Caitlin N. Dreisbach
Caitlin Dreisbach

In both research and practice, Caitlin Dreisbach, Ph.D., R.N. infuses her work with a combination of data science expertise and deep empathy. 

As a nurse, researcher, and data scientist, Dreisbach highlights the human in the data to develop “data-informed” rather than “data-driven” health care. “My view is that data are not just data—they are a reflection of real people, of real places, and real experiences,” she said. “My goal is to combine nursing and data science to alter the care delivery of clinicians at the bedside, thereby resulting in improved maternal and child health outcomes.”

Dreisbach’s primary project as a postdoctoral research scientist for the Data Science Institute (DSI) at Columbia University leverages data science to improve the information provided by electronic fetal monitors (EFMs) in labor and delivery. EFMs provide important information on maternal contractions and fetal heart rate. However, this information is just one data point, and its availability may overshadow other important metrics regarding the mother’s physical and emotional state. 

Often clinicians focus on the monitor and not the patient, Dreisbach explained, with a reductionist tendency to use fetal monitoring as a proxy for maternal health. Interpretations of EFM data can be inconsistent, and their use is associated with higher rates of Cesarean surgery, instrumental vaginal births, and maternal infection. Initially intended for use in high-risk birth scenarios, today EFMs are prevalent in the majority of labor and delivery rooms.

Dreisbach’s solution is to augment the information EFMs produce by providing clinicians with a more comprehensive picture of labor progression. She is designing a deep-learning-based fetal monitoring system that adds other important data points, such as maternal physiological data, self-reported symptoms, and nursing assessments, to information on contractions and fetal heart rate. By incorporating the mother’s experience into the process, she hopes more clinicians will recognize the interconnection between maternal and fetal health.

The coronavirus pandemic began as Dreisbach completed her Ph.D. in nursing at the University of Virginia. She worked as a clinician full-time during the Spring 2020 lockdown, and her hospital experiences during the pandemic emphasized the importance of recognizing women’s experiences in medical research and practice. “Women are understudied in research in general, and COVID-19 has had a unique impact on women, especially in pregnancy,” Dreisbach said.

She is working on a six-month biobehavioral study to examine gender and racial differences in self-reported COVID-19 symptoms. The study will analyze a range of socioeconomic and experiential data from the CovidWatcher app and qualitative interviews with Latinas to investigate the multiple factors that affect the experience of COVID-19 symptoms and outcomes. Conducted in conjunction with the Precision in Symptom Self-Management (PriSSM) Center and supported by the National Institutes of Health, the pilot study aims to advance understanding of COVID-19 and women’s health, highlighting potential symptom self-management and relief interventions, and targeting individuals who need resources the most.

Dreisbach is grateful for the access to thought leadership in data science and health care enabled by DSI and her postdoctoral advisers Suzanne Bakken, Nursing; Elizabeth Corwin, Nursing; Noemie Elhadad, Biomedical Informatics; and Dena Goffman, Obstetrics and Gynecology.

— Karina Alexanyan, Ph.D.