Since 2013, Ieso has focused on depression and generalized anxiety disorder, and used data-driven techniques—of which NLP is a core part—to boost recovery rates for those conditions dramatically. According to Ieso, its recovery rate in 2021 for depression is 62%—compared to a national average of 50%—and 73% for generalized anxiety disorder—compared to a national average of 58%.
Ieso says it has focused on anxiety and depression partly because they are two of the most common conditions. But they also respond better to CBT than others, such as obsessive compulsive disorder. It’s not yet clear how far the clinic can extend its success, but it plans to start focusing on more conditions.
In theory, using AI to monitor quality frees up clinicians to see more clients because better therapy means fewer unproductive sessions, although Ieso has not yet studied the direct impact of NLP on the efficiency of care.
“Right now, with 1,000 hours of therapy time, we can treat somewhere between 80 and 90 clients,” says Freer. “We’re trying to move that needle and ask: Can you treat 200, 300, even 400 clients with the same amount of therapy hours?”
Unlike Ieso, Lyssn does not offer therapy itself. Instead, it provides its software to other clinics and universities, in the UK and the US, for quality control and training.
In the US, Lyssn’s clients include a telehealth opioid treatment program in California that wants to monitor the quality of care being given by its providers. The company is also working with the University of Pennsylvania to set up CBT therapists across Philadelphia with its technology.
In the UK, Lyssn is working with three organizations, including Trent Psychological Therapies Service, an independent clinic, which—like Ieso—is commissioned by the NHS to provide mental-health care. Trent PTS is still trialing the software. Because the NLP model was built in the US, the clinic had to work with Lyssn to make it recognize British regional accents.
Dean Repper, Trent PTS’s clinical services director, believes that the software could help therapists standardize best practices. “You’d think therapists who have been doing it for years would get the best outcomes,” he says. “But they don’t, necessarily.” Repper compares it to driving: “When you learn to drive a car, you get taught to do a number of safe things,” he says. “But after a while you stop doing some of those safe things and maybe pick up speeding fines.”
Improving, not replacing
The point of the AI is to improve human care, not replace it. The lack of quality mental-health care is not going to be resolved by short-term quick fixes. Addressing that problem will also require reducing stigma, increasing funding, and improving education. Blackwell, in particular, dismisses many of the claims being made for AI. “There is a dangerous amount of hype,” he says.
For example, there’s been a lot of buzz about things like chatbot therapists and round-the-clock monitoring by apps—often billed as Fitbits for the mind. But most of this tech falls somewhere between “years away” and “never going to happen.”
“It’s not about well-being apps and stuff like that,” says Blackwell. “Putting an app in someone’s hand that says it’s going to treat their depression probably serves only to inoculate them against seeking help.”
One problem with making psychotherapy more evidence-based, though, is that it means asking therapists and clients to open up their private conversations. Will therapists object to having their professional performance monitored in this way?
Repper anticipates some reluctance. “This technology represents a challenge for therapists,” he says. “It’s as if they’ve got someone else in the room for the first time, transcribing everything they say.” To start with, Trent PTS is using Lyssn’s software only with trainees, who expect to be monitored. When those therapists qualify, Repper thinks, they may accept the monitoring because they are used to it. More experienced therapists may need to be convinced of its benefits.
The idea is not to use the technology as a stick but as support, says Imel, who used to be a therapist himself.He thinks many will welcome the extra information. “It’s hard to be on your own with your clients,” he says. “When all you do is sit in a private room with another person for 20 or 30 hours a week, without getting feedback from colleagues, it can be really tough to improve.”
Freer agrees. At Ieso, therapists discuss the AI-generated feedback with their supervisors. The idea is to let therapists take control of their professional development, showing them what they’re good at (things that other therapists can potentially learn from) and not so good at (areas where they might want to get some more training).
Ieso and Lyssn are just starting down this path, but there’s clear potential for learning things about therapy that are revealed only by mining sufficiently large data sets. Atkins mentions a meta-analysis published in 2018 that pulled together around 1,000 hours’ worth of therapy without the help of AI. “Lyssn processes that in a day,” he says. New studies published by both Ieso and Lyssn analyze tens of thousands of sessions.
For example, in a paper published in JAMA Psychiatry in 2019, Ieso researchers described a deep-learning NLP model that was trained to categorize utterances from therapists in more than 90,000 hours of CBT sessions with around 14,000 clients. The algorithm learned to discern whether different phrases and short sections of conversation were instances of specific types of CBT-based conversation—such as checking the client’s mood, setting and reviewing homework (where clients practice skills learned in a session), discussing methods of change, planning for the future, and so on—or talk not related to CBT, such as general chat.