Respecting Autonomy And Enabling Diversity: The Effect Of Eligibility And Enrollment On Research Data Demographics

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

What Should ChatGPT Mean for Bioethics?

In the last several months, several major disciplines have started their initial reckoning with what ChatGPT and other Large Language Models (LLMs) mean for them - law, medicine, business among other professions. With a heavy dose of humility, given how fast the technology is moving and how uncertain its social implications are, this article attempts to give some early tentative thoughts on what ChatGPT might mean for bioethics. I will first argue that many bioethics issues raised by ChatGPT are similar to those raised by current medical AI - built into devices, decision support tools, data analytics, etc. These include issues of data ownership, consent for data use, data representativeness and bias, and privacy. I describe how these familiar issues appear somewhat differently in the ChatGPT context, but much of the existing bioethical thinking on these issues provides a strong starting point. There are, however, a few "new-ish" issues I highlight - by new-ish I mean issues that while perhaps not truly new seem much more important for it than other forms of medical AI. These include issues about informed consent and the right to know we are dealing with an AI, the problem of medical deepfakes, the risk of oligopoly and inequitable access related to foundational models, environmental effects, and on the positive side opportunities for the democratization of knowledge and empowering patients. I also discuss how races towards dominance (between large companies and between the U.S. and geopolitical rivals like China) risk sidelining ethics.

Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association

The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow. Along with these advances come concerns about the sensitive nature of some health data, equity considerations about the involvement of historically excluded communities, and the complex intersection of laws attempting to govern behavior. Data-sharing principles are therefore necessary across a wide swath of entities, including parties who collect health information, funders, researchers, patients, legislatures, commercial companies, and regulatory departments and agencies. This policy statement outlines some of the key equity and legal background relevant to health data sharing and responsible management. It then articulates principles that will guide the American Heart Association's engagement in public policy related to data collection, sharing, and use to continue to inform its work across the research enterprise, as well as specific examples of how these principles might be applied in the policy landscape. The goal of these principles is to improve policy to support the use or reuse of health information in ways that are respectful of patients and research participants, equitable in impact in terms of both risks and potential benefits, and beneficial across broad and demographically diverse communities in the United States.

Assessing Diversity in Newborn Genomic Sequencing Research Recruitment: Race/Ethnicity and Primary Spoken Language Variation in Eligibility, Enrollment, and Reasons for Declining

PURPOSE

Diagnostic genomic research has the potential to directly benefit participants. This study sought to identify barriers to equitable enrollment of acutely ill newborns into a diagnostic genomic sequencing research study.

METHODS

We reviewed the 16-month recruitment process of a diagnostic genomic research study enrolling newborns admitted to the neonatal intensive care unit at a regional pediatric hospital that primarily serves English- and Spanish-speaking families. Differences in eligibility, enrollment, and reasons for not enrolling were examined as functions of race/ethnicity and primary spoken language.

FINDINGS

Of the 1248 newborns admitted to the neonatal intensive care unit, 46% (n = 580) were eligible, and 17% (n = 213) were enrolled. Of the 16 languages represented among the newborns' families, 4 (25%) had translated consent documents. Speaking a language other than English or Spanish increased a newborn's likelihood of being ineligible by 5.9 times (P < 0.001) after controlling for race/ethnicity. The main reason for ineligibility was documented as the clinical team declined having their patient recruited (41% [51 of 125]). This reason significantly affected families who spoke languages other than English or Spanish and was able to be remediated with training of the research staff. Stress (20% [18 of 90]) and the study intervention(s) (20% [18 of 90]) were the main reasons given for not enrolling.

IMPLICATIONS

This analysis of eligibility, enrollment, and reasons for not enrolling in a diagnostic genomic research study found that recruitment generally did not differ as a function of a newborn's race/ethnicity. However, differences were observed depending on the parent's primary spoken language. Regular monitoring and training can improve equitable enrollment into diagnostic genomic research. There are also opportunities at the federal level to improve access to those with limited English proficiency and thus decrease disparities in representation in research participation.

Identifying Persistent Opioid Use After Surgery: The Reliability of Pharmacy Dispensation Databases

OBJECTIVE

The present study assessed concordance in perioperative opioid fulfillment data between Michigan's prescription drug monitoring program (PDMP) and a national pharmacy prescription database.

BACKGROUND

PDMPs and pharmacy dispensation databases are widely utilized, yet no research has compared their opioid fulfilment data postoperatively.

METHODS

This retrospective study included participants (N=19,823) from 2 registry studies at Michigan Medicine between July 1, 2016, and February 7, 2019. We assessed the concordance of opioid prescription fulfilment between the Michigan PDMP and a national pharmacy prescription database (Surescripts). The primary outcome was concordance of opioid fill data in the 91 to 180 days after surgical discharge, a time period frequently used to define persistent opioid use. Secondary outcomes included concordance of opioid dose and number of prescriptions fulfilled. Multinomial logistic regression analysis examined concordance across key subgroups.

RESULTS

In total, 3076 participants had ≥1 opioid fulfillments 91 to 180 days after discharge, with 1489 (49%) documented in PDMP only, 243 (8%) in Surescripts only, and 1332 (43%) in both databases. Among participants with fulfillments in both databases, there were differences in the number (n=239; 18%) and dose (n=227; 17%). The PDMP database was more likely to capture fulfillment among younger and publicly insured participants, while Surescripts was more likely to capture fulfillment from counties bordering neighboring states. The prevalence of persistent opioid use was 10.7% using PDMP data, 5.5% using Surescripts data only, and 11.7% using both data resources.

CONCLUSIONS

The state PDMP appears reliable for detecting opioid fulfillment after surgery, detecting 2 times more patients with persistent opioid use compared with Surescripts.

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Biobanks of linked clinical patient histories and biological samples are an efficient strategy to generate large cohorts for modern genetics research. Biobank recruitment varies by factors such as geographic catchment and sampling strategy, which affect biobank demographics and research utility. Here, we describe the Michigan Genomics Initiative (MGI), a single-health-system biobank currently consisting of >91,000 participants recruited primarily during surgical encounters at Michigan Medicine. The surgical enrollment results in a biobank enriched for many diseases and ideally suited for a disease genetics cohort. Compared with the much larger population-based UK Biobank, MGI has higher prevalence for nearly all diagnosis-code-based phenotypes and larger absolute case counts for many phenotypes. Genome-wide association study (GWAS) results replicate known findings, thereby validating the genetic and clinical data. Our results illustrate that opportunistic biobank sampling within single health systems provides a unique and complementary resource for exploring the genetics of complex diseases.

Social determinants of health data in solid organ transplantation: National data sources and future directions

Health equity research in transplantation has largely relied on national data sources, yet the availability of social determinants of health (SDOH) data varies widely among these sources. We sought to characterize the extent to which national data sources contain SDOH data applicable to end-stage organ disease (ESOD) and transplant patients. We reviewed 10 active national data sources based in the United States. For each data source, we examined patient inclusion criteria and explored strengths and limitations regarding SDOH data, using the National Institutes of Health PhenX toolkit of SDOH as a data collection instrument. Of the 28 SDOH variables reviewed, eight-core demographic variables were included in ≥80% of the data sources, and seven variables that described elements of social status ranged between 30 and 60% inclusion. Variables regarding identity, healthcare access, and social need were poorly represented (≤20%) across the data sources, and five of these variables were included in none of the data sources. The results of our review highlight the need for improved SDOH data collection systems in ESOD and transplant patients via: enhanced inter-registry collaboration, incorporation of standardized SDOH variables into existing data sources, and transplant center and consortium-based investigation and innovation.

Exclusion cycles: Reinforcing disparities in medicine

Clinical practice, data collection, and medical AI constitute self-reinforcing and interacting cycles of exclusion.

Multiorgan microphysiological systems as tools to interrogate interorgan crosstalk and complex diseases

Metabolic and inflammatory disorders such as autoimmune and neurodegenerative diseases are increasing at alarming rates. Many of these are not tissue-specific occurrences but complex, systemic pathologies of unknown origin for which no cure exists. Such complexity obscures causal relationships among factors regulating disease progression. Emerging technologies mimicking human physiology, such as microphysiological systems (MPSs), offer new possibilities to provide clarity in systemic metabolic and inflammatory diseases. Controlled interaction of multiple MPSs and the scalability of biological complexity in MPSs, supported by continuous multiomic monitoring, might hold the key to identifying novel relationships between interorgan crosstalk, metabolism, and immunity. In this perspective, I aim to discuss the current state of modeling multiorgan physiology and evaluate current opportunities and challenges.