Barriers to the Initiation of Telecommunicator-CPR during 9-1-1 Out-of-Hospital Cardiac Arrest Calls: A Qualitative Study.

INTRODUCTION: 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. OBJECTIVE: We examined the barriers to initiation of T-CPR. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

Fifty Years of Trust Research in Health Care: A Synthetic Review.

Policy Points First, policymakers can create conditions that will facilitate public trust in health care organizations by making creating and enforcing health policies that make exploitative behavior costly. Second, policymakers can bolster the trustworthiness of health care markets and organizations by using their regulatory authority to address and mitigate harm from conflicts-of-interest and regulatory capture. Third, policymakers and government agencies can further safeguard the public's trust by being transparent and effective about their role in the provision of health services to the public. CONTEXT: Trust plays a critical role in facilitating health care delivery and calls for rebuilding trust in health care are increasingly commonplace. This article serves as a primer on the trust literature for health policymakers, organizational leaders, clinicians, and researchers based on the long history of engagement with the topic among health policy and services researchers. METHODS: We conducted a synthetic review of the health services and health policy literatures on trust since 1970. We organize our findings by trustor-trustee dyads, highlighting areas of convergence, tensions and contradictions, and methodological considerations. We close by commenting on the challenges facing the study of trust in health care, the potential value in borrowing from other disciplines, and imperatives for the future. FINDINGS: We identified 725 articles for review. Most focused on patients' trust in clinicians (n = 499), but others explored clinicians' trust in patients (n = 11), clinicians' trust in clinicians (n = 69), and clinician/patient trust in organizations (n = 19) and systems (n = 127).  Across these five subliteratures, there was lack of consensus about definitions, dimensions, and key attributes of trust. Researchers leaned heavily on cross-sectional survey designs, with limited methodological attention to the relational or contextual realities of trust. Trust has most commonly been treated as an independent variable related to attitudinal and behavioral outcomes. We suggest two challenges have limited progress for the field: (1) conceptual murkiness in terms and theories, and (2) limited observability of the phenomena. Insights from philosophy, sociology, economics, and psychology offer insights for how to advance both the theoretical and empirical study of health-related trust. CONCLUSION: Conceptual clarity and methodological creativity are critical to advancing health-related trust research. Although rigorous research in this area is challenging, the essential role of trust in population health necessitates continued grappling with the topic.

US Residents' Preferences for Sharing of Electronic Health Record and Genetic Information: A Discrete Choice Experiment.

OBJECTIVES: The aim to this study was to assess preferences for sharing of electronic health record (EHR) and genetic information separately and to examine whether there are different preferences for sharing these 2 types of information. METHODS: Using a population-based, nationally representative survey of the United States, we conducted a discrete choice experiment in which half of the subjects (N = 790) responded to questions about sharing of genetic information and the other half (N = 751) to questions about sharing of EHR information. Conditional logistic regression models assessed relative preferences across attribute levels of where patients learn about health information sharing, whether shared data are deidentified, whether data are commercialized, how long biospecimens are kept, and what the purpose of sharing the information is. RESULTS: Individuals had strong preferences to share deidentified (vs identified) data (odds ratio [OR] 3.26, 95% confidence interval 2.68-3.96) and to be able to opt out of sharing information with commercial companies (OR 4.26, 95% confidence interval 3.42-5.30). There were no significant differences regarding how long biospecimens are kept or why the data are being shared. Individuals had a stronger preference for opting out of sharing genetic (OR 4.26) versus EHR information (OR 2.64) (P = .002). CONCLUSIONS: Hospital systems and regulatory bodies should consider patient preferences for sharing of personal medical records or genetic information. For both genetic and EHR information, patients strongly prefer their data to be deidentified and to have the choice to opt out of sharing information with commercial companies.

Family Caregivers' Experiences With Telehealth During COVID-19: Insights From Michigan.

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.

Learning about COVID-19: sources of information, public trust, and contact tracing during the pandemic.

OBJECTIVE: To assess the association between public attitudes, beliefs, and information seeking about the COVID-19 pandemic and willingness to participate in contact tracing in Michigan. METHODS: Using data from the quarterly Michigan State of the State survey conducted in May 2020 (n = 1000), we conducted multiple regression analyses to identify factors associated with willingness to participate in COVID-19 contact tracing efforts. RESULTS: Perceived threat of the pandemic to personal health (B = 0.59, p = <.00, Ref = No threat) and general trust in the health system (B = 0.17, p < 0.001), were the strongest positive predictors of willingness to participate in contact tracing. Concern about misinformation was also positively associated with willingness to participate in contact tracing (B = 0.30, p < 0.001; Ref = No concern). Trust in information from public health institutions was positively associated with willingness to participate in contact tracing, although these institutions were not necessarily the main sources of information about COVID-19. CONCLUSION: Policy makers can enhance willingness to participate in public health efforts such as contact tracing during infectious disease outbreaks by helping the public appreciate the seriousness of the public health threat and communicating trustworthy information through accessible channels.

An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review.

BACKGROUND: In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care. OBJECTIVE: This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward. METHODS: We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial Knowledge Gaps and Uncertainties proposed in 2007. RESULTS: We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy. CONCLUSIONS: There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.

Improving the Informed Consent Process in Hematopoietic Cell Transplantation: Patient, Caregiver, and Provider Perspectives.

One of the significant modifications to the Common Rule is the requirement that prospective participants be given information sufficient for a "reasonable person." However, research is limited on what types of information patients, caregivers, and providers consider "key information." Although certain aspects of informed consent (IC) may be considered standard, considering the individualized needs and preferences of patients is necessary for patient-centered consent. In this study, we qualitatively examined the specific types of information that patients and caregivers involved in hematopoietic cell transplantation (HCT), as well as their providers, believe to be important and necessary as part of the IC process to make a decision about participating in clinical research; and further, how these perspectives are aligned. Our findings suggest opportunities for improving the IC document and process by emphasizing information of importance to patients, such as the benefits to others and contributions to science that are associated with participation in clinical research. Furthermore, increasing patient engagement during the IC process may allow providers to streamline information that is aligned with patient information needs and preferences.

Public preferences regarding informed consent models for participation in population-based genomic research.

PURPOSE: Some large population biobanks that house biospecimens and health information for research seek broad consent from participants, whereas others reconsent for specific new studies. Understanding research participants' attitudes and preferences about broad and narrow consent may improve recruitment, retention, and public support. METHODS: An online survey was conducted among a representative sample of 4,659 US adults to examine relationships between consent preferences and demographic factors, beliefs about privacy and the value of research, and the perceived trustworthiness of researchers. RESULTS: Participants preferred broad consent (52%) over study-by-study consent models (48%). Higher preferences for study-by-study consent observed among black non-Hispanic respondents and respondents with lower income and education were explained by differences in the prevalence of one or more beliefs about the study. Respondents with fears about research and those who would feel respected if asked for permission for each research use preferred study-by-study consent. Preference for broad consent was related to the desire not to be bothered with multiple requests and the belief that the study could lead to improved treatments, cures, and lives saved. CONCLUSION: These data suggest that support for broad consent is contingent on sufficient information about data use. Work with research participants and community leaders to understand, respond to, and influence opinions about a given, ongoing study may improve uptake of broad consent.

Equity and AI governance at academic medical centers.

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.

Public perspectives on the use of different data types for prediction in healthcare.

OBJECTIVE: Understand public comfort with the use of different data types for predictive models. MATERIALS AND METHODS: We analyzed data from a national survey of US adults (n = 1436) fielded from November to December 2021. For three categories of data (identified using factor analysis), we use descriptive statistics to capture comfort level. RESULTS: Public comfort with data use for prediction is low. For 13 of 15 data types, most respondents were uncomfortable with that data being used for prediction. In factor analysis, 15 types of data grouped into three categories based on public comfort: (1) personal characteristic data, (2) health-related data, and (3) sensitive data. Mean comfort was highest for health-related data (2.45, SD 0.84, range 1-4), followed by personal characteristic data (2.36, SD 0.94), and sensitive data (1.88, SD 0.77). Across these categories, we observe a statistically significant positive relationship between trust in health systems' use of patient information and comfort with data use for prediction. DISCUSSION: Although public trust is recognized as important for the sustainable expansion of predictive tools, current policy does not reflect public concerns. Low comfort with data use for prediction should be addressed in order to prevent potential negative impacts on trust in healthcare. CONCLUSION: Our results provide empirical evidence on public perspectives, which are important for shaping the use of predictive models. Findings demonstrate a need for realignment of policy around the sensitivity of non-clinical data categories.

How patients distinguish between clinical and administrative predictive models in health care.

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.

Public comfort with the use of ChatGPT and expectations for healthcare.

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.

Advancing maturity modeling for precision oncology.

Introduction: This study aimed to map the maturity of precision oncology as an example of a Learning Health System by understanding the current state of practice, tools and informatics, and barriers and facilitators of maturity. Methods: We conducted semi-structured interviews with 34 professionals (e.g., clinicians, pathologists, and program managers) involved in Molecular Tumor Boards (MTBs). Interviewees were recruited through outreach at 3 large academic medical centers (AMCs) (n = 16) and a Next Generation Sequencing (NGS) company (n = 18). Interviewees were asked about their roles and relationships with MTBs, processes and tools used, and institutional practices. The interviews were then coded and analyzed to understand the variation in maturity across the evolving field of precision oncology. Results: The findings provide insight into the present level of maturity in the precision oncology field, including the state of tooling and informatics within the same domain, the effects of the critical environment on overall maturity, and prospective approaches to enhance maturity of the field. We found that maturity is relatively low, but continuing to evolve, across these dimensions due to the resource-intensive and complex sociotechnical infrastructure required to advance maturity of the field and to fully close learning loops. Conclusion: Our findings advance the field by defining and contextualizing the current state of maturity and potential future strategies for advancing precision oncology, providing a framework to examine how learning health systems mature, and furthering the development of maturity models with new evidence.

Betraying, Earning, or Justifying Trust in Health Organizations.

Health care and public health programs increasingly rely on, and often even require, organizational action, which is facilitated, if not dependent on, trust. Case examples in this essay highlight trust, trustworthiness, and distrust in public and private organizations, providing insights into how trust in health-related organizations can be betrayed, earned, and justified and into the consequences of organizational trust and trustworthiness for the health of individuals and communities. These examples demonstrate the need for holistic assessments of trust in clinicians and trust in organizations and for organizations, public health, and the medical profession to address questions concerning their own trustworthiness. Normative and empirical assessments of trust and trustworthiness that capture the experiences of those treated within the walls of a health care organization, as well as the care of those outside, will contribute to more trustworthy systems of care.

A Critical Analysis of White Racial Framing and Comfort with Medical Research.

OBJECTIVE: Analyze racial differences in comfort with medical research using an alternative to the traditional approach that treats white people as a raceless norm. METHODS: Quantitative analysis of survey responses (n = 1,570) from Black and white residents of the US to identify relationships between perceptions of research as a right or a risk, and comfort participating in medical research. RESULTS: A lower proportion of white respondents reported that medical experimentation occurred without patient consent (p < 0.001) and a higher proportion of white respondents reported that it should be their right to participate in medical research (p = 0.02). Belief in one's right to participate was significantly predictive of comfort (b = 0.37, p < 0.001). Belief in experimentation without consent was significantly predictive of comfort for white respondents but not for Black respondents in multivariable analysis. CONCLUSIONS: A rights-based orientation and less concern about the risks of medical research among white respondents demonstrate comparative advantage. Efforts to diversify medical research may perpetuate structural racism if they do not (1) critically engage with whiteness and its role in comfort with participation, and (2) identify and respond specifically to the needs of Black patients.

Applying anti-racist approaches to informatics: a new lens on traditional frames.

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.

A survey of computable biomedical knowledge repositories.

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.

Policy Preferences Regarding Health Data Sharing Among Patients With Cancer: Public Deliberations.

BACKGROUND: Precision health offers the promise of advancing clinical care in data-driven, evidence-based, and personalized ways. However, complex data sharing infrastructures, for-profit (commercial) and nonprofit partnerships, and systems for data governance have been created with little attention to the values, expectations, and preferences of patients about how they want to be engaged in the sharing and use of their health information. We solicited patient opinions about institutional policy options using public deliberation methods to address this gap. OBJECTIVE: We aimed to understand the policy preferences of current and former patients with cancer regarding the sharing of health information collected in the contexts of health information exchange and commercial partnerships and to identify the values invoked and perceived risks and benefits of health data sharing considered by the participants when formulating their policy preferences. METHODS: We conducted 2 public deliberations, including predeliberation and postdeliberation surveys, with patients who had a current or former cancer diagnosis (n=61). Following informational presentations, the participants engaged in facilitated small-group deliberations to discuss and rank policy preferences related to health information sharing, such as the use of a patient portal, email or SMS text messaging, signage in health care settings, opting out of commercial data sharing, payment, and preservation of the status quo. The participants ranked their policy preferences individually, as small groups by mutual agreement, and then again individually in the postdeliberation survey. RESULTS: After deliberation, the patient portal was ranked as the most preferred policy choice. The participants ranked no change in status quo as the least preferred policy option by a wide margin. Throughout the study, the participants expressed concerns about transparency and awareness, convenience, and accessibility of information about health data sharing. Concerns about the status quo centered around a lack of transparency, awareness, and control. Specifically, the patients were not aware of how, when, or why their data were being used and wanted more transparency in these regards as well as greater control and autonomy around the use of their health data. The deliberations suggested that patient portals would be a good place to provide additional information about data sharing practices but that over time, notifications should be tailored to patient preferences. CONCLUSIONS: Our study suggests the need for increased disclosure of health information sharing practices. Describing health data sharing practices through patient portals or other mechanisms personalized to patient preferences would minimize the concerns expressed by patients about the extent of data sharing that occurs without their knowledge. Future research and policies should identify ways to increase patient control over health data sharing without reducing the societal benefits of data sharing.