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Original Investigation
Less Is More
August 8, 2022

Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial

Author Affiliations
  • 1Ann Arbor Department of Veterans Affairs (VA) Center for Clinical Management Research, Ann Arbor, Michigan
  • 2School of Public Health, University of Michigan, Ann Arbor
  • 3Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor
  • 4Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor
  • 5Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut
  • 6Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
  • 7Department of Psychology, Yale University, New Haven, Connecticut
  • 8Department of Neurology, Yale School of Medicine, New Haven, Connecticut
  • 9Department of Clinical, Social, and Administrative Sciences, College of Pharmacy, University of Michigan, Ann Arbor
  • 10VA Boston Healthcare System, Boston, Massachusetts
  • 11Boston University School of Medicine, Boston, Massachusetts
  • 12Yale Center for Analytical Sciences, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
JAMA Intern Med. 2022;182(9):975-983. doi:10.1001/jamainternmed.2022.3178
Key Points

Question  Can a cognitive behavioral therapy intervention for chronic pain (CBT-CP) that adjusts treatment using artificial intelligence (AI-CBT-CP) based on feedback about patient progress achieve outcomes that are not inferior to standard telephone CBT-CP while reducing therapist time?

Findings  This randomized comparative effectiveness trial of AI-CBT-CP found that its outcomes were not inferior to those of 45-minute telephone therapist sessions, with less than half the therapist time. At 6 months, more patients who experienced AI-CBT-CP had clinically meaningful improvements in physical function and pain intensity.

Meaning  The findings of this randomized trial indicated that AI-CBT-CP can achieve noninferior and possibly better outcomes relative to standard CBT-CP while increasing access and reducing therapist costs.


Importance  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.

Objectives  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.

Interventions  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.

Results  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.

Trial Registration  ClinicalTrials.gov Identifier: NCT02464449

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