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

使用人工智能和移动健康工具以患者为中心的疼痛护理

• 批准号: 10179467
• 负责人: Alicia Heapy
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10179467
• 关键词: Adoption; Algorithms; Artificial Intelligence; Back; Behavior; Behavioral; Behavioral Symptoms; Budgets; Caring; Characteristics; Chronic; Chronic Disease; Chronic low back pain; Client satisfaction; Clinical; Cognitive Therapy; Collaborations; Complex; Connecticut; Counseling; Data; Diabetes Mellitus; Disease; Disease Management; Dropout; Drops; Early treatment; Ensure; Evaluation; Feedback; Goals; Health; Health Communication; Health Services; Health Services Accessibility; Health Technology; Healthcare Systems; Hour; Hypertension; Individual; Intervention; Interview; Learning; Length; Light; Manuals; Measures; Medical; Mental Depression; Mental disorders; Methods; Michigan; Modeling; Monitor; Outcome; Pain; Pain Clinics; Pain management; Patient Monitoring; Patients; Persons; Philosophy; Physical Function; Physical activity; Physiological; Psychological reinforcement; Randomized; Recording of previous events; Reporting; Resources; Robotics; Self Care; Self Management; Services; Structure; Substance abuse problem; Suicide; System; Telephone; Therapy trial; Time; Training; Trees; Universities; Variant; Veterans; Visit; Voice; Work; base; brief intervention; budget impact; chronic pain; cognitive training; comorbidity; cost; design; effective therapy; emotional distress; evidence base; experience; follow-up; functional outcomes; health management; improved; individualized medicine; inpatient service; intelligent algorithm; mHealth; model design; novel strategies; patient engagement; patient oriented; pedometer; personalized approach; personalized medicine; programs; recruit; response; satisfaction; service delivery; skill acquisition; skills; tool; treatment planning; treatment response; trend

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 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. Our hypothesis is that AI-CBT will result in pain-related functional outcomes that are no worse (and possibly better) than the standard approach, but by scaling back the intensity of contact that is not resulting in marginal gains in pain control, the AI-CBT approach will be significantly less costly in terms of therapy time. Secondary hypotheses are that AI-CBT will result in greater patient engagement and patient satisfaction. Outcomes will be measured at three and six months post recruitment and will include pain-related interference, treatment satisfaction, and treatment dropout.

描述(由申请人提供)： 认知行为疗法(CBT)是治疗慢性下腰痛最有效的方法之一。然而，只有一半的退伍军人可以获得训练有素的CBT治疗师，而且扩大计划的成本很高。此外，退伍军人事务部CBT计划包括每周10个小时的课程，使用的方法与旨在确保尽可能有效和高效地使用稀缺资源的分步护理模式不同步。先前CBT试验的数据证明，患者对延长治疗的需求存在很大差异，有效计划的特点也有很大差异。一些患者在最初的几次治疗后有所改善 会议，而其他人则需要更广泛的接触。在最初建立行为计划后，其他退伍军人可能能够使用个性化的手册组合、更短的与治疗师的后续联系以及自动电话监控和自我护理支持呼叫来实现行为和症状目标。通过与国家疼痛管理计划的合作，我们建议应用“强化学习”(人工智能或人工智能领域中成功应用于机器人和在线消费者定位)中的最先进原则来开发基于证据的个性化CBT疼痛管理服务，该服务自动适应每个退伍军人的独特和不断变化的需求(AI-CBT)。AI-CBT将使用来自患者的反馈，通过计步器步数每天测量他们在疼痛相关功能方面的进展，以自动个性化患者支持的强度和类型；从而确保尽可能有效地使用稀缺的治疗师资源，并潜在地允许固定预算的项目为更多的退伍军人服务。该研究的具体目的是：(1)证明与标准的电话CBT相比，AI-CBT具有与疼痛相关的非劣质结果；(2)证明AI-CBT以更有效地利用有限的临床医生资源实现这些结果，这体现在总体治疗师时间更少且不增加使用其他退伍军人健康服务；以及(3)展示干预措施对与治疗反应相关的近期结果的影响，包括计划参与度、疼痛管理技能的获得、护理满意度和患者退出的可能性。我们将使用对患者、临床医生和退伍军人管理局运营合作伙伴的定性访谈，以确保该服务具有最大限度地提高可扩展性、广泛采用和影响的功能。278名慢性下腰痛患者将从弗吉尼亚州康涅狄格州医疗系统和VA Ann Arbor医疗系统招募，并随机接受标准的10次电话CBT与AI-CBT。所有患者都将从每周长达一小时的电话咨询开始，但对于AI-CBT组的患者，那些表现出显著治疗反应的患者将通过资源密集度较低的替代方案来替代长达一小时的联系，包括：(A)与治疗师进行15分钟的联系，以及(B)通过交互式语音应答呼叫(IVR)提供CBT临床医生的反馈。AI引擎将根据IVR对患者计步器测量的步数以及CBT技能练习和身体功能的每日反馈，学习在患者个人量身定制的治疗计划方面效果最好的方案。我们将使用的人工智能算法被设计成尽可能高效，这样系统就可以根据其他类似患者的集体经验以及个人的历史来学习什么对给定的患者最有效。我们的假设是，人工智能-CBT将导致 在与疼痛相关的功能结果中，AI-CBT方法并不比标准方法差(甚至可能更好)，但通过减少接触强度而不会导致疼痛控制的边际收益，AI-CBT方法在治疗时间方面将显著降低成本。第二个假设是AI-CBT将导致更大的患者参与度和患者满意度。结果将在招募后三个月和六个月进行评估，并将包括疼痛相关干预、治疗满意度和治疗退出。