National Academies Recommendations for Transformative Change in Women's Health Research at the National Institutes of Health

Background Due to significant gaps in knowledge around women's health, Congress commissioned the National Academies of Sciences, Engineering, and Medicine (National Academies) to conduct a consensus study on funding allocation, workforce needs, and priority research areas for the National Institutes of Health (NIH). Objectives This manuscript summarizes the key points of the new National Academies report on women's health research for NIH, presents the relevance and importance for nursing research, and briefly discusses the need for increased representation of nurse scientists on National Academies panels. Methods Beginning in the Fall of 2023, a multidisciplinary panel of 17 experts was convened by National Academies to address gaps in women's health research at NIH. The committee was tasked to identify research priorities for NIH-funded research with a focus on conditions that are female specific, are more common in women, or affect women differently. In addition, the committee was asked to make recommendations on NIH training and education to strengthen the women's health research workforce; changes to NIH structural, systems, and review processes; and allocation of funding to more equitably reflect the burden of disease among women. Results The committee found that, from 2013 to 2023, only 8.8% of NIH research dollars focused on women's health research and that basic knowledge about women's physiological, hormonal fluctuations, and chromosomal differences is lacking. Data are also needed to better understand diseases that are female specific, are more common in women, or affect women differently. The committee made eight recommendations for transformative change at NIH related to women's health research. Discussion Overall, the report describes the need for transformative change at NIH to advance the science on women's health research and improve outcomes. This includes a comprehensive approach and recommendations that would double the NIH's investment in women's health research, enhance accountability, and provide rigorous oversight, prioritization, and integration of women's health research across NIH.

Applying natural language processing to understand symptoms among older adult home healthcare patients with urinary incontinence

Abstract

Introduction

Little is known about the range and frequency of symptoms among older adult home healthcare patients with urinary incontinence, as this information is predominantly contained in clinical notes. Natural language processing can uncover symptom information among older adults with urinary incontinence to promote holistic, equitable care.

Design

We conducted a secondary analysis of cross-sectional data collected between January 1, 2015, and December 31, 2017, from the largest HHC agency in the Northeastern United States. We aimed to develop and test a natural language processing algorithm to extract symptom information from clinical notes for older adults with urinary incontinence and analyze differences in symptom documentation by race or ethnicity.

Methods

Symptoms were identified through expert clinician-driven Delphi survey rounds. We developed a natural language processing algorithm for symptom identification in clinical notes, examined symptom documentation frequencies, and analyzed differences in symptom documentation by race or ethnicity using chi-squared tests and logistic regression models.

Results

In total, 39,179 home healthcare episodes containing 1,098,419 clinical notes for 29,981 distinct patients were included. Nearly 40% of the sample represented racially or ethnically minoritized groups (i.e., 18% Black, 14% Hispanic, 7% Asian/Pacific Islander, 0.3% multi-racial, and 0.2% Native American). Based on expert clinician-driven Delphi survey rounds, the following symptoms were identified: anxiety, dizziness, constipation, syncope, tachycardia, urinary frequency/urgency, urinary hesitancy/retention, and vision impairment/blurred vision. The natural language processing algorithm achieved excellent performance (average precision of 0.92). Approximately 29% of home healthcare episodes had symptom information documented. Compared to home healthcare episodes for White patients, home healthcare episodes for Asian/Pacific Islander (odds ratio = 0.74, 95% confidence interval [0.67–0.80], p < 0.001), Black (odds ratio = 0.69, 95% confidence interval [0.64–0.73], p < 0.001), and Hispanic (odds ratio = 0.91, 95% confidence interval [0.85–0.97], p < 0.01) patients were less likely to have any symptoms documented in clinical notes.

Conclusion

We found multidimensional symptoms and differences in symptom documentation among a diverse cohort of older adults with urinary incontinence, underscoring the need for comprehensive assessments by clinicians. Future research should apply natural language processing to other data sources and investigate symptom clusters to inform holistic care strategies for diverse populations.

Clinical Relevance

Knowledge of symptoms of older adult home healthcare patients with urinary incontinence can facilitate comprehensive assessments, health equity, and improved outcomes.