Beyond Human Limits: Leveraging AI to Improve Psychotherapy Practices

    A growing number of researchers and startup companies are looking to integrate artificial intelligence (AI) into perhaps the most human of places in health care: the therapy room. The hope is that AI can help mitigate the time and expense it takes for humans to rate and code elements of the patient-provider interaction in a therapy session, a process that is vital for training new therapists, evaluating therapy quality, and conducting research studies and clinical trials of psychotherapy.

When Zac Imel, Ph.D., professor and director of clinical training at the University of Utah and co-founder and chief science officer at Lyssn, first considered integrating AI with psychotherapy, he began with a proof of principle study. 

    Could AI be used to rate how empathetic a therapist was in a given session—a vital component of effective therapy? To test this, Imel and his colleagues trained a machine learning model using human ratings of counselor empathy and several hundred counseling session transcripts and recordings. They found that the model could detect empathy from transcripts with a reliability similar to human raters, paving the way for more studies using machine learning to detect other therapy skills.

    In 2017, Imel and a group of other researchers founded Lyssn, a company which uses automatic speech recognition and AI to support the training and supervision of behavioral health specialists via a Health Insurance Portability and Accountability Act (HIPAA)-compliant cloud-based platform. Over 20,000 human-rated psychotherapy sessions were used to train Lyssn’s algorithms. When fed a recording or transcript of a therapy session, Lyssn’s AI automatically detects more than 54 research-backed metrics of therapy quality, such as provider empathy, active listening skills, and engagement. Lyssn can also detect elements of evidence-based practices, including motivational interviewing and cognitive behavioral therapy (CBT), and can create session summaries and clinician notes.

    Technical advancements in AI—in natural language processing and deep learning in particular—over the last five to six years have made Lyssn’s detailed analytics possible. “The revolution that’s happened in natural language processing is that we figured out ways to learn from unlabeled data and then transfer that learning to a specific problem,” says Michael Tanana, Ph.D., co-founder and chief technology officer at Lyssn. This advance means Lyssn’s AI is able to perform subtle, complicated tasks—like detecting empathy and writing clinical notes that look like they were generated by a person.

    By integrating AI into the clinic, Lyssn gives therapist trainees more opportunities to practice skills and receive immediate feedback and expands opportunities for quality monitoring of therapy. The platform allows supervisors to see detailed information about the performance of individual therapists such as how their listening skills have varied from session to session.

Lyssn is also working with state governments to help evaluate evidence-­based practices administered through federally-funded programs. “There were already mandates for measuring what was happening,” says Imel. “Instead of having to pay a motivational interviewing expert thousands of dollars to grade just a few sessions, we can get the cost per provider down quite low, and we can give them maximum visibility into the quality of services across their systems.”

References

K. Aafjes-van Doorn et al., “A scoping review of machine learning in psychotherapy research,” Psychotherapy Res., vol. 31, no. 1, pp. 92–116, Jan. 2021. Accessed: Jul. 15, 2022, doi: 10.1080/10503307.2020.1808729.

— Adamaya Pratap Singh