AI’s potential to diagnose and treat mental illness

Oct. 24, 2018

The U.S. faces a mental health epidemic. Nearly one in five American adults suffers from a form of mental illness. Suicide rates are at an all-time high, 115 people die daily from opioid abuse, and one in eight Americans over 12 years’ old take an antidepressant every day. The economic burden of depression alone is estimated to be at least $210 billion annually, with more than half of that cost coming from increased absenteeism and reduced productivity in the workplace.

In a crisis that has become progressively dire over the past decade, digital solutions—many with artificial intelligence (AI) at their core—offer hope for reversing the decline in our mental wellness. New tools are being developed by tech companies and universities with potent diagnostic and treatment capabilities that can be used to serve large populations at reasonable costs.

AI solutions are arriving at an opportune time. The nation is confronting a critical shortfall in psychiatrists and other mental health specialists that is exacerbating the crisis. Nearly 40% of Americans live in areas designated by the federal government as having a shortage of mental health professionals; more than 60% of U.S. counties are without a single psychiatrist within their borders. Those fortunate enough to live in areas with sufficient access to mental health services often can’t afford them because many therapists don’t accept insurance.

Instead, the countless undiagnosed suffer, or look to emergency rooms and primary care physicians for treatment. Patients with depression, for instance, see their primary care physicians more than five times on average annually, versus fewer than three times for those without depression. For this reason, even though mental health treatment appears to account for only 4% of employer health costs, it’s really linked to nearly a quarter of them.

While some may consider the digitization of mental health services impersonal, the inherent anonymity of AI turns out to be a positive in some instances. Patients, who are often embarrassed to reveal problems to a therapist they’ve never met before, let down their guard with AI-powered tools. The lower cost of AI treatments versus seeing a psychiatrist or psychologist is another plus. These advantages help AI tools ferret out the undiagnosed, speed up needed treatment, and improve the odds of positive outcomes.

Like all digitization efforts in healthcare and other industries, these new tools pose risks, especially to patient privacy. Healthcare has already become a prime target of hackers as more and more records have been digitized. But hacking claims data is one thing; getting access to each patient’s most intimate details presents a whole new type of risk—particularly when those details are linked to consumer data and social media logins. Providers must design their solutions from the outset to employ mitigation techniques such as storing minimal personally identifiable data, regularly deleting session transcripts following analysis, and encrypting data on the server itself (not just communications).

More broadly, AI’s scale can be both a blessing and a curse. With AI, one poor programming choice carries the risk of harming millions of patients. Just as in drug development, we’re going to need careful regulation to make sure that large-scale treatment protocols remain safe and effective.

But as long as appropriate safeguards are in place, there are concrete signs that AI offers a powerful diagnostic and therapeutic tool in the battle against mental illness.

Harvard Business Review has the full article

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