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 (AI-CBT) that automatically adapts to each Veteran's unique and changing needs. 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 contacts, including: (a) 15-minute contacts with a therapist, and (b) CBT clinician feedback provided via interactive voice response calls (IVR). The AI engine will learn what works best in terms of patients' personally-tailored treatment plan based on daily feedback via IVR about patients' pedometer-measured step counts as well as their CBT skill practice and physical functioning. The AI algorithm we will use is designed to be as efficient as possible, so that the system can learn what works best for a given patient based on the collective experience of other similar patients as well as the individual's own history. Outcomes will be measured at three and six months post-recruitment, and will include pain-related interference, treatment satisfaction, and treatment dropout.
During this reporting period, we have completed recruitment and enrollment activities, intervention delivery, and 3 and 6-month follow-up assessments. The projected enrollment was 278 patients and we have enrolled 278 patients into the study, with 166 randomized to the intervention group and 112 to the control group. While most of the data entry and cleaning have been completed, there is still some that remains due to in-person work restrictions put into place as a result of the COVID-19 pandemic. When staff are allowed to conduct in-person work, we plan to complete any remaining data entry and cleaning activities.
Analysis of call completion rates found that engagement in the intervention was consistently high with most identified subgroups completing more than 85% of their IVR assessment calls. Intervention IVR call completion rates were excellent across subgroups including those with high pain scores at enrollment.
Preliminary analyses indicate that the AI system was learning which characteristics of patients' history should carry the most weight in making subsequent decisions regarding the session types that it should recommend to improve Veterans outcomes while minimizing the use of scarce therapist time. Outcome analyses have been temporarily delayed because of COVID-19 restrictions, as noted above.
We hope to show that AI-CBT improves pain-related functional outcomes at least as much as VA's current evidence-based approach, and by scaling back unnecessary therapist contact, the AI-CBT approach will be significantly less resource-intensive. Secondary hypotheses are that AI-CBT will result in greater patient engagement and patient satisfaction.
External Links for this Project
Grant Number: I01HX001460-01A1
- Piette JD, Newman S, Krein SL, Marinec N, Chen J, Williams DA, Edmond SN, Driscoll M, LaChappelle KM, Kerns RD, Maly M, Kim HM, Farris KB, Higgins DM, Buta E, Heapy AA. Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial. JAMA internal medicine. 2022 Sep 1; 182(9):975-983. [view]
- Piette JD, Krein SL, Striplin D, Marinec N, Kerns RD, Farris KB, Singh S, An L, Heapy AA. Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program. JMIR research protocols. 2016 Apr 7; 5(2):e53. [view]
- Piette JD. Computer algorithm for targeting support to VA patients with chronic back pain. 2016 Sep 30. [view]
Health Systems, Musculoskeletal Disorders
Technology Development and Assessment, Treatment - Implementation
Care Management Tools, Cognitive Therapy, Healthcare Algorithms, Outcomes - Patient, Pain, Physical Activity, Technology Development