consortium is a collaboration between academic and practice partners with a commitment to training 879 students in PHIT. The Schools of Public Health and Nursing at the University of Minnesota, Jiann-Ping Hsu College of Public Health at Georgia Southern University, Morehouse School of Medicine, and Public Health Informatics Institute offer PHIT training through various programs. Academic institutions focus on student recruitment, developing courses/curriculum, and granting degrees/certificates, and the role of practice partners is to support experiential learning through internships/practicums.
RESULTS:  The TRIUMPH consortium is progressing toward its goals, with 692 students (79%) already trained in a PHIT modality as of December 2023. The learners comprise diverse race/ethnicity, including White (48%), Black/African American (32%), Asian (10%), White Hispanic (5%), American Indian/Alaska Native (2%), and Black Hispanic (1%). Numerous internships have been completed in settings ranging from state/local public health agencies to health care delivery systems. Diversity initiatives were supported by partnering with existing programs (e.g., the AMIA First Look program and the Nursing Knowledge Big Data Science conference).
CONCLUSIONS:  This consortium model is an excellent approach to informatics training and sharing expertise across partners. It provides scalability and broader geographic outreach while presenting opportunities to students from underrepresented backgrounds. Lessons learned have implications for overall informatics training (e.g., partnerships models, promoting racial/ethnic diversity).

During the coronavirus disease 2019 (COVID-19) pandemic, the number and types of dashboards produced increased to convey complex information using digestible visualizations. The pandemic saw a notable increase in genomic surveillance data, which genomic epidemiology dashboards presented in an easily interpretable manner. These dashboards have the potential to increase the transparency between the scientists producing pathogen genomic data and policymakers, public health stakeholders, and the public. This scoping review discusses the data presented, functional and visual features, and the computational architecture of six publicly available SARS-CoV-2 genomic epidemiology dashboards. We found three main types of genomic epidemiology dashboards: phylogenetic, genomic surveillance, and mutational. We found that data were sourced from different databases, such as GISAID, GenBank, and specific country databases, and these dashboards were produced for specific geographic locations. The key performance indicators and visualization used were specific to the type of genomic epidemiology dashboard. The computational architecture of the dashboards was created according to the needs of the end user. The genomic surveillance of pathogens is set to become a more common tool used to track ongoing and future outbreaks, and genomic epidemiology dashboards are powerful and adaptable resources that can be used in the public health response.

Here, we analyze the public health implications of recent legal developments - including privacy legislation, intergovernmental data exchange, and artificial intelligence governance - with a view toward the future of public health informatics and the potential of diverse data to inform public health actions and drive population health outcomes.

UNASSIGNED: To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.
UNASSIGNED: We undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly.
UNASSIGNED: We created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups.
UNASSIGNED: This platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes.
UNASSIGNED: We have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.

Medical and preventive integration effectively bridges the gap between \"treating diseases\" and \"preventing diseases\". Over the years, medical and preventive integration research has focused on chronic and chronic infectious diseases, with insufficient attention to acute ones. Confronting newly emerging infectious diseases establishing continuous monitoring, early warning, emergency response, and appropriate treatment will be a key focus for developing and reforming the healthcare system. Interoperability and sharing of medical and health data are essential prerequisites for bridging the gap between medical treatment and disease prevention and are also important for promoting intelligent surveillance and early warning of infectious diseases. Informatization is necessary to achieve efficient collaboration between medical treatment and disease prevention. Reviewing the development of medical and health informatization in the United States and Europe, this paper compares and discusses the problems and challenges in developing medical and health informatization in China. The aim is to provide references for the development of medical and health informatization and the innovation of medical and preventive integration mechanisms in the country.
医防融合是“治病”和“防病”有效衔接。多年来，医防融合研究多聚焦在慢性非传染性疾病及慢性传染性疾病，对急性传染病关注不足。面对新发突发传染病，如何做好持续监测、早期预警、应急处置和合理救治，将是医疗卫生体系发展和改革的重点。而医疗健康数据互通共享是弥合医防裂隙的首要前提，也是推动智慧化传染病监测预警的重要路径，信息化则是实现医防高效协同的必由之路。本文通过梳理美国、欧洲国家的医疗卫生信息化发展历程，对比并探讨我国医疗卫生信息化发展中存在的主要问题与挑战，以期为我国医疗卫生信息化的发展及医防融合机制创新提供参考。.

The field of public health informatics has undergone significant evolution in recent years, and advancements in technology and its applications are imperative to address emerging public health challenges. Interdisciplinary approaches and training can assist with these challenges. In 2023, the inaugural Public Health Informatics and Technology (PHIAT) Conference was established as a hybrid 3-day conference at the University of California, San Diego, and online. The conference\'s goal was to establish a forum for academics and public health organizations to discuss and tackle new opportunities and challenges in public health informatics and technology. This paper provides an overview of the quest for interest, speakers and topics, evaluations from the attendees, and lessons learned to be implemented in future conferences.

BACKGROUND: Despite accumulating evidence concerning the association between daily step counts and mortality or disease risks, it is unclear whether daily step counts are associated with healthy life years.
METHODS: We used the combined dataset of the Comprehensive Survey of Living Conditions and the National Health and Nutrition Survey conducted for a randomly sampled general population in Japan, 2019. Daily step counts were measured for 4957 adult participants. The associations of daily step counts with activity limitations in daily living and self-assessed health were evaluated using a multivariable logistic regression model. The bootstrap method was employed to mitigate uncertainties in estimating the threshold of daily step counts.
RESULTS: The median age was 60 (44-71) years, and 2592 (52.3%) were female. The median daily step counts were 5650 (3332-8452). The adjusted OR of activity limitations in daily living for the adjacent daily step counts was 0.27 (95% CI 0.26 to 0.27) for all ages and 0.25 (95% CI 0.25 to 0.26) for older adults at the lowest, with the thresholds of significant association at 9000 step counts. The OR of self-assessed unhealthy status was 0.45 (95% CI 0.44 to 0.46) for all ages and 0.42 (95% CI 0.41 to 0.43) for older adults at the lowest, with the thresholds at 11 000 step counts.
CONCLUSIONS: Daily step counts were significantly associated with activity limitations in daily living and self-assessed health as determinants of healthy life years, up to 9000 and 11 000 step counts, respectively. These results suggest a target of daily step counts to prolong healthy life years within health initiatives.

Sexually transmitted infections (STIs) continue to pose a substantial public health challenge in the United States (US). Surveillance, a cornerstone of disease control and prevention, can be strengthened to promote more timely, efficient, and equitable practices by incorporating health information exchange (HIE) and other large-scale health data sources into reporting. New York City patient-level electronic health record data between January 1, 2018 and June 30, 2023 were obtained from Healthix, the largest US public HIE. Healthix data were linked to neighborhood-level information from the American Community Survey. In this casecontrol study, chlamydia, gonorrhea, and HIV-positive cases were compared to controls to estimate the odds of receiving a specific laboratory test or positive result using generalized estimating equations with logit function and robust standard errors. Among 1,519,121 tests performed for chlamydia, 1,574,772 for gonorrhea, and 1,200,560 for HIV, 2%, 0.6% and 0.3% were positive for chlamydia, gonorrhea, and HIV, respectively. Chlamydia and gonorrhea co-occurred in 1,854 cases (7% of chlamydia and 21% of gonorrhea total cases). Testing behavior was often incongruent with geographic and sociodemographic patterns of positive cases. For example, people living in areas with the highest levels of poverty were less likely to test for gonorrhea but almost twice as likely to test positive compared to those in low poverty areas. Regional HIE enabled review of testing and cases using granular and complementary data not typically available given existing reporting practices. Enhanced surveillance spotlights potential incongruencies between testing patterns and STI risk in certain populations, signaling potential under- and over-testing. These and future insights derived from HIE data may be used to continuously inform public health practice and drive further improvements in provision and evaluation of services and programs.

This Medical News article is an interview with JAMA Editor in Chief Kirsten Bibbins-Domingo and Virologist Davey Smith, head of the Division of Infectious Diseases and Global Public Health at the University of California, San Diego.