Searching for a therapist can be challenging and time-consuming. It usually requires calling around to prospective providers to learn more about their technique and training, before gauging whether they’d be a good fit. What if, instead, you could just be matched with the therapist most able to help you solve your problems, as determined by an artificial intelligence program?
Better Therapy Outcomes
That scenario is more plausible than some might think. With AI and machine learning tools that predictively interpret data culled from successful therapy sessions, researchers believe they can determine which treatment approaches work best for different combinations of symptoms. These insights can then help match a prospective client with the right therapist and type of therapy. Better outcomes are thus more likely and more achievable.
More Personalized, Optimized Treatment
When researchers tested an AI tool developed to personalize depression treatments and improve patient outcomes, their findings were promising. A machine learning algorithm was able to determine which patients needed to be fast-tracked into intensive treatment, based on data like levels of depression and anxiety upon patient intake, personality traits, social functioning capacity, employment status, and other sociodemographic markers. Instead of having to spend months experimenting with less intensive treatments, patients who needed intensive treatment for depression could get it earlier—more personalized treatment faster, and, in turn, improved outcomes.
Predicting and Preventing Suicide
Machine learning and ML predictive models are advancing our understanding about depression, suicide, and the complex relationships between risk factors and outcomes. Certain advances in AI even make it possible to identify and triage patients who are at high risk of suicide. In findings published in April 2021 in Heliyon:
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AI-enabled statistical analysis of data from self-reporting questionnaires was able to identify children who were most at risk of suicide attempts.
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A neural network model—another AI tool developed to improve suicide risk assessment—was more accurate than expert psychiatric assessments at identifying and triaging patients who were at high risk of suicide.
Improving Patient Flow and Ensuring High Standards of Care
Research has found that AI technologies can improve the movement of patients through an inpatient facility (“patient flow”) and monitor quality of care from point of admission to discharge. These considerations are arguably more urgent and important than ever, thanks to the recent uptick in demand for mental health treatment. A 2022 report by the American Psychological Association found that 79 percent of practitioners said they had seen an increase in patients with anxiety; 66 percent said they had seen an increase in patients with depression.
Meanwhile, many of these providers also said they were maxed-out and no longer taking new patients. That shortage can in turn strain hospitals and inpatient facilities, which are often a first point of contact for those without outpatient therapy options.
The benefit of AI is that it can help an inpatient facility more efficiently accommodate these incoming patients. By analyzing data like biomarkers, lifestyle, comorbidities, and other factors, AI tools can inform smarter decisions about cost, bed occupancy, and length of stay. These same decisions do not just improve patient flow—they also enhance quality of care.
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