Depression is the world’s most common mental illness. It is estimated that the World Health Organization impacts about 350 million individuals. Some individuals can handle their depression on their own or with a primary care provider’s assistance. Others, however, may have more severe depression requiring advanced care from suppliers of mental health care.
Researchers from Regenstrief and IU developed algorithms to recognize those patients so that physicians and suppliers of primary care can refer them to experts in mental health.
The objective was to construct reproducible models that match into clinical workflows according to Suranga N. Kasthurirathne, PhD, Regenstrief Institute’s first document author and research scientist. “This algorithm is special because it offers clinicians with actionable data to help them define which people may be more at danger for worse events from depression.
The algorithms mixed a broad range of cognitive and clinical data for clients in Eskenazi Health from the Indiana Network for Patient Care, a nationwide exchange of health information. Dr. Kasthurirathne and his group created algorithms for the full population of patients as well as various high-risk organizations
Dr. Kasthurirathne commented that they give health system leaders the choice to select the finest testing strategy to their requirements by developing models for distinct patient communities. Maybe they don’t have the computing or human resources to operate simulations on every single patient, which provides them the choice of screening high-risk individuals. Dr. Kasthurirathne is also works at IUPUI’s Richard M. Fairbanks School of Public Health, Indiana University as a visiting research assistant professor.
Primary care physicians often have restricted time, and it can be difficult and time-consuming to identify patients with more serious types of depression. At the same time, Shaun Grannis, M.D., M.S., co-author on document and director of the Regenstrief Institute Clem McDonald Center for Biomedical Informatics said our model enables them to assist their clients more effectively and enhance quality of care. Our strategy is also well adapted to enhancing the implementation and interoperability of health information technology to allow preventive care and enhance access to wraparound health facilities.
Dr. Grannis is Professor of Biomedical Informatics at Indiana University School of Medicine at Clem McDonald.