A new tool for evaluating health equity in academic journals; the Diversity Factor

Datawiz-IN: fostering representative innovation in health data science-outcomes from a summer research experience

The growing adoption of Artificial Intelligence (AI) across sectors highlights the importance of diverse perspectives in guiding its development and implementation. This study examines"Datawiz-IN" an educational initiative that provides data science and machine learning research experience to students from various backgrounds in biomedicine. Supported by a National Institutes of Health R25 grant from the National Library of Medicine, the program engaged cohorts of 14 students in Summer 2023 and 13 students in Summer 2024. Initial data suggest modest increases in representation, with higher participation rates of women and less prevalant students compared to typical AI research programs. Student projects addressed various aspects of biomedical data science, including disease mechanism analysis, clinical decision support systems, and health disparity investigations. While the program's limited scale and short duration constrain broad generalizations, preliminary results indicate the potential benefits of structured inclusion efforts in expanding participation in AI research and development. This case study contributes to ongoing discussions about approaches for developing more representative AI systems and research communities, though longer-term studies will be needed to assess sustained impact. The findings suggest that targeted educational initiatives may play a role in broadening participation in AI development, while acknowledging that meaningful change requires sustained, systemic efforts across multiple institutions and career stages.

Introducing the Team Card: Enhancing governance for medical Artificial Intelligence (AI) systems in the age of complexity

This paper introduces the Team Card (TC) as a protocol to address harmful biases in the development of clinical artificial intelligence (AI) systems by emphasizing the often-overlooked role of researchers' positionality. While harmful bias in medical AI, particularly in Clinical Decision Support (CDS) tools, is frequently attributed to issues of data quality, this limited framing neglects how researchers' worldviews-shaped by their training, backgrounds, and experiences-can influence AI design and deployment. These unexamined subjectivities can create epistemic limitations, amplifying biases and increasing the risk of inequitable applications in clinical settings. The TC emphasizes reflexivity-critical self-reflection-as an ethical strategy to identify and address biases stemming from the subjectivity of research teams. By systematically documenting team composition, positionality, and the steps taken to monitor and address unconscious bias, TCs establish a framework for assessing how diversity within teams impacts AI development. Studies across business, science, and organizational contexts demonstrate that diversity improves outcomes, including innovation, decision-making quality, and overall performance. However, epistemic diversity-diverse ways of thinking and problem-solving-must be actively cultivated through intentional, collaborative processes to mitigate bias effectively. By embedding epistemic diversity into research practices, TCs may enhance model performance, improve fairness and offer an empirical basis for evaluating how diversity influences bias mitigation efforts over time. This represents a critical step toward developing inclusive, ethical, and effective AI systems in clinical care. A publicly available prototype presenting our TC is accessible at https://www.teamcard.io/team/demo.

Bibliometric Analysis of Myelomeningocele Management: National Disease Burden versus Publication Volume

BACKGROUND

Myelomeningocele (MMC) disproportionately affects low-resource areas and regions without mandatory folic acid fortification. No specific literature exists on the distribution of research output regarding neurosurgical management of myelomeningocele worldwide in relation to regional disease burden. We aimed to examine the country of origin and patient population of published papers on MMC and topics related to neurosurgical management of MMC, to determine whether these were proportionate to disease burden.

METHODS

A systematic literature search was conducted on neurosurgical aspects of MMC care. The geographic distribution of neurosurgical MMC research output was examined against the national burden of disease. Bibliometric analysis quantified author and patient country affiliations stratified by World Bank income group classification and folic acid fortification status, juxtaposed with disease burden.

RESULTS

From 9692 titles, 1843 were included, representing 107,446 patients and 2650 authorship instances. High-income countries (HICs) constituted 3% of 2019's global neural tube defect (NTD) births, 74% of authorships, and 83% of patients represented. Upper-middle-income countries (UMICs) represented 9% of NTD births, 16% of authorships, and 9% of published patients. Lower-middle-income countries (LMICs) represented 55% of NTD births but only 8.6% of authorships and 7% of patients. Low-income countries (LICs) shouldered 32% of NTD births and contributed 1.3% of authorships and 1.6% of patients. Countries with mandatory folic acid fortification represented 75% of patients and 54% of authorships. Postnatal repair, hydrocephalus, and postoperative complications were the most frequently studied topics.

CONCLUSIONS

The global literature concerning neurosurgical management of myelomeningocele originates predominantly from HICs. Published experiences of myelomeningocele patients from LICs/LMICs are scarce, even though they constitute the majority of the affected population. Neurosurgeons and other health professionals must address this mismatch between disease burden and publication volume in order to inform practice, policy, and advocacy for MMC care worldwide.

The Integration of Clinical Trials With the Practice of Medicine: Repairing a House Divided

Importance

Optimal health care delivery, both now and in the future, requires a continuous loop of knowledge generation, dissemination, and uptake on how best to provide care, not just determining what interventions work but also how best to ensure they are provided to those who need them. The randomized clinical trial (RCT) is the most rigorous instrument to determine what works in health care. However, major issues with both the clinical trials enterprise and the lack of integration of clinical trials with health care delivery compromise medicine's ability to best serve society.

Observations

In most resource-rich countries, the clinical trials and health care delivery enterprises function as separate entities, with siloed goals, infrastructure, and incentives. Consequently, RCTs are often poorly relevant and responsive to the needs of patients and those responsible for care delivery. At the same time, health care delivery systems are often disengaged from clinical trials and fail to rapidly incorporate knowledge generated from RCTs into practice. Though longstanding, these issues are more pressing given the lessons learned from the COVID-19 pandemic, heightened awareness of the disproportionate impact of poor access to optimal care on vulnerable populations, and the unprecedented opportunity for improvement offered by the digital revolution in health care. Four major areas must be improved. First, especially in the US, greater clarity is required to ensure appropriate regulation and oversight of implementation science, quality improvement, embedded clinical trials, and learning health systems. Second, greater adoption is required of study designs that improve statistical and logistical efficiency and lower the burden on participants and clinicians, allowing trials to be smarter, safer, and faster. Third, RCTs could be considerably more responsive and efficient if they were better integrated with electronic health records. However, this advance first requires greater adoption of standards and processes designed to ensure health data are adequately reliable and accurate and capable of being transferred responsibly and efficiently across platforms and organizations. Fourth, tackling the problems described above requires alignment of stakeholders in the clinical trials and health care delivery enterprises through financial and nonfinancial incentives, which could be enabled by new legislation. Solutions exist for each of these problems, and there are examples of success for each, but there is a failure to implement at adequate scale.

Conclusions and Relevance

The gulf between current care and that which could be delivered has arguably never been wider. A key contributor is that the 2 limbs of knowledge generation and implementation-the clinical trials and health care delivery enterprises-operate as a house divided. Better integration of these 2 worlds is key to accelerated improvement in health care delivery.

Datawiz-IN: Summer Research Experience for Health Data Science Training

Background

Good science necessitates diverse perspectives to guide its progress. This study introduces Datawiz-IN, an educational initiative that fosters diversity and inclusion in AI skills training and research. Supported by a National Institutes of Health R25 grant from the National Library of Medicine, Datawiz-IN provided a comprehensive data science and machine learning research experience to students from underrepresented minority groups in medicine and computing.

Methods

The program evaluation triangulated quantitative and qualitative data to measure representation, innovation, and experience. Diversity gains were quantified using demographic data analysis. Computational projects were systematically reviewed for research productivity. A mixed-methods survey gauged participant perspectives on skills gained, support quality, challenges faced, and overall sentiments.

Results

The first cohort of 14 students in Summer 2023 demonstrated quantifiable increases in representation, with greater participation of women and minorities, evidencing the efficacy of proactive efforts to engage talent typically excluded from these fields. The student interns conducted innovative projects that elucidated disease mechanisms, enhanced clinical decision support systems, and analyzed health disparities.

Conclusion

By illustrating how purposeful inclusion catalyzes innovation, Datawiz-IN offers a model for developing AI systems and research that reflect true diversity. Realizing the full societal benefits of AI requires sustaining pathways for historically excluded voices to help shape the field.

Interdisciplinary collaboration in critical care alarm research: A bibliometric analysis

BACKGROUND

Alarm fatigue in nurses is a major patient safety concern in the intensive care unit. This is caused by exposure to high rates of false and non-actionable alarms. Despite decades of research, the problem persists, leading to stress, burnout, and patient harm resulting from true missed events. While engineering approaches to reduce false alarms have spurred hope, they appear to lack collaboration between nurses and engineers to produce real-world solutions. The aim of this bibliometric analysis was to examine the relevant literature to quantify the level of authorial collaboration between nurses, physicians, and engineers.

METHODS

We conducted a bibliometric analysis of articles on alarm fatigue and false alarm reduction strategies in critical care published between 2010 and 2022. Data were extracted at the article and author level. The percentages of author disciplines per publication were calculated by study design, journal subject area, and other article-level factors.

RESULTS

A total of 155 articles with 583 unique authors were identified. While 31.73 % (n = 185) of the unique authors had a nursing background, publications using an engineering study design (n = 46), e.g., model development, had a very low involvement of nursing authors (mean proportion at 1.09 %). Observational studies (n = 58) and interventional studies (n = 33) had a higher mean involvement of 52.27 % and 47.75 %, respectively. Articles published in nursing journals (n = 32) had the highest mean proportion of nursing authors (80.32 %), while those published in engineering journals (n = 46) had the lowest (9.00 %), with 6 (13.04 %) articles having one or more nurses as co-authors.

CONCLUSION

Minimal involvement of nursing expertise in alarm research utilizing engineering methodologies may be one reason for the lack of successful, real-world solutions to ameliorate alarm fatigue. Fostering a collaborative, interdisciplinary research culture can promote a common publication culture across fields and may yield sustainable implementation of technological solutions in healthcare.

Patient diversity and author representation in clinical studies supporting the Surviving Sepsis Campaign guidelines for management of sepsis and septic shock 2021: a systematic review of citations

BACKGROUND

The generalizability of the Surviving Sepsis Campaign (SSC) guidelines to various patient populations and hospital settings has been debated. A quantitative assessment of the diversity and representation in the clinical evidence supporting the guidelines would help evaluate the generalizability of the recommendations and identify strategic research goals and priorities. In this study, we evaluated the diversity of patients in the original studies, in terms of sex, race/ethnicity, and geographical location. We also assessed diversity in sex and geographical representation among study first and last authors.

METHODS

All clinical studies cited in support of the 2021 SSC adult guideline recommendations were identified. Original clinical studies were included, while editorials, reviews, non-clinical studies, and meta-analyses were excluded. For eligible studies, we recorded the proportion of male patients, percentage of each represented racial/ethnic subgroup (when available), and countries in which they were conducted. We also recorded the sex and location of the first and last authors. The World Bank classification was used to categorize countries.

RESULTS

The SSC guidelines included six sections, with 85 recommendations based on 351 clinical studies. The proportion of male patients ranged from 47 to 62%. Most studies did not report the racial/ ethnic distribution of the included patients; when they did so, most were White patients (68-77%). Most studies were conducted in high-income countries (77-99%), which included Europe/Central Asia (33-66%) and North America (36-55%). Moreover, most first/last authors were males (55-93%) and from high-income countries (77-99%).

CONCLUSIONS

To enhance the generalizability of the SCC guidelines, stakeholders should define strategies to enhance the diversity and representation in clinical studies. Though there was reasonable representation in sex among patients included in clinical studies, the evidence did not reflect diversity in the race/ethnicity and geographical locations. There was also lack of diversity among the first and last authors contributing to the evidence.