Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools

NIH RePORTER · VA · I01 · · view on reporter.nih.gov ↗

Abstract

DESCRIPTION (provided by applicant): Cognitive behavioral therapy (CBT) is one of the most effective treatments for chronic low back pain. However, only half of Veterans have access to trained CBT therapists, and program expansion is costly. Moreover, VA CBT programs consist of 10 weekly hour-long sessions delivered using an approach that is out-of-sync with stepped-care models designed to ensure that scarce resources are used as effectively and efficiently as possible. Data from prior CBT trials have documented substantial variation in patients' needs for extended treatment, and the characteristics of effective programs vary significantly. Some patients improve after the first few sessions while others need more extensive contact. After initially establishing a behavioral plan, still other Veterans may be able to reach behavioral and symptom goals using a personalized combination of manuals, shorter follow-up contacts with a therapist, and automated telephone monitoring and self-care support calls. In partnership with the National Pain Management Program, we propose to apply state-of-the-art principles from "reinforcement learning" (a field of artificial intelligence or AI used successfully in robotics and on-line consumer targeting) to develop an evidence-based, personalized CBT pain management service that automatically adapts to each Veteran's unique and changing needs (AI- CBT). AI-CBT will use feedback from patients about their progress in pain-related functioning measured daily via pedometer step-counts to automatically personalize the intensity and type of patient support; thereby ensuring that scarce therapist resources are used as efficiently as possible and potentially allowing programs with fixed budgets to serve many more Veterans. The specific aims of the study are to: (1) demonstrate that AI-CBT has non-inferior pain-related outcomes compared to standard telephone CBT; (2) document that AI-CBT achieves these outcomes with more efficient use of scarce clinician resources as evidenced by less overall therapist time and no increase in the use of other VA health services; and (3) demonstrate the intervention's impact on proximal outcomes associated with treatment response, including program engagement, pain management skill acquisition, satisfaction with care, and patients' likelihood of dropout. We will use qualitative interviews with patients, clinicians, and VA operational partners to ensure that the service has features that maximize scalability, broad scale adoption, and impact. 278 patients with chronic low back pain will be recruited from the VA Connecticut Healthcare System and the VA Ann Arbor Healthcare System, and randomized to standard 10-sessions of telephone CBT versus AI-CBT. All patients will begin with weekly hour-long telephone counseling, but for patients in the AI-CBT group, those who demonstrate a significant treatment response will be stepped down through less resource-intensive alternatives to hour-long conta...

Key facts

NIH application ID
10181034
Project number
5I01HX001460-05
Recipient
VETERANS HEALTH ADMINISTRATION
Principal Investigator
Alicia Heapy
Activity code
I01
Funding institute
VA
Fiscal year
2020
Award amount
Award type
5
Project period
2015-07-01 → 2019-12-31