How Artificial Intelligence is Disrupting Behavioral Healthcare
Mental health and substance use disorders are the largest and fastest growing crisis in healthcare—yet technology has lagged dramatically in improving efficiency and quality of care. Artificial intelligence will tackle the segment of healthcare that’s evaded modernization for years.
Nearly a billion people live with some sort of mental disorder worldwide. Many of them lack proper diagnoses or accessible care. A troubling amount of clinicians struggle to integrate behavioral health services into electronic health records, or EHRs.
Patients frequently hop from provider to provider in behavioral health. Whether to address symptoms of a disorder, seeking a second opinion, changing insurance or relocating, oftentimes they must start all over, re-explaining their history to new clinicians. A significant chunk (if not all) information can get lost in transit—providers now have to start from scratch with little history of the patient’s progress and trends in their mental health.
The only way to tackle, and stem, a growing crisis is to improve the flow and tracking of information. Mental Health Technologies’ SmarTest™ is a cloud-based, HIPAA-compliant platform for screening and recurring testing for various behavioral health disorders, cognitive learning disorders, mental degenerative disorders associated with old age, addictions, chronic pain and more.
“AI-enabled tools can prevent more severe mental illness from developing by identifying higher-risk populations that lead to quicker intervention,” researchers from the Johns Hopkins Bloomberg School of Public Health and the Boston University School of Public Health wrote recently. “AI can process natural language from electronic health records to detect early cognitive impairment or child maltreatment, which can have effects on mental health across the course of one’s life.”
Early detection is key to preventing symptoms or manageable disorders from upending a patient’s day-to-day well-being and quality of life. But detection is just one place machine learning can improve mental healthcare.
Natural language processing can allow providers to more easily meet quality measurement requirements and lessen their administrative burden, according to Harold Alan Pincus, a professor at Columbia University’s College of Physicians and Surgeons, and others.
Our clinical partners who use MHT’s SmarTest™ find automatic referrals to specialists crucial in getting their patients the care they need. It also generates additional revenue through add-on CPT codes and additional ancillary services. Our platform will track and upload subsequent test results performed by referred specialists.
MHT’s SmarTest™ enables screens to be taken before appointments, allowing intake to run smoothly and providers to have a good gauge for how their patient has been doing before seeing them. High-risk answers automatically notify providers.
The next frontier for AI in healthcare is to uncover comorbid mental health and substance use conditions, a societal breakthrough. Predictive analytics through machine learning could improve doctors’ efficiency in identifying and treating disorders at a time when there’s a massive shortage of trained providers in the U.S. Technology can also help serve rural populations with less access to care, or environments cutoff due to a crisis like a natural disaster.
In our opinion, MHT’s SmarTest™ platform and AI capabilities will be, and already are, at the forefront of addressing the behavioral healthcare crisis. Just ask our partners. To learn more about how MHT and SmarTest™ work, contact us today.
Resources:
https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(21)00395-3/fulltext
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690520/#ref1
https://www.commonwealthfund.org/blog/2023/using-technology-improve-efficacy-and-equity-integrated-behavioral-health-care
https://feinstein.northwell.edu/news/the-latest/feinstein-institutes-study-warns-of-general-internal-medicine-doctors-shortage-u-s
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690520/#ref1
https://www.mhtech.com/case-studies