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Objectives To examine whether comfort with the use of ChatGPT in society differs from comfort with other uses of AI in society and to identify whether this comfort and other patient characteristics such as trust, privacy concerns, respect, and tech-savviness are associated with expected benefit of the use of ChatGPT for improving health. Materials and Methods We analyzed an original survey of U.S. adults using the NORC AmeriSpeak Panel (n = 1787). We conducted paired t-tests to assess differences in comfort with AI applications. We conducted weighted univariable regression and 2 weighted logistic regression models to identify predictors of expected benefit with and without accounting for trust in the health system. Results Comfort with the use of ChatGPT in society is relatively low and different from other, common uses of AI. Comfort was highly associated with expecting benefit. Other statistically significant factors in multivariable analysis (not including system trust) included feeling respected and low privacy concerns. Females, younger adults, and those with higher levels of education were less likely to expect benefits in models with and without system trust, which was positively associated with expecting benefits (P  = 1.6 × 10−11). Tech-savviness was not associated with the outcome. Discussion Understanding the impact of large language models (LLMs) from the patient perspective is critical to ensuring that expectations align with performance as a form of calibrated trust that acknowledges the dynamic nature of trust. Conclusion Including measures of system trust in evaluating LLMs could capture a range of issues critical for ensuring patient acceptance of this technological innovation. [ABSTRACT FROM AUTHOR]

Objectives: Health care organizations are taking a greater role in providing social care. Federally qualified health centers (FQHCs) lead these efforts, leveraging investments in technology to manage patient care and information. This study describes the challenges of meeting patient social needs and presents new opportunities for engaging in communitywide activities to address care and quality. Study Design: Sociotechnical systems theory was applied in qualitative research. Methods: Eleven of the 40 Michigan FQHCs were recruited into the study. Twenty-five semistructured interviews were conducted with practice leadership, clinicians, and staff to explore information technology and other resources used for managing social care. Analyses of the interviews were used to organize infrastructural and technological factors that emerged into key themes. Results: Interviews revealed that Michigan FQHCs participated in community-wide activities to develop value-based care practices and quality initiatives using shared data and technology. Study participants revealed that their community-level involvement facilitated improved data management, digital information sharing, and quality improvement efforts. Ultimately, however, limited housing services and inadequate behavioral health care created challenges in connecting patients with essential treatments and social supports. Conclusions: Developing information infrastructure and new technological capabilities to manage patient care and population health is necessary but insufficient. Major investments in both technology and human capital are critical to effectively provide social care and improve quality. Building on the evidence gained from FQHC partnerships and data activities offers public officials and health care leaders insight into the local resources and policy changes that are needed. [ABSTRACT FROM AUTHOR]

Telehealth can benefit older adults during COVID-19. The purpose of this study was to understand benefits and barriers to telemedicine visits for older adults from the perspectives of family caregivers. A cross-sectional, online survey was conducted across the state of Michigan with family caregivers (n = 90) who responded to open- and closed-ended questions. Perceived benefits of telemedicine included access and rapport; barriers included the appropriateness of telemedicine for certain health care needs. Telemedicine is a likely to remain beyond COVID-19 and can facilitate access to and continuity of care. There are barriers, however, that must be addressed, especially among older patients. Key words: COVID-19; caregivers; telemedicine; aging, INTRODUCTION One of the major shifts in health care delivery during the COVID-19 pandemic has been the rapid implementation of telehealth (hereon, specifically audio/video mediated synchronous health care visits) to [...]

Fewer than 10% of individuals who suffer out-of-hospital cardiac arrest (OHCA) survive with good neurologic function. Bystander CPR more than doubles the chance of survival, and telecommunicator-CPR (T-CPR) during a 9-1-1 call substantially improves the frequency of bystander CPR. We examined the barriers to initiation of T-CPR. We analyzed the 9-1-1 call audio from 65 EMS-treated OHCAs from a single US 9-1-1 dispatch center. We initially conducted a thematic analysis aimed at identifying barriers to the initiation of T-CPR. We then conducted a conversation analysis that examined the interactions between telecommunicators and bystanders during the recognition phase (i.e., consciousness and normal breathing). We identified six process themes related to barriers, including incomplete or delayed recognition assessment, delayed repositioning, communication gaps, caller emotional distress, nonessential questions and assessments, and caller refusal, hesitation, or inability to act. We identified three suboptimal outcomes related to arrest recognition and delivery of chest compressions, which are missed OHCA identification, delayed OHCA identification and treatment, and compression instructions not provided following OHCA identification. A primary theme observed during missed OHCA calls was incomplete or delayed recognition assessment and included failure to recognize descriptors indicative of agonal breathing (e.g., "snoring", "slow") or to confirm that breathing was effective in an unconscious victim. We observed that modifiable barriers identified during 9-1-1 calls where OHCA was missed, or treatment was delayed, were often related to incomplete or delayed recognition assessment. Repositioning delays were a common barrier to the initiation of chest compressions. [ABSTRACT FROM AUTHOR]

This article explores the current state and challenges of precision oncology, specifically focusing on the maturity of the field as a learning health system. The study conducted interviews with professionals involved in Molecular Tumor Boards to gather insights on the practice, tools, and barriers of precision oncology. The findings suggest that while the field is evolving, there are still obstacles such as resource-intensive infrastructure and knowledge gaps. The article also discusses the role of academic medical centers and industry in precision oncology, as well as the need for education, accessibility, and privacy considerations. Overall, the study emphasizes the importance of learning from patient experiences and developing maturity models to advance precision oncology. [Extracted from the article]

Health organizations and systems rely on increasingly sophisticated informatics infrastructure. Without anti-racist expertise, the field risks reifying and entrenching racism in information systems. We consider ways the informatics field can recognize institutional, systemic, and structural racism and propose the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms. We enumerate guiding questions for stakeholders along with a PHCRP-Informatics framework. By focusing on (1) critical self-reflection, (2) following the expertise of well-established scholars of racism, (3) centering the voices of affected individuals and communities, and (4) critically evaluating practice resulting from informatics systems, stakeholders can work to minimize the impacts of racism. Informatics, informed and guided by this proposed framework, will help realize the vision of health systems that are more fair, just, and equitable. [ABSTRACT FROM AUTHOR]

Introduction: While data repositories are well‐established in clinical and research enterprises, knowledge repositories with shareable computable biomedical knowledge (CBK) are relatively new entities to the digital health ecosystem. Trustworthy knowledge repositories are necessary for learning health systems, but the policies, standards, and practices to promote trustworthy CBK artifacts and methods to share, and safely and effectively use them are not well studied Methods: We conducted an online survey of 24 organizations in the United States known to be involved in the development or deployment of CBK. The aim of the survey was to assess the current policies and practices governing these repositories and to identify best practices. Descriptive statistics methods were applied to data from 13 responding organizations, to identify common practices and policies instantiating the TRUST principles of Transparency, Responsibility, User Focus, Sustainability, and Technology Results: All 13 respondents indicated to different degrees adherence to policies that convey TRUST. Transparency is conveyed by having policies pertaining to provenance, credentialed contributors, and provision of metadata. Repositories provide knowledge in machine‐readable formats, include implementation guidelines, and adhere to standards to convey Responsibility. Repositories report having Technology functions that enable end‐users to verify, search, and filter for knowledge products. Less common TRUST practices are User Focused procedures that enable consumers to know about user licensing requirements or query the use of knowledge artifacts. Related to Sustainability, less than a majority post describe their sustainability plans. Few organizations publicly describe whether patients play any role in their decision‐making. Conclusion: It is essential that knowledge repositories identify and apply a baseline set of criteria to lay a robust foundation for their trustworthiness leading to optimum uptake, and safe, reliable, and effective use to promote sharing of CBK. Identifying current practices suggests a set of desiderata for the CBK ecosystem in its continued evolution [ABSTRACT FROM AUTHOR]

The exponential growth of biomedical knowledge in computable formats challenges organizations to consider mobilizing artifacts in findable, accessible, interoperable, reusable, and trustable (FAIR+T) ways1. There is a growing need to apply biomedical knowledge artifacts to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations lack the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are insufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Fourth Annual MCBK public meeting, which was held virtually July 20 to July 21, 2021 and convened over 100 participants spanning diverse domains to frame and address important dimensions for mobilizing CBK. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To understand whether and how equity is considered in artificial intelligence/machine learning governance processes at academic medical centers. STUDY DESIGN: Qualitative analysis of interview data. METHODS: We created a database of academic medical centers from the full list of Association of American Medical Colleges hospital and health system members in 2022. Stratifying by census region and restricting to nonfederal and nonspecialty centers, we recruited chief medical informatics officers and similarly positioned individuals from academic medical centers across the country. We created and piloted a semistructured interview guide focused on (1) how academic medical centers govern artificial intelligence and prediction and (2) to what extent equity is considered in these processes. A total of 17 individuals representing 13 institutions across 4 census regions of the US were interviewed. RESULTS: A minority of participants reported considering inequity, racism, or bias in governance. Most participants conceptualized these issues as characteristics of a tool, using frameworks such as algorithmic bias or fairness. Fewer participants conceptualized equity beyond the technology itself and asked broader questions about its implications for patients. Disparities in health information technology resources across health systems were repeatedly identified as a threat to health equity. CONCLUSIONS: We found a lack of consistent equity consideration among academic medical centers as they develop their governance processes for predictive technologies despite considerable national attention to the ways these technologies can cause or reproduce inequities. Health systems and policy makers will need to specifically prioritize equity literacy among health system leadership, design oversight policies, and promote critical engagement with these tools and their implications to prevent the further entrenchment of inequities in digital health care. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To understand patient perceptions of specific applications of predictive models in health care. STUDY DESIGN: Original, cross-sectional national survey. METHODS: We conducted a national online survey of US adults with the National Opinion Research Center from November to December 2021. Measures of internal consistency were used to identify how patients differentiate between clinical and administrative predictive models. Multivariable logistic regressions were used to identify relationships between comfort with various types of predictive models and patient demographics, perceptions of privacy protections, and experiences in the health care system. RESULTS: A total of 1541 respondents completed the survey. After excluding observations with missing data for the variables of interest, the final analytic sample was 1488. We found that patients differentiate between clinical and administrative predictive models. Comfort with prediction of bill payment and missed appointments was especially low (21.6% and 36.6%, respectively). Comfort was higher with clinical predictive models, such as predicting stroke in an emergency (55.8%). Experiences of discrimination were significant negative predictors of comfort with administrative predictive models. Health system transparency around privacy policies was a significant positive predictor of comfort with both clinical and administrative predictive models. CONCLUSIONS: Patients are more comfortable with clinical applications of predictive models than administrative ones. Privacy protections and transparency about how health care systems protect patient data may facilitate patient comfort with these technologies. However, larger inequities and negative experiences in health care remain important for how patients perceive administrative applications of prediction. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Sharing patient health information and biospecimens can improve health outcomes and accelerate breakthroughs in medical research. But patients generally lack understanding of how their clinical data and biospecimens are used or commercialized for research. In this mixed methods project, we assessed the impact of communication materials on patient understanding, attitudes, and perceptions.Michigan Medicine patients were recruited for a survey (n = 480) or focus group (n = 33) via a web‐based research study portal. The survey assessed the impact of mode of communication about health data and biospecimen sharing (via an informational poster vs. a news article) on patient perceptions of privacy, transparency, comfort, respect, and trust. Focus groups provided in‐depth qualitative feedback on three communication materials, including a poster, FAQ webpage, and a consent form excerpt.Among survey respondents, the type of intervention (poster vs. news) made no statistically significant difference in its influence on any characteristic. However, 95% preferred that Michigan Medicine tell them about patient data and biospecimen research sharing versus hearing it from the news. Focus group participants provided additional insights, discussing values and perceptions of altruism and reciprocity, concerns about commercialization, privacy, and security; and the desire for consent, control, and transparency.Developing our understanding of patient data‐sharing practices and integrating patient preferences into health system policy, through this work and continued exploration, contributes to building infrastructure that can be used to support the development of a learning health system across hospital systems nationally. [ABSTRACT FROM AUTHOR]

OBJECTIVES: As predictive analytics are increasingly used and developed by health care systems, recognition of the threat posed by bias has grown along with concerns about how providers can make informed decisions related to predictive models. To facilitate informed decision-making around the use of these models and limit the reification of bias, this study aimed to (1) identify user requirements for informed decision-making and utilization of predictive models and (2) anticipate and reflect equity concerns in the information provided about models. STUDY DESIGN: Qualitative analysis of user-centered design (n = 46) and expert interviews (n = 10). METHODS: We conducted a user-centered design study at an academic medical center with clinicians and stakeholders to identify informational elements required for decision-making related to predictive models with a product information label prototype. We also conducted equity- focused interviews with experts to extend the user design study and anticipate the ways in which models could interact with or reflect structural inequity. RESULTS: Four key informational elements were reported as necessary for informed decision-making and confidence in the use of predictive models: information on (1) model developers and users, (2) methodology, (3) peer review and model updates, and (4) population validation. In subsequent expert interviews, equity-related concerns included the purpose or application of a model and its relationship to structural inequity. CONCLUSIONS: Health systems should provide key information about predictive models to clinicians and other users to facilitate informed decision-making about the use of these models. Implementation efforts should also expand to routinely incorporate equity considerations from inception through the model development process. [ABSTRACT FROM AUTHOR]

The far-reaching impact of the COVID-19 pandemic on Latinx communities is well-documented. This population has higher rates of COVID-19 infection and death compared with non-Latinx White Americans mainly due to long-standing problems related to Social Determinants of Health. Communication about issues such as health threats and safety measures are a vital part of public health, and need to be appropriate to the population of focus. To understand the effectiveness of public health communication to Latinx communities in Michigan during the COVID-19 pandemic, semi-structured interviews ( n = 16) and three focus groups ( n = 24 participants) were conducted virtually in 2021 across counties in Michigan deeply impacted by COVID-19: Washtenaw, Kent, Genesee, and Wayne. Participants shared some facilitating factors that supported effective health communication during the pandemic for their communities. For instance, in their experiences, religious and community leaders were especially effective in communicating information about the pandemic. They also expressed issues with English often being the only language of official communication; the need for bilingual options; and, the need for multiple channels of communication to reach as many people as possible. Participants also highlighted their concerns about inconsistent government communication and politicizing messages as a hindering factor that impacted effective health communication within their communities. Lessons from successes and failures experienced during the COVID-19 pandemic are vital to improving health outcomes for marginalized communities during public health crises. Future responses must ensure that public health communication is appropriate and effective for Latinx communities to better protect them., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: While data repositories are well-established in clinical and research enterprises, knowledge repositories with shareable computable biomedical knowledge (CBK) are relatively new entities to the digital health ecosystem. Trustworthy knowledge repositories are necessary for learning health systems, but the policies, standards, and practices to promote trustworthy CBK artifacts and methods to share, and safely and effectively use them are not well studied., Methods: We conducted an online survey of 24 organizations in the United States known to be involved in the development or deployment of CBK. The aim of the survey was to assess the current policies and practices governing these repositories and to identify best practices. Descriptive statistics methods were applied to data from 13 responding organizations, to identify common practices and policies instantiating the TRUST principles of Transparency, Responsibility, User Focus, Sustainability, and Technology., Results: All 13 respondents indicated to different degrees adherence to policies that convey TRUST. Transparency is conveyed by having policies pertaining to provenance, credentialed contributors, and provision of metadata. Repositories provide knowledge in machine-readable formats, include implementation guidelines, and adhere to standards to convey Responsibility . Repositories report having Technology functions that enable end-users to verify, search, and filter for knowledge products. Less common TRUST practices are User Focused procedures that enable consumers to know about user licensing requirements or query the use of knowledge artifacts. Related to Sustainability , less than a majority post describe their sustainability plans. Few organizations publicly describe whether patients play any role in their decision-making., Conclusion: It is essential that knowledge repositories identify and apply a baseline set of criteria to lay a robust foundation for their trustworthiness leading to optimum uptake, and safe, reliable, and effective use to promote sharing of CBK. Identifying current practices suggests a set of desiderata for the CBK ecosystem in its continued evolution., Competing Interests: The authors declare no potential conflict of interest., (© 2022 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)