Design, Implementation, and Clinical Impact of a Machine Learning–Assisted Intervention Bundle to Improve Opioid Prescribing

Marwaha, Jayson S.; Beaulieu‐Jones, Brendin R.; Kennedy, Chris; Nathanson, Larry; Robinson, Kortney; Tandon, Manu; Brat, Gabriel

doi:10.1056/cat.21.0477

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Moreover, PowerED did not have available objective indicators of patient status, including information about patients’ medication use, overdoses, or unplanned ED visits. Future studies should consider using alternative strategies to improve the information with which adaptive programs such as PowerED make decisions, including the use of potentially more user-friendly text message monitoring [ 37 ] and direct access to clinical records. Finally, it would be valuable to evaluate the performance of this intervention in a larger sample of patients with high OA-related risk, particularly among patients from more racially and ethnically diverse communities.…”

Section: Discussionmentioning

confidence: 99%

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures

Piette¹,

Thomas²,

Newman³

et al. 2023

J Med Internet Res

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Background Some patients prescribed opioid analgesic (OA) medications for pain experience serious side effects, including dependence, sedation, and overdose. As most patients are at low risk for OA-related harms, risk reduction interventions requiring multiple counseling sessions are impractical on a large scale. Objective This study evaluates whether an intervention based on reinforcement learning (RL), a field of artificial intelligence, learned through experience to personalize interactions with patients with pain discharged from the emergency department (ED) and decreased self-reported OA misuse behaviors while conserving counselors’ time. Methods We used data representing 2439 weekly interactions between a digital health intervention (“Prescription Opioid Wellness and Engagement Research in the ED” [PowerED]) and 228 patients with pain discharged from 2 EDs who reported recent opioid misuse. During each patient’s 12 weeks of intervention, PowerED used RL to select from 3 treatment options: a brief motivational message delivered via an interactive voice response (IVR) call, a longer motivational IVR call, or a live call from a counselor. The algorithm selected session types for each patient each week, with the goal of minimizing OA risk, defined in terms of a dynamic score reflecting patient reports during IVR monitoring calls. When a live counseling call was predicted to have a similar impact on future risk as an IVR message, the algorithm favored IVR to conserve counselor time. We used logit models to estimate changes in the relative frequency of each session type as PowerED gained experience. Poisson regression was used to examine the changes in self-reported OA risk scores over calendar time, controlling for the ordinal session number (1st to 12th). Results Participants on average were 40 (SD 12.7) years of age; 66.7% (152/228) were women and 51.3% (117/228) were unemployed. Most participants (175/228, 76.8%) reported chronic pain, and 46.2% (104/225) had moderate to severe depressive symptoms. As PowerED gained experience through interactions over a period of 142 weeks, it delivered fewer live counseling sessions than brief IVR sessions (P=.006) and extended IVR sessions (P<.001). Live counseling sessions were selected 33.5% of the time in the first 5 weeks of interactions (95% CI 27.4%-39.7%) but only for 16.4% of sessions (95% CI 12.7%-20%) after 125 weeks. Controlling for each patient’s changes during the course of treatment, this adaptation of treatment-type allocation led to progressively greater improvements in self-reported OA risk scores (P<.001) over calendar time, as measured by the number of weeks since enrollment began. Improvement in risk behaviors over time was especially pronounced among patients with the highest risk at baseline (P=.02). Conclusions The RL-supported program learned which treatment modalities worked best to improve self-reported OA risk behaviors while conserving counselors’ time. RL-supported interventions represent a scalable solution for patients with pain receiving OA prescriptions. Trial Registration Clinicaltrials.gov NCT02990377; https://classic.clinicaltrials.gov/ct2/show/NCT02990377

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Section: Discussionmentioning

confidence: 99%

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures

Piette¹,

Thomas²,

Newman³

et al. 2023

J Med Internet Res

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The role of artificial intelligence in helping providers manage pain and opioid use after surgery

Wang,

Beaulieu-Jones,

Brat

et al. 2024

Global Surg Educ

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An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures (Preprint)

Piette¹,

Thomas²,

Newman³

et al. 2022

Preprint

View full text Add to dashboard Cite

BACKGROUND Some patients prescribed opioid analgesic (OA) medications for pain experience serious side effects, including dependence, sedation, and overdose. As most patients are at low risk for OA-related harms, risk reduction interventions requiring multiple counseling sessions are impractical on a large scale. OBJECTIVE This study evaluates whether an intervention based on reinforcement learning (RL), a field of artificial intelligence, learned through experience to personalize interactions with patients with pain discharged from the emergency department (ED) and decreased self-reported OA misuse behaviors while conserving counselors’ time. METHODS We used data representing 2439 weekly interactions between a digital health intervention (“Prescription Opioid Wellness and Engagement Research in the ED” [PowerED]) and 228 patients with pain discharged from 2 EDs who reported recent opioid misuse. During each patient’s 12 weeks of intervention, PowerED used RL to select from 3 treatment options: a brief motivational message delivered via an interactive voice response (IVR) call, a longer motivational IVR call, or a live call from a counselor. The algorithm selected session types for each patient each week, with the goal of minimizing OA risk, defined in terms of a dynamic score reflecting patient reports during IVR monitoring calls. When a live counseling call was predicted to have a similar impact on future risk as an IVR message, the algorithm favored IVR to conserve counselor time. We used logit models to estimate changes in the relative frequency of each session type as PowerED gained experience. Poisson regression was used to examine the changes in self-reported OA risk scores over calendar time, controlling for the ordinal session number (1st to 12th). RESULTS Participants on average were 40 (SD 12.7) years of age; 66.7% (152/228) were women and 51.3% (117/228) were unemployed. Most participants (175/228, 76.8%) reported chronic pain, and 46.2% (104/225) had moderate to severe depressive symptoms. As PowerED gained experience through interactions over a period of 142 weeks, it delivered fewer live counseling sessions than brief IVR sessions (P=.006) and extended IVR sessions (P<.001). Live counseling sessions were selected 33.5% of the time in the first 5 weeks of interactions (95% CI 27.4%-39.7%) but only for 16.4% of sessions (95% CI 12.7%-20%) after 125 weeks. Controlling for each patient’s changes during the course of treatment, this adaptation of treatment-type allocation led to progressively greater improvements in self-reported OA risk scores (P<.001) over calendar time, as measured by the number of weeks since enrollment began. Improvement in risk behaviors over time was especially pronounced among patients with the highest risk at baseline (P=.02). CONCLUSIONS The RL-supported program learned which treatment modalities worked best to improve self-reported OA risk behaviors while conserving counselors’ time. RL-supported interventions represent a scalable solution for patients with pain receiving OA prescriptions. CLINICALTRIAL Clinicaltrials.gov NCT02990377; https://classic.clinicaltrials.gov/ct2/show/NCT02990377

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Design, Implementation, and Clinical Impact of a Machine Learning–Assisted Intervention Bundle to Improve Opioid Prescribing

Cited by 3 publications

References 17 publications

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures

The role of artificial intelligence in helping providers manage pain and opioid use after surgery

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures (Preprint)

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