Artificial Intelligence (AI) to improve chronic pain care: Evidence of AI learning

Piette, John D.; Newman, Sean; Krein, Sarah L.; Marinec, Nicolle; Chen, Jenny; Williams, David A.; Edmond, Sara N.; Driscoll, Mary; LaChappelle, K.; Maly, Marianna; Kim, Hyungjin Myra; Farris, Karen B.; Kerns, Robert D.; Heapy, Alicia

doi:10.1016/j.ibmed.2022.100064

Cited by 18 publications

(17 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients received the session type recommended (ie, without substitution) 80.3% of the time. More details about AI-CBT-CP patient engagement with the intervention have been published …”

Section: Resultsmentioning

confidence: 99%

“…Secondary analysis of data from the AI-CBT-CP group suggest that the intervention increased its effectiveness as it gained experience through patient interactions. 35 Future studies should seek to maximize the experience of AI-CBT-CP and similar programs through trials with larger populations and quantify more precisely the influence of program learning on patient health status.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, the program’s effectiveness in this trial may represent a lower bound of what could be expected if it were implemented in larger samples of patients over longer time periods. Secondary analysis of data from the AI-CBT-CP group suggest that the intervention increased its effectiveness as it gained experience through patient interactions . Future studies should seek to maximize the experience of AI-CBT-CP and similar programs through trials with larger populations and quantify more precisely the influence of program learning on patient health status.…”

Section: Discussionmentioning

confidence: 99%

“…More details about AI-CBT-CP patient engagement with the intervention have been published. 35 A total of 45.8% of AI-CBT-CP sessions were delivered via IVR, 41.6% were 15-minute synchronous (live) sessions, and 12.6% were 45-minute synchronous sessions. Compared with the standard CBT-CP intervention, the use of IVR and brief therapist contacts among the AI-CBT-CP group translated into a substantial reduction in therapist time.…”

Section: Engagement In Treatment and Ivr Call Completionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

IMPORTANCECognitive 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 PARTICIPANTSThis 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 MEASURESThe 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. RESULTSThe 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 RELEVANCEThe 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.

show abstract

“…Patients received the session type recommended (ie, without substitution) 80.3% of the time. More details about AI-CBT-CP patient engagement with the intervention have been published …”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Engagement In Treatment and Ivr Call Completionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…AI can be beneficial for classifying, predicting, diagnosing, and managing pain based on personalised patterns that can be missed by subjective methods. [ 26 , 27 ]…”

Section: Pre-operative Predictors For Acute and Chronic Post-surgical...mentioning

confidence: 99%

Predicting and evaluating pain after surgery…Newer methods to the rescue?

Sharma

Shah

Sahu

et al. 2023

Indian Journal of Anaesthesia

View full text Add to dashboard Cite

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling

Ghosh,

Kim,

Chhabria

et al. 2024

Mach Learn

View full text Add to dashboard Cite

Artificial Intelligence (AI) to improve chronic pain care: Evidence of AI learning

Cited by 18 publications

References 29 publications

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

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

Predicting and evaluating pain after surgery…Newer methods to the rescue?

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling

Contact Info

Product

Resources

About