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Machine Learning/AI Can ID People Who Need Specialist Care for Depression

Researchers have created decision models to predict which patients might need more treatment for their depression than what their primary care provider can offer. Scientists say the algorithms are specifically designed to provide information the clinician can act on and fit into existing clinical workflows.

Experts note that depression is the most commonly occurring mental illness in the world. The World Health Organization estimates that it affects about 350 million people. The illness may vary in intensity ranging from a relatively mild mood disorder to advanced or severe depression.

Some people may be able to manage their depression on their own or with guidance from a primary care provider. However, others may have more severe depression that requires advanced care from mental health care providers.

Scientists at Regenstrief Institute and Indiana University created algorithms to mine the electronic health record and identify individuals who would benefit from advanced care. The information system then provides primary care providers a notice so that they can refer the the individual to appropriate mental health specialists.

“Our goal was to build reproducible models that fit into clinical workflows,” said Suranga N. Kasthurirathne, Ph.D., first author of the paper and research scientist at Regenstrief Institute.

“This algorithm is unique because it provides actionable information to clinicians, helping them to identify which patients may be more at risk for adverse events from depression.”

The algorithms combined a wide variety of behavioral and clinical information from the Indiana Network for Patient Care, a statewide health information exchange. Dr. Kasthurirathne and his team developed algorithms for the entire patient population, as well as several different high-risk groups.

“By creating models for different patient populations, we offer health system leaders the option of selecting the best screening approach for their needs,” said  Kasthurirathne.

“Perhaps they don’t have the computational or human resources to run models on every single patient. This gives them the option to screen select high-risk patients.”

“Primary care doctors often have limited time, and identifying patients with more severe forms of depression can be challenging and time consuming. Our model helps them help their patients more efficiently and improve quality of care simultaneously,” said Shaun Grannis, M.D., M.S., a co-author.

“Our approach is also well-suited to leverage increasing health information technology adoption and interoperability to enable preventive care and improve access to wraparound health services,” said Grannis.

The study appears in the Journal of Medical Internet Research.

Source: Regenstrief Institute/EurekAlert

Machine Learning/AI Can ID People Who Need Specialist Care for Depression

Rick Nauert PhD

Rick Nauert, PhDDr. Rick Nauert has over 25 years experience in clinical, administrative and academic healthcare. He is currently an associate professor for Rocky Mountain University of Health Professionals doctoral program in health promotion and wellness. Dr. Nauert began his career as a clinical physical therapist and served as a regional manager for a publicly traded multidisciplinary rehabilitation agency for 12 years. He has masters degrees in health-fitness management and healthcare administration and a doctoral degree from The University of Texas at Austin focused on health care informatics, health administration, health education and health policy. His research efforts included the area of telehealth with a specialty in disease management.

APA Reference
Nauert PhD, R. (2019). Machine Learning/AI Can ID People Who Need Specialist Care for Depression. Psych Central. Retrieved on September 21, 2019, from https://psychcentral.com/news/2019/08/21/machine-learning-ai-can-id-people-who-need-specialist-care-for-depression/149632.html
Scientifically Reviewed
Last updated: 21 Aug 2019
Last reviewed: By a member of our scientific advisory board on 21 Aug 2019
Published on Psych Central.com. All rights reserved.