Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial
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 prog...
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| Veröffentlicht in: | Archives of internal medicine (1960) 2022-09, Vol.182 (9), p.975-983 |
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| creator | Piette, John D Newman, Sean Krein, Sarah L Marinec, Nicolle Chen, Jenny Williams, David A Edmond, Sara N Driscoll, Mary LaChappelle, Kathryn M Kerns, Robert D Maly, Marianna Kim, H. Myra Farris, Karen B Higgins, Diana M Buta, Eugenia Heapy, Alicia A |
| description | 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 |
| doi_str_mv | 10.1001/jamainternmed.2022.3178 |
| format | Article |
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Myra ; Farris, Karen B ; Higgins, Diana M ; Buta, Eugenia ; Heapy, Alicia A</creator><creatorcontrib>Piette, John D ; Newman, Sean ; Krein, Sarah L ; Marinec, Nicolle ; Chen, Jenny ; Williams, David A ; Edmond, Sara N ; Driscoll, Mary ; LaChappelle, Kathryn M ; Kerns, Robert D ; Maly, Marianna ; Kim, H. Myra ; Farris, Karen B ; Higgins, Diana M ; Buta, Eugenia ; Heapy, Alicia A</creatorcontrib><description>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</description><identifier>ISSN: 2168-6106</identifier><identifier>EISSN: 2168-6114</identifier><identifier>DOI: 10.1001/jamainternmed.2022.3178</identifier><identifier>PMID: 35939288</identifier><language>eng</language><publisher>United States: American Medical Association</publisher><subject>Artificial Intelligence ; Chronic Pain - psychology ; Chronic Pain - therapy ; Clinical outcomes ; Clinical trials ; Cognitive Behavioral Therapy ; Cognitive therapy ; Female ; Humans ; Less Is More ; Male ; Middle Aged ; Narcotics ; Online First ; Original Investigation ; Pain management ; Patient-Centered Care ; Telemedicine ; Treatment Outcome</subject><ispartof>Archives of internal medicine (1960), 2022-09, Vol.182 (9), p.975-983</ispartof><rights>Copyright American Medical Association Sep 2022</rights><rights>Copyright 2022 American Medical Association. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-a361t-5350a4d85ec84a37a9ecac8c4e9199110715708feb44649329322e91906c5f0a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://jamanetwork.com/journals/jamainternalmedicine/articlepdf/10.1001/jamainternmed.2022.3178$$EPDF$$P50$$Gama$$H</linktopdf><linktohtml>$$Uhttps://jamanetwork.com/journals/jamainternalmedicine/fullarticle/10.1001/jamainternmed.2022.3178$$EHTML$$P50$$Gama$$H</linktohtml><link.rule.ids>64,230,314,776,780,881,3327,27901,27902,76231,76234</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35939288$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Piette, John D</creatorcontrib><creatorcontrib>Newman, Sean</creatorcontrib><creatorcontrib>Krein, Sarah L</creatorcontrib><creatorcontrib>Marinec, Nicolle</creatorcontrib><creatorcontrib>Chen, Jenny</creatorcontrib><creatorcontrib>Williams, David A</creatorcontrib><creatorcontrib>Edmond, Sara N</creatorcontrib><creatorcontrib>Driscoll, Mary</creatorcontrib><creatorcontrib>LaChappelle, Kathryn M</creatorcontrib><creatorcontrib>Kerns, Robert D</creatorcontrib><creatorcontrib>Maly, Marianna</creatorcontrib><creatorcontrib>Kim, H. Myra</creatorcontrib><creatorcontrib>Farris, Karen B</creatorcontrib><creatorcontrib>Higgins, Diana M</creatorcontrib><creatorcontrib>Buta, Eugenia</creatorcontrib><creatorcontrib>Heapy, Alicia A</creatorcontrib><title>Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial</title><title>Archives of internal medicine (1960)</title><addtitle>JAMA Intern Med</addtitle><description>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</description><subject>Artificial Intelligence</subject><subject>Chronic Pain - psychology</subject><subject>Chronic Pain - therapy</subject><subject>Clinical outcomes</subject><subject>Clinical trials</subject><subject>Cognitive Behavioral Therapy</subject><subject>Cognitive therapy</subject><subject>Female</subject><subject>Humans</subject><subject>Less Is More</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Narcotics</subject><subject>Online First</subject><subject>Original Investigation</subject><subject>Pain management</subject><subject>Patient-Centered Care</subject><subject>Telemedicine</subject><subject>Treatment Outcome</subject><issn>2168-6106</issn><issn>2168-6114</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdUU1rGzEQXUpLE9L8gR5aQS-9rKuP_ZB6KBiTNIGUhuKcxViedWR2JVdaB9pDf3tmcWqaCoEG3ps3evOK4r3gM8G5-LSFAXwYMYUB1zPJpZwp0eoXxakUjS4bIaqXx5o3J8V5zltOR3NeKfW6OFG1UUZqfVr8uYXRYxjLBU6KuGa3pM0WkJDdZR82bJ5G33nnoWfXROl7v8HgkEFYs29x5XtkVwj9eM-WMfb5M5uzH4TFwf8mtUUcdpBoxgOyi65DN1UBc2bLRJJvilcd9BnPn96z4u7yYrm4Km--f71ezG9KUI0Yy1rVHKq1rtHpClQLBh047So0whgheCvqlusOV1XVVEZJunLCeOPqjoM6K74cdHf7FS3NkdkEvd0lP0D6ZSN4-xwJ_t5u4oM1NF9oRQIfnwRS_LnHPNrBZ0fbgIBxn61sjDF1W6uWqB_-o27jPgWyZ2UrVN1o00ys9sByKeacsDt-RnA7xWyfxWynmO0UM3W--9fLse9vqER4eyCQwBGVrak0OXkEPV-w4A</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Piette, John D</creator><creator>Newman, Sean</creator><creator>Krein, Sarah L</creator><creator>Marinec, Nicolle</creator><creator>Chen, Jenny</creator><creator>Williams, David A</creator><creator>Edmond, Sara N</creator><creator>Driscoll, Mary</creator><creator>LaChappelle, Kathryn M</creator><creator>Kerns, Robert D</creator><creator>Maly, Marianna</creator><creator>Kim, H. 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Myra</au><au>Farris, Karen B</au><au>Higgins, Diana M</au><au>Buta, Eugenia</au><au>Heapy, Alicia A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial</atitle><jtitle>Archives of internal medicine (1960)</jtitle><addtitle>JAMA Intern Med</addtitle><date>2022-09-01</date><risdate>2022</risdate><volume>182</volume><issue>9</issue><spage>975</spage><epage>983</epage><pages>975-983</pages><issn>2168-6106</issn><eissn>2168-6114</eissn><abstract>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</abstract><cop>United States</cop><pub>American Medical Association</pub><pmid>35939288</pmid><doi>10.1001/jamainternmed.2022.3178</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Archives of internal medicine (1960), 2022-09, Vol.182 (9), p.975-983 |
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| source | MEDLINE; American Medical Association Journals (including JAMA) |
| subjects | Artificial Intelligence Chronic Pain - psychology Chronic Pain - therapy Clinical outcomes Clinical trials Cognitive Behavioral Therapy Cognitive therapy Female Humans Less Is More Male Middle Aged Narcotics Online First Original Investigation Pain management Patient-Centered Care Telemedicine Treatment Outcome |
| title | Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial |
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