IntroductionTelehealth has the potential to expand access to care, though barriers including insurance coverage, technology literacy, and personal preferences have been described since the pandemic induced uptick. Sexual and gender minoritized individuals (SGMs) face unique challenges which make telehealth a particularly promising care option. Regardless of population needs, research efforts looking at experiences and opportunities following multiple years of use are missing from the literature. Given that telehealth is here to stay, this study explores telehealth experiences, barriers, and preferences of SGM individuals 4 + years after rapid uptick to inform future efforts for the community.MethodsParticipants were recruited from an observational cohort study of SGM individuals. Eligible participants consented to follow-up contact and had previously used telehealth. Semi-structured interviews (N = 21) were conducted between March and April of 2024, covering telehealth use, barriers, and future directions. Interviews were recorded, transcribed, and thematically analyzed using a directed content analysis approach informed by prior literature. Coding was completed iteratively by four authors, with themes refined through group consensus, resulting in final recommendations for improving telehealth practice and engagement among SGM populations.ResultsThe majority of participants were White (52%), cisgender men (90%) with at least some college education (90%), employed full-time (73%), and residing in the southern U.S (57%). Participants highlighted telehealth's benefits, including provider accessibility, convenience, and comfort discussing sexual health needs with affirming clinicians. They found telehealth particularly useful for medication management, mental health, and sexual health services. They also emphasized the importance of promoting telehealth as providing privacy, security, and culturally competent care, findings which underscore their utilization motivators and shed light on unique needs of the population. Participants recommended improvement opportunities, such as creating/expanding directories of LGBTQ + -friendly providers and increasing/tailoring community-targeted marketing through social media and physical spaces.Discussion and conclusionWe interviewed a diverse group of SGM-identifying telehealth users to explore their experiences with telehealth-based care. Participants expressed ongoing interest in telehealth overall, but conversations highlighted future opportunities and provided clear next steps for research, practice, and marketing. Our findings provide valuable insight for the next era of telehealth use.

The ubiquity of clinical artificial intelligence (AI) and machine learning (ML) models necessitates measures to ensure the reliability of model output over time. Previous reviews have highlighted the lack of external validation for most clinical models, but a comprehensive review assessing the current priority given to clinical model updating is lacking. The objective of this study was to analyze studies of clinical AI models based on PRISMA guidelines. Additionally, a new simple checklist/score system was developed and employed to screen the quality of published AI/ML models. The primary aim was to understand the extent to which clinical model updating is prioritized in current research. We conducted a systematic analysis of studies on clinical AI models, adhering to PRISMA guidelines. To assess the quality of the models, we introduced a new checklist/score and considered demographic composition based on ethnicity or race. This comprehensive approach aimed to provide a thorough evaluation of the current landscape of clinical AI models. A comprehensive literature search was conducted using Ovid Embase, Ovid MEDLINE, Ovid PsycINFO, Web of Science Core Collection, Scopus, and the Cochrane Library. Inclusion criteria encompassed AI and ML studies involving clinically predictive or prognostic modeling, human studies with algorithms, articles using supervised learning methods, articles using at least two predictor variables, and studies including randomized controlled trials, prospective and retrospective cohorts, case-control studies, and case-cohort studies. Studies that did not meet these inclusion criteria were excluded. This methodology ensures a thorough and systematic evaluation of clinical AI models. The results of our analysis revealed that only 9% of the reviewed 390 AI/ML studies on sampled models stated an intention or method to update their models in the future. 98% of the AI/ML models in our review were in the research phase, and only 2 % were in the production phase. Furthermore, a mere 12% reported following best practice standards for model development. Notably, 84% of the studies did not provide demographic composition based on ethnicity or race. These findings shed light on the characteristics of recent clinical models and underscore the prevalence of research phase models built on proprietary data, limiting independent verification and validation of model output. In conclusion, our review emphasizes the need for increased attention to the updating of clinical AI models, as a significant portion of studies currently lack commitment to future model updates. The low adherence to best practice standards for model development also highlights areas for improvement in the field. Furthermore, the absence of demographic information in a substantial number of studies raises concerns about the generalizability and equitable application of these models. These findings shed light on the characteristics of recent clinical models and underscore the prevalence of research phase models built on proprietary data, limiting independent verification and validation of model output is also a big concern for patient safety. Addressing these issues is crucial for advancing the reliability and inclusivity of clinical AI and ML applications.

BackgroundMobile health apps often require the collection of identifiable information. Subsequently, this places users at significant risk of privacy breaches when the data are misused or not adequately stored and secured. These issues are especially concerning for users of reproductive health apps in the United States as protection of sensitive user information is affected by shifting governmental regulations such as the overruling of Roe v Wade and varying state-level abortion laws. Limited studies have analyzed the data privacy policies of these apps and considered the safety issues associated with a lack of user transparency and protection. ObjectiveThis study aimed to evaluate popular reproductive health apps, assess their individual privacy policies, analyze federal and state data privacy laws governing these apps in the United States and the European Union (EU), and recommend best practices for users and app developers to ensure user data safety. MethodsIn total, 4 popular reproductive health apps—Clue, Flo, Period Tracker by GP Apps, and Stardust—as identified from multiple web sources were selected through convenience sampling. This selection ensured equal representation of apps based in the United States and the EU, facilitating a comparative analysis of data safety practices under differing privacy laws. A qualitative content analysis of the apps and a review of the literature on data use policies, governmental data privacy regulations, and best practices for mobile app data privacy were conducted between January 2023 and July 2023. The apps were downloaded and systematically evaluated using the Transparency, Health Content, Excellent Technical Content, Security/Privacy, Usability, Subjective (THESIS) evaluation tool to assess their privacy and security practices. ResultsThe overall privacy and security scores for the EU-based apps, Clue and Flo, were both 3.5 of 5. In contrast, the US-based apps, Period Tracker by GP Apps and Stardust, received scores of 2 and 4.5, respectively. Major concerns regarding privacy and data security primarily involved the apps’ use of IP address tracking and the involvement of third parties for advertising and marketing purposes, as well as the potential misuse of data. ConclusionsCurrently, user expectations for data privacy in reproductive health apps are not being met. Despite stricter privacy policies, particularly with state-specific adaptations, apps must be transparent about data storage and third-party sharing even if just for marketing or analytical purposes. Given the sensitivity of reproductive health data and recent state restrictions on abortion, apps should minimize data collection, exceed encryption and anonymization standards, and reduce IP address tracking to better protect users.

Motivated by the Triple Aim, US health care policy is expanding its focus from individual patient care to include population health management. Health Information Exchanges are positioned to play an important role in that expansion. The objective is to describe the evolution of the Indiana Network for Patient Care (INPC) and discuss examples of its innovations that support both population health and clinical applications. A descriptive analytical approach was used to gather information on the INPC. This included a literature review of recent systematic and scoping reviews, collection of research that used INPC data as a resource, and data abstracted by Regenstrief Data Services to understand the breadth of uses for the INPC as a data resource. Although INPC data are primarily gathered from and used in healthcare settings, their use for population health management and research has increased. By December 2023, the INPC contained nearly 25 million patients, a significant growth from 3.5 million in 2004. This growth was a result of the use of INPC data for population health surveillance, clinical applications for data, disease registries, Patient-Centered Data Homes, non-clinical population health advancements, and accountable care organization connections with Health Information Exchanges. By structuring services on the fundamental building blocks, expanding the focus to population health, and ensuring value in the services provided to the stakeholders, Health Information Exchanges are uniquely positioned to support both population health and clinical applications.

This study aims to advocate for the continued evaluation of published clinical artificial intelligence (AI) and Machine Learning (ML) studies, including the reporting of demographic information, specifically gender, racial composition, and geographic location. Models are often trained on data lacking representation across basic demographics, potentially leading to biased outputs and exacerbating health disparities. Previous research in drug and device development has demonstrated the dangers of underrepresenting women and minority populations. This study aimed to assess the extent to which published clinical AI/ML studies report demographic information, specifically gender and racial composition, in their training datasets. A systematic review was conducted in accordance with PRISMA guidelines. The databases that were used in the study include Ovid MEDLINE, Embase, PsycINFO, Scopus, Web of Science and the Cochrane Library, for clinical AI/ML studies with direct implications for patient care. Inclusion criteria required models to be clinically actionable and not pre-clinical or administrative in scope. Two independent reviewers screened and extracted data using Covidence software, with conflicts resolved by a third reviewer. Out of 390 studies included, 84% of global models did not report the racial composition of their training data, while 31% lacked gender data. US-based models performed slightly better, with 56% reporting race and 77% reporting gender. Only 16% of all models utilized publicly available, non-proprietary datasets. The low frequency of demographic disclosure and limited use of open data raise serious concerns about the transparency, generalizability and fairness of clinical AI/ML models. Standardized reporting of gender and racial composition in training data is urgently needed to ensure ethical and equitable deployment of these technologies.

Telehealth is a great tool that makes accessing healthcare easier for those incarcerated and can help with reentry into the the community. Justice impacted individuals face many hardships including adverse health outcomes which can be mitigated through access to telehealth services and providers. During the federally recognized COVID-19 pandemic the need for accessible healthcare was exacerbated and telehealth use surged. While access to telehealth should be considered a necessity, there are many challenges and barriers for justice impacted individuals to be able to utilize this service. This perspective examines aspects of accessibility, pandemic, policy, digital tools, and ethical and social considerations of telehealth in correctional facilities. Carceral facilities should continue to innovate and invest in telehealth to revolutionize healthcare delivery, and improve health outcomes for justice impacted individuals.

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.

Importance:Starting in 2018, the 'Women in American Medical Informatics Association (AMIA) Podcast' was women-focused, in 2021 the podcast was rebranded and relaunched as the "For Your Informatics Podcast" (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community. Objectives:The goals of this case report are to characterize our rebranding and expanding decisions, and to assess how they impacted our listenership and engagement to achieve the Podcast goals of increasing diversity among the Podcast team, guests, audience, and improve audience engagement. Materials and Methods:This descriptive case study is focused on the FYI Podcast team's processes to develop a revised mission, vision, and values, increase the diversity of guests, augment listenership through social media, and track the reach through the number of followers, downloads, and impressions. Results:As of December 2023, 35 FYI Podcast episodes are available with 685 social media followers, over 20 000 downloads, and nearly 145 000 impressions. In addition to introductions to informatics and loyal listeners within AMIA, the FYI Podcast episodes have been used by students as teaching material in a graduate biomedical informatics curriculum, and as introductory material for student clubs and programs. Discussion:The Podcast relaunching led to 98% of guests from underrepresented groups and growth in listenership by 329% since May 2021. Conclusion:The FYI Podcast supports AMIA's diversity mission, and gives voices to underrepresented groups, engages the clinical informatics community in critical conversations on justice, equity, diversity and inclusion, and supports education.

Although many mobile applications require the input of identifiable information, certain users are at a higher risk when the data collected is not properly stored and secured. With the changes in government, such as the overruling of Roe v. Wade just over a year ago, users in the US, specifically those of reproductive health apps, are susceptible to these risks. Limited studies have analyzed data privacy policies of reproductive health apps and considered the safety concerns associated with lack of user protection. This study aims to analyze the most commonly utilized reproductive health apps and their individual data policies, in addition to the differences between laws governing data in the United States and in the European Union. Four popular reproductive health apps, selected based on their popularity and country of origin, were downloaded for comparison of respective data use, storage, and sharing policies, demographic ratings, and data security measures. The THESIS Evaluation Tool was used to rate the privacy and security of the selected apps. A content analysis of existing data privacy laws was also conducted to determine efficiency of app use and data protection. Findings indicated that while the four reproductive health apps have privacy policies intended to provide users with assurance of data security, there are loopholes that endanger their users. These can include collection of identifiable information with and without proper anonymization or encryption, the sharing of this data with analytical and marketing third parties, and the created accessibility of user information upon request from government agencies, whether through the app itself or third-party platforms. Three out of the four apps collect identifiable information, with one limiting data collection the most. Despite the pretense of not sharing data, all four apps do provide data to third parties, whose own privacy policies vary and may not align with data protection protocols. Personal data collected by the app, even unidentifiable, may be linked to specific users and shared with third parties. While there are pending laws pertaining to data privacy, responsibilities fall onto the individual apps themselves to ensure their user data safety.

BACKGROUND:With an increase in the number of artificial intelligence (AI) and machine learning (ML) algorithms available for clinical settings, appropriate model updating and implementation of updates are imperative to ensure applicability, reproducibility, and patient safety. OBJECTIVE:The objective of this scoping review was to evaluate and assess the model-updating practices of AI and ML clinical models that are used in direct patient-provider clinical decision-making. METHODS:We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and the PRISMA-P protocol guidance in addition to a modified CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist to conduct this scoping review. A comprehensive medical literature search of databases, including Embase, MEDLINE, PsycINFO, Cochrane, Scopus, and Web of Science, was conducted to identify AI and ML algorithms that would impact clinical decision-making at the level of direct patient care. Our primary end point is the rate at which model updating is recommended by published algorithms; we will also conduct an assessment of study quality and risk of bias in all publications reviewed. In addition, we will evaluate the rate at which published algorithms include ethnic and gender demographic distribution information in their training data as a secondary end point. RESULTS:Our initial literature search yielded approximately 13,693 articles, with approximately 7810 articles to consider for full reviews among our team of 7 reviewers. We plan to complete the review process and disseminate the results by spring of 2023. CONCLUSIONS:Although AI and ML applications in health care have the potential to improve patient care by reducing errors between measurement and model output, currently there exists more hype than hope because of the lack of proper external validation of these models. We expect to find that the AI and ML model-updating methods are proxies for model applicability and generalizability on implementation. Our findings will add to the field by determining the degree to which published models meet the criteria for clinical validity, real-life implementation, and best practices to optimize model development, and in so doing, reduce the overpromise and underachievement of the contemporary model development process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):PRR1-10.2196/37685.

Objective National interoperability is an agenda that has gained momentum in health care. Although several attempts to reach national interoperability, an alerting system through interconnected network of Health Information Exchange (HIE) organizations, Patient-Centered Data Home (PCDH), has seen preliminary success. The aim was to characterize the PCDH initiative through the Indiana Health Information Exchange's participation in the Heartland Region Pilot, which includes HIEs in Indiana, Ohio, Michigan, Kentucky, and Tennessee. Materials and Methods Admission, Discharge, and Transfer (ADT) transactions were collected between December 2016 and December 2017 among the seven HIEs in the Heartland Region. ADTs were parsed and summarized. Overlap analyses and patient matching software were used to characterize the PCDH patients. R software and Microsoft Excel were used to populate descriptive statistics and visualization. Results Approximately 1.5 million ADT transactions were captured. Majority of patients were female, ages 56–75 years, and were outpatient visits. Top noted reasons for visit were labs, screening, and abdominal pain. Based on the overlap analysis, Eastern Tennessee HIE was the only HIE with no duplicate service areas. An estimated 80 percent of the records were able to be matched with other records. Discussion The high volume of exchange in the Heartland Region Pilot established that PCDH is practical and feasible to exchange data. PCDH has the posture to build better comprehensive medical histories and continuity of care in real time. Conclusion The value of the data gained extends beyond clinical practitioners to public health workforce for improved interventions, increased surveillance, and greater awareness of gaps in health for needs assessments. This existing interconnection of HIEs has an opportunity to be a sustainable path toward national interoperability.

BackgroundPatient-reported medical histories and medical consults are primary approaches to obtaining patients' medical histories in dental settings. While patient-reported medical histories are reported to have inconsistencies, sparse information exists regarding the completeness of medical providers' responses to dental providers' medical consults. This study examined records from a predoctoral dental student clinic to determine the reasons for medical consults; the medical information requested, the completeness of returned responses, and the time taken to receive answers for medical consult requests.MethodsA random sample of 240 medical consult requests for 179 distinct patients were selected from patient encounters between 1 January 2015 and 31 December 2017. Descriptive statistics and summaries were calculated to determine the reasons for the consult, the type of information requested and returned, and the time interval for each consult.ResultsThe top two reasons for medical consults were to obtain more information (46.1%) and seek medical approval to proceed with treatment (30.3%). Laboratory and diagnostic reports (56.3%), recommendations/medical clearances (39.6%), medication information (38.3%), and current medical conditions (19.2%) were the frequent requests. However, medical providers responded fewer times to dental providers' laboratory and diagnostic report requests (41.3%), recommendations/medical clearances (19.2%), and current medical conditions (13.3%). While 86% of consults were returned in 30 days and 14% were completed after 30 days.ConclusionsThe primary reasons for dental providers' medical consults are to obtain patient information and seek recommendations for dental care. Laboratory/diagnostic reports, current medical conditions, medication history, or modifications constituted the frequently requested information. Precautions for dental procedures, antibiotic prophylaxis, and contraindications included reasons to seek medical providers' recommendations. The results also highlight the challenges they experience, such as requiring multiple attempts to contact medical providers, the incompleteness of information shared, and the delays experienced in completing at least 25% of the consults.Practical ImplicationsThe study results call attention to the importance of interdisciplinary care to provide optimum dental care and the necessity to establish systems such as integrated electronic dental record-electronic health record systems and health information exchanges to improve information sharing and communication between dental and medical providers.

Given the ubiquity of electronic health records (EHR), health administrators should be formally trained on the use and evaluation of EHR data for common operational tasks and managerial decision-making. A teaching electronic medical record (tEMR) is a fully operational electronic medical record that uses de-identified electronic patient data and provides a framework for students to familiarize themselves with the data, features, and functionality of an EHR. Although purported to be of value in health administration programs, specific benefits of using a tEMR in health administration education is unknown. We sought to examine Master of Health Administration (MHA) students' perceptions of the use, challenges, and benefits of a tEMR. We analyzed qualitative data collected from a focus group session with students who were exposed to the tEMR during a semester MHA course. We also administered pre- and post-survey questions on students' self-efficacy and perceptions of the ease of use, usefulness, and intention to use health care data analysis in their future jobs. We found several MHA students valued their exposure to the tEMR, as this provided them a realistic environment to explore de-identified patient data. Scores for students' perceived ease of using healthcare data analysis in their future job significantly increased following use of the tEMR (pre-test score M=3.31, SD=0.21; post-test score M=3.71, SD=0.18; p=0.01). Student exposure and use of a tEMR may positively affect perceptions of using EHR data for strategic and managerial tasks typical of health administrators.

Developing a diverse informatics workforce broadens the research agenda and ensures the growth of innovative solutions that enable equity-centered care. The American Medical Informatics Association (AMIA) established the AMIA First Look Program in 2017 to address workforce disparities among women, including those from marginalized communities. The program exposes women to informatics, furnishes mentors, and provides career resources. In 4 years, the program has introduced 87 undergraduate women, 41% members of marginalized communities, to informatics. Participants from the 2019 and 2020 cohorts reported interest in pursuing a career in informatics increased from 57% to 86% after participation, and 86% of both years' attendees responded that they would recommend the program to others. A June 2021 LinkedIn profile review found 50% of participants working in computer science or informatics, 4% pursuing informatics graduate degrees, and 32% having completed informatics internships, suggesting AMIA First Look has the potential to increase informatics diversity.

Background Despite the popularity of maternal and infant health mobile apps, ongoing consumer engagement and sustained app use remain barriers. Few studies have examined user experiences or perceived benefits of maternal and infant health app use from consumer perspectives. Objective This study aims to assess users’ self-reported experiences with maternal and infant health apps, perceived benefits, and general feedback by analyzing publicly available user reviews on two popular app stores—Apple App Store and Google Play Store. Methods We conducted a qualitative assessment of publicly available user reviews (N=2422) sampled from 75 maternal and infant health apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. The reviews were coded and analyzed using a general inductive qualitative content analysis approach. Results The three major themes included the following: app functionality, where users discussed app features and functions; technical aspects, where users talked about technology-based aspects of an app; and app content, where users specifically focused on the app content and the information it provides. The six minor themes included the following: patterns of use, where users highlighted the frequency and type of use; social support, where users talked about receiving social support from friends, family and community of other users; app cost, where users talked about the cost of an app within the context of being cost-effective or a potential waste of money; app comparisons, where users compared one app with others available in app stores; assistance in health care, where users specifically highlighted the role of an app in offering clinical assistance; and customer care support, where users specifically talked about their interaction with the app customer care support team. Conclusions Users generally tend to value apps that are of low cost and preferably free, with high-quality content, superior features, enhanced technical aspects, and user-friendly interfaces. Users also find app developer responsiveness to be integral, as it offers them an opportunity to engage in the app development and delivery process. These findings may be beneficial for app developers in designing better apps, as no best practice guidelines currently exist for the app environment.