Natural language processing used to extract information on housing challenges, financial stability, employment status from unstructured patient data
Information on the nonmedical factors that influence health outcomes, known as social determinants of health, is often collected at medical appointments. But this information is frequently recorded as text within the clinical notes written by physicians, nurses, social workers, and therapists.
Researchers from Regenstrief Institute and Indiana University Fairbanks School of Public Health recently published one of the first studies in which natural language processing was applied to social determinants of health. The researchers developed three new natural language processing algorithms to successfully extract information from text data related to housing challenges, financial stability and employment status from electronic health records.
“Health and well-being is not just about medical care. Mostly, it is about our behaviors, our environment, our social connections,” said Regenstrief Institute Research Scientist and Fairbanks School of Public Health faculty member Joshua Vest, PhD, who led the study. “More and more healthcare organizations are having to deal with social determinants because it is factors like financial resources, housing, and employment status that really drive costs that make people unhealthy. The challenge for health care organizations is effectively measuring and identifying patients with social risks so that they can intervene.”
“Our work helps advance the field in both application and methodology. Natural language processing has been applied to numerous conditions in the past, but this is one of the first papers that applies it to social determinants of health. We demonstrated that a relatively simplistic natural language processing approach could effectively measure social determinants instead of using of more sophisticated deep learning and neural network models. These later models are powerful but complex, difficult to implement, and require a lot of expertise, which many health systems don’t have.”
Dr. Vest added, “We purposely designed a system that could run in the background, read all the notes and create tags or indicators that says this patient’s record contains data suggesting possible concern about a social indicator related to health. Our overall goal is to measure social determinants well enough for researchers to develop risk models and for clinicians and healthcare systems to be able to use these factors – housing challenges, financial security and employment status – in routine practice to help individuals and to provide a better understanding of the overall characteristics and needs of their patient population.”
Information indicating social needs can be extracted for many types of data in an electronic medical record, including information on patient occupation, health insurance coverage, marital status, size of household, address (low versus high crime area) and frequency of address changes.
Previously, Dr. Vest and colleagues, including Regenstrief Institute Vice President for Data and Analytics Shaun Grannis, M.D., created an app they named Uppstroms, Swedish for upstream, and successfully demonstrated that it could use structured data to predict patients in need of a referral to a social service such as a nutritionist.
“Natural language processing-driven state machines to extract social factors from unstructured clinical documentation” is published in JAMIA Open.
Authors and affiliations
Katie S. Allen1,2, Dan R. Hood1, Jonathan Cummins1, Suranga Kasturi1, Eneida A. Mendonca3,4, Joshua R. Vest1,2
1Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, USA.
2Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, IUPUI, Indianapolis, Indiana, USA.
3Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA.
4Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
This work was supported by funding from the Indiana University Addictions Grand Challenge.
About Joshua Vest, PhD, MPH
In addition to his roles as Regenstrief research scientist and Fairbanks School of Public Health professor, Dr. Vest is also the director of the Center for Health Policy at the Fairbanks School of Public Health.
About Regenstrief Institute
Founded in 1969 in Indianapolis, the Regenstrief Institute is a local, national and global leader dedicated to a world where better information empowers people to end disease and realize true health. A key research partner to Indiana University, Regenstrief and its research scientists are responsible for a growing number of major healthcare innovations and studies. Examples range from the development of global health information technology standards that enable the use and interoperability of electronic health records to improving patient-physician communications, to creating models of care that inform practice and improve the lives of patients around the globe.
Sam Regenstrief, a nationally successful entrepreneur from Connersville, Indiana, founded the institute with the goal of making healthcare more efficient and accessible for everyone. His vision continues to guide the institute’s research mission.
About the IU Richard M. Fairbanks School of Public Health
Located on the IUPUI and Fort Wayne campuses, the IU Richard M. Fairbanks School of Public Health is committed to advancing the public’s health and well-being through education, innovation, and leadership. The Fairbanks School of Public Health is known for its expertise in biostatistics, epidemiology, cancer research, community health, environmental public health, global health, health policy, and health services administration.