Leveraging electronic health record data for endometriosis research

Frontiers in Digital Health

Digital Health

Nadia Penrod¹  Kurt Barnhart²  Suneeta Senapati²  Digna R. Velez Edwards³  Shefali S. Verma⁴  Chelsea Okeh⁴ 

[1] College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States;Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States;Department of Obstetrics and Gynecology, Vanderbilt University, Nashville, TN, United States;Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, United States;

关键词: reproductive health;    women’s health;    electronic health records—EHR;    endometriosis;    obstetric & gynecologic;   

DOI  :  10.3389/fdgth.2023.1150687

 received in 2023-01-24, accepted in 2023-05-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Endometriosis is a chronic, complex disease for which there are vast disparities in diagnosis and treatment between sociodemographic groups. Clinical presentation of endometriosis can vary from asymptomatic disease—often identified during (in)fertility consultations—to dysmenorrhea and debilitating pelvic pain. Because of this complexity, delayed diagnosis (mean time to diagnosis is 1.7–3.6 years) and misdiagnosis is common. Early and accurate diagnosis of endometriosis remains a research priority for patient advocates and healthcare providers. Electronic health records (EHRs) have been widely adopted as a data source in biomedical research. However, they remain a largely untapped source of data for endometriosis research. EHRs capture diverse, real-world patient populations and care trajectories and can be used to learn patterns of underlying risk factors for endometriosis which, in turn, can be used to inform screening guidelines to help clinicians efficiently and effectively recognize and diagnose the disease in all patient populations reducing inequities in care. Here, we provide an overview of the advantages and limitations of using EHR data to study endometriosis. We describe the prevalence of endometriosis observed in diverse populations from multiple healthcare institutions, examples of variables that can be extracted from EHRs to enhance the accuracy of endometriosis prediction, and opportunities to leverage longitudinal EHR data to improve our understanding of long-term health consequences for all patients.

【 授权许可】

Unknown   
© 2023 Penrod, Okeh, Velez Edwards, Barnhart, Senapati and Verma.