Summary: Cognitive behavioral therapy for pain that is supported by artificial intelligence renders the same results as guideline-recommended programs while requiring less clinician time, thus making the option more accessible for patients.
Source: University of Michigan
Cognitive behavioral therapy is an effective alternative to opioid painkillers for managing chronic pain. But getting patients to complete those programs is challenging, especially because psychotherapy often requires multiple sessions and mental health specialists are scarce.
A new study in JAMA Internal Medicine suggests that pain CBT supported by artificial intelligence renders the same results as guideline-recommended programs delivered by therapists, while requiring substantially less clinician time, making this therapy more accessible.
“Chronic pain is incredibly common: back pain, osteoarthritis, migraine headaches and more. Because of pain, people miss work, develop depression, some people drink more alcohol than is healthy, and chronic pain is one of the main drivers of the opioid epidemic,” said John Piette, a professor at the University of Michigan’s School of Public Health and senior research scientist at the Veterans Administration.
“We’re very excited about the results of this study, because we were able to demonstrate that we can achieve pain outcomes that are at least as good as standard cognitive behavioral therapy programs, and maybe even better. And we did that with less than half the therapist time as guideline-recommended approaches.”
Traditionally, CBT is delivered by a therapist in 6 to 12 weekly in-person sessions that target patients’ behaviors, help them cope mentally and assist them in regaining functioning.
“Unfortunately, many people with pain don’t have access to these programs, and multiple weekly sessions is a deal breaker for people who have competing demands like jobs and family responsibilities,” Piette said.
As a consequence, some patients look to medications to treat their symptoms or simply drop out of care before achieving benefit, he said.
Piette and colleagues recruited 278 patients with chronic back pain and randomized them into two groups. One group received standard CBT through ten 45-minute telephone sessions with a therapist. The other group received the AI-supported therapy, in which they reported their symptoms via brief, daily automated calls.
Based on how they were doing, the AI-supported program recommended a 45-minute or 15-minute therapist session or a fully automated session covering similar content but without the need for a therapist to be present.
At three months, patients’ pain intensity and pain interference were just as good with the AI-supported program, and at six months, substantially more patients in the AI-supported group had clinically important improvements in their outcomes, Piette said.
Eighty-two percent of patients in the AI-CBT group completed all 10 weeks of treatment, compared to 57% of patients who were offered 10 weeks of telephone counseling by a therapist.
“Despite receiving more weeks of treatment, the AI-supported program used less than half the therapist time, meaning that we could double the number of patients who can be treated with the same number of clinicians,” Piette said.
“This finding could have a dramatic impact on how we think about delivering psychotherapies for people with pain.”
Piette said that similar CBT approaches are used for other common problems such as depression, anxiety and post-traumatic stress. This approach could make those services much more accessible as well, despite a shortage of therapists, he said.
“Artificial intelligence can help figure out how to provide each person as much attention as they need, while ensuring that we’re not expending scarce resources with patients who don’t benefit from them,” he said.
“Not everyone needs the same amount of therapist time; some need more while other patients can achieve benefits with a lighter touch. AI can help us target those services where they can help the most.”
About this pain and AI research news
Author: Press Office
Source: University of Michigan
Contact: Press Office – University of Michigan
Image: The image is in the public domain
Original Research: Closed access.
“Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools” by John D. Piette et al. JAMA Internal Medicine
Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools
Cognitive behavioral therapy for chronic pain (CBT-CP) is a safe and effective alternative to opioid analgesics. Because CBT-CP requires multiple sessions and therapists are scarce, many patients have limited access or fail to complete treatment.
To determine if a CBT-CP program that personalizes patient treatment using reinforcement learning, a field of artificial intelligence (AI), and interactive voice response (IVR) calls is noninferior to standard telephone CBT-CP and saves therapist time.
Design, Setting, and Participants
This was a randomized noninferiority, comparative effectiveness trial including 278 patients with chronic back pain from the Department of Veterans Affairs health system (recruitment and data collection from July 11, 2017-April 9, 2020). More patients were randomized to the AI-CBT-CP group than to the control (1.4:1) to maximize the system’s ability to learn from patient interactions.
All patients received 10 weeks of CBT-CP. For the AI-CBT-CP group, patient feedback via daily IVR calls was used by the AI engine to make weekly recommendations for either a 45-minute or 15-minute therapist-delivered telephone session or an individualized IVR-delivered therapist message. Patients in the comparison group were offered 10 therapist-delivered telephone CBT-CP sessions (45 minutes/session).
Main Outcomes and Measures
The primary outcome was the Roland Morris Disability Questionnaire (RMDQ; range 0-24), measured at 3 months (primary end point) and 6 months. Secondary outcomes included pain intensity and pain interference. Consensus guidelines were used to identify clinically meaningful improvements for responder analyses (eg, a 30% improvement in RMDQ scores and pain intensity). Data analyses were performed from April 2021 to May 2022.
The study population included 278 patients (mean [SD] age, 63.9 [12.2] years; 248 [89.2%] men; 225 [81.8%] White individuals). The 3-month mean RMDQ score difference between AI-CBT-CP and standard CBT-CP was −0.72 points (95% CI, −2.06 to 0.62) and the 6-month difference was -1.24 (95% CI, -2.48 to 0); noninferiority criterion were met at both the 3- and 6-month end points (P < .001 for both). A greater proportion of patients receiving AI-CBT-CP had clinically meaningful improvements at 6 months as indicated by RMDQ (37% vs 19%; P = .01) and pain intensity scores (29% vs 17%; P = .03). There were no significant differences in secondary outcomes. Pain therapy using AI-CBT-CP required less than half of the therapist time as standard CBT-CP.
Conclusions and Relevance
The findings of this randomized comparative effectiveness trial indicated that AI-CBT-CP was noninferior to therapist-delivered telephone CBT-CP and required substantially less therapist time. Interventions like AI-CBT-CP could allow many more patients to be served effectively by CBT-CP programs using the same number of therapists.
ClinicalTrials.gov Identifier: NCT02464449