Can AI help hospitals spot patients in need of extra non-medical assistance?
In the rush to harness artificial intelligence and machine learning tools to make care more efficient at hospitals nationwide, a new study points to another possible use: identifying patients with non-medical needs that could affect their health and ability to receive care.
These social determinants of health — everything from transportation and housing to food supply and availability of family and friends as supports — can play a major role in a patient’s health and use of health care services.
The new study focuses on a patient population with especially complex needs: people with Alzheimer’s disease or other forms of dementia. Their condition can make them especially reliant on others to get them to medical appointments and social activities, handle medications and finances, shop and prepare food, and more.
The results of the study show that a rule-based natural language processing tool successfully identified patients with unstable access to transportation, food insecurity, social isolation, financial problems and signs of abuse, neglect, or exploitation.
The researchers found that a rule-based NLP tool — a kind of AI that analyzes human speech or writing — was far superior to deep learning and regularized logistic regression algorithms for identifying patients’ social determinants of health.
However, even the NLP tool did not do well enough at identifying needs related to housing or affording or taking medication.
The study was led by Elham Mahmoudi, Ph.D., a health economist at Michigan Medicine, the University of Michigan’s academic medical center, and Wenbo Wu, Ph.D., who completed the work while earning a doctorate at the U-M School of Public Health and is now at New York University. Mahmoudi and two other authors are in the Department of Family Medicine. More