Annie B. Friedrich’s research while affiliated with Washington University in St. Louis and other places

Qualitative research data, such as data from focus groups and in-depth interviews, are increasingly made publicly available and used by secondary researchers, which promotes open science and improves research transparency. This has prompted concerns about the sensitivity of these data, participant confidentiality, data ownership, and the time burden and cost of de-identifying data. As more qualitative researchers (QRs) share sensitive data, they will need support to share responsibly. Few repositories provide qualitative data sharing (QDS) guidance, and currently researchers must manually de-identify data prior to sharing. To address these needs, our project team worked to identify and reduce ethical and practical barriers to sharing qualitative research data in health sciences research. We developed specific QDS guidelines and tools for data de-identification, depositing, and sharing. Additionally, we developed and tested Qualitative Data Sharing (QuaDS) Software to support qualitative data de-identification. We assisted 28 qualitative health science researchers in preparing and de-identifying data for deposit in a repository. Here, we describe the process of recruiting, enrolling, and assisting QRs to use the guidelines and software and report on the revisions we made to our processes and software based on feedback from QRs and curators and observations made by project team members. Through our pilot project, we demonstrate that qualitative data sharing is feasible and can be done responsibly.

In January 2023, a new NIH policy on data sharing went into effect. The policy applies to both quantitative and qualitative research (QR) data such as data from interviews or focus groups. QR data are often sensitive and difficult to deidentify, and thus have rarely been shared in the United States. Over the past 5 y, our research team has engaged stakeholders on QR data sharing, developed software to support data deidentification, produced guidance, and collaborated with the ICPSR data repository to pilot the deposit of 30 QR datasets. In this perspective article, we share important lessons learned by addressing eight clusters of questions on issues such as where, when, and what to share; how to deidentify data and support high-quality secondary use; budgeting for data sharing; and the permissions needed to share data. We also offer a brief assessment of the state of preparedness of data repositories, QR journals, and QR textbooks to support data sharing. While QR data sharing could yield important benefits to the research community, we quickly need to develop enforceable standards, expertise, and resources to support responsible QR data sharing. Absent these resources, we risk violating participant confidentiality and wasting a significant amount of time and funding on data that are not useful for either secondary use or data transparency and verification. data sharing | qualitative research | research compliance | FAIR principles | data de-identification

Objectives To characterize the prevalence and content of pediatric triage policies. Methods We surveyed and solicited policies from U.S. hospitals with pediatric intensive care units. Policies were analyzed using qualitative methods and coded by 2 investigators. Results Thirty-four of 120 institutions (28%) responded. Twenty-five (74%) were freestanding children’s hospitals and 9 (26%) were hospitals within a hospital. Nine (26%) had approved policies, 9 (26%) had draft policies, 5 (14%) were developing policies, and 7 (20%) did not have policies. Nineteen (68%) institutions shared their approved or draft policy. Eight (42%) of those policies included neonates. The polices identified 0 to 5 (median 2) factors to prioritize patients. The most common factors were short- (17, 90%) and long- (14, 74%) term predicted mortality. Pediatric scoring systems included Pediatric Logistic Organ Dysfunction-2 (12, 63%) and Score for Neonatal Acute Physiology and Perinatal Extensions-II (4, 21%). Thirteen (68%) policies described a formal algorithm. The most common tiebreakers were random/lottery (10, 71%) and life cycles (9, 64%). The majority (15, 79%) of policies specified the roles of triage team members and 13 (68%) precluded those participating in patient care from making triage decisions. Conclusions While many institutions still do not have pediatric triage policies, there appears to be a trend among those with policies to utilize a formal algorithm that focuses on short- and long-term predicted mortality and that incorporates age-appropriate scoring systems. Additional work is needed to expand access to pediatric-specific policies, to validate scoring systems, and to address health disparities.

Introduction Several evidence-informed consent practices (ECPs) have been shown to improve informed consent in clinical trials but are not routinely used. These include optimizing consent formatting, using plain language, using validated instruments to assess understanding, and involving legally authorized representatives when appropriate. We hypothesized that participants receiving an implementation science toolkit and a social media push would have increased adoption of ECPs and other outcomes. Methods We conducted a 1-year trial with clinical research professionals in the USA ( n = 1284) who have trials open to older adults or focus on Alzheimer’s disease. We randomized participants to receive information on ECPs via receiving a toolkit with a social media push (intervention) or receiving an online learning module (active control). Participants completed a baseline survey and a follow-up survey after 1 year. A subset of participants was interviewed ( n = 43). Results Participants who engaged more with the toolkit were more likely to have tried to implement an ECP during the trial than participants less engaged with the toolkit or the active control group. However, there were no significant differences in the adoption of ECPs, intention to adopt, or positive attitudes. Participants reported the toolkit and social media push were satisfactory, and participating increased their awareness of ECPs. However, they reported lacking the time needed to engage with the toolkit more fully. Conclusions Using an implementation science approach to increase the use of ECPs was only modestly successful. Data suggest that having institutional review boards recommend or require ECPs may be an effective way to increase their use.

Background: There are numerous benefits to ethics consultation services, but little is known about the reasons different professionals may or may not request an ethics consultation. Inter-professional differences in the perceived utility of ethics consultation have not previously been studied.Methods: To understand profession-specific perceived benefits of ethics consultation, we surveyed all employees at an urban tertiary children's hospital about their use of ethics committee services (n = 842).Results: Our findings suggest that nurses and physicians find ethics consultations useful for different reasons; physicians were more likely to report normative benefits, while nurses were more likely to report communicative and relational benefits.Conclusions: These findings support an open model of ethics consultation and may also help ethics committees to better understand consultation requests and remain attuned to the needs of various professional groups.

We conducted a qualitative content analysis of health science literature ( N = 100) involving qualitative interviews or focus groups. Given recent data sharing mandates, our goal was to characterize the nature of relationships between the researchers and participants to inform ethical deliberations regarding qualitative data sharing and secondary analyses. Specifically, some researchers worry that data sharing might harm relationships, while others claim that data cannot be analyzed absent meaningful relationships with participants. We found little evidence of relationship building with participants. The majority of studies involve single encounters (95%), lasting less than 60 min (59%), with less than half of authors involved in primary data collection. Our findings suggest that relationships with participants might not pose a barrier to sharing some qualitative data collected in the health sciences and speak to the feasibility in principle of secondary analyses of these data.

In recent years, increasingly advanced artificial intelligence (AI), and in particular machine learning, has shown great promise as a tool in various healthcare contexts. Yet as machine learning in medicine has become more useful and more widely adopted, concerns have arisen about the “black-box” nature of some of these AI models, or the inability to understand—and explain—the inner workings of the technology. Some critics argue that AI algorithms must be explainable to be responsibly used in the clinical encounter, while supporters of AI dismiss the importance of explainability and instead highlight the many benefits the application of this technology could have for medicine. However, this dichotomy fails to consider the particular ways in which machine learning technologies mediate relations in the clinical encounter, and in doing so, makes explainability more of a problem than it actually is. We argue that postphenomenology is a highly useful theoretical lens through which to examine black-box AI, because it helps us better understand the particular mediating effects this type of technology brings to clinical encounters and moves beyond the explainability stalemate. Using a postphenomenological approach, we argue that explainability is more of a concern for physicians than it is for patients, and that a lack of explainability does not introduce a novel concern to the physician–patient encounter. Explainability is just one feature of technological mediation and need not be the central concern on which the use of black-box AI hinges.

In this paper, we discuss the foundational values informing the Catholic perspective on decision-making for critically ill newborns and infants, particularly focusing on the prudent use of medical technologies. Although the Church has consistently affirmed the general good of advances in scientific research and medicine, the technocratic paradigm of medicine may, particularly in cases with severely ill infants, lead to decision-making conflicts and breakdowns in communication between parents and providers. By exploring two paradigm cases, we offer specific practices in which providers can engage to connect with parents and avoid common technologically mediated decision-making conflicts. By focusing on the inherent relationality of all human persons, regardless of debility, and the Christian hope in the life to come, we can make decisions in the midst of the technocratic paradigm without succumbing to it.

We describe a case of parents refusing a tracheostomy for an otherwise healthy newborn. The refusal was not honored because permitting the refusal would have violated state law, which required a child to have a qualifying condition (e.g. a terminal diagnosis, permanent unconsciousness, incurable condition with severe suffering) to remove or withhold life-sustaining treatment. However, this case strained the relationship between the parents and medical staff, who worried about sending the newborn home with a tracheostomy where she was not wanted. While many ethical issues arise in treatment refusal cases like this, we focus on the opportunity for ethicists to help the medical staff reflect on the technological alienation of the parents, which may help foster empathy, reduce moral distress, and strengthen the quality of the doctor-parent-patient triad.