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.
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.
Colleagues compared the SDOH-spotting capabilities of three different AI techniques, first training them on a set of 700 patient records to teach them the kinds of words and phrases to look for, and then using them on 300 records and judging the results.
The tools only looked at the anonymized contents of emergency department and inpatient social worker notes made between 2015 and 2019 in the electronic health records of 231 patients with dementia.
