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: 10181034
• 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/10181034
• 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，并且计划扩展成本高昂。此外，VA CBT计划包括每周10个小时的会议，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。来自先前CBT试验的数据已经记录了患者对延长治疗的需求的实质性变化，并且有效方案的特征显著不同。有些病人在最初的几次治疗后病情好转 有些人需要更多的接触，有些人需要更多的接触。在最初建立行为计划后，还有一些退伍军人可能能够使用个性化的手册组合，与治疗师进行更短的后续接触，以及自动电话监控和自我护理支持电话来达到行为和症状目标。与国家疼痛管理计划合作，我们建议应用“强化学习”（人工智能或人工智能领域成功地用于机器人和在线消费者定位）的最先进原则，开发基于证据的个性化CBT疼痛管理服务，自动适应每个退伍军人独特和不断变化的需求（AI- CBT）。AI-CBT将使用患者的反馈，通过计步器步数每天测量疼痛相关功能的进展，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，并可能允许固定预算的计划为更多的退伍军人服务。该研究的具体目的是：（1）证明与标准电话CBT相比，AI-CBT具有非劣效性疼痛相关结局;（2）记录AI-CBT通过更有效地利用稀缺的临床医生资源实现这些结局，如总治疗时间更少，并且其他VA健康服务的使用没有增加;和（3）证明干预对与治疗反应相关的近端结局的影响，包括项目参与、疼痛管理技能获得、对护理的满意度和患者退出的可能性。我们将使用与患者、临床医生和VA运营合作伙伴的定性访谈，以确保该服务具有最大化可扩展性、广泛采用和影响的功能。将从VA Connecticut Healthcare System和VA安阿伯医疗保健系统招募278例慢性腰痛患者，并随机分配至标准的10次电话CBT与AI-CBT。所有患者将开始每周一次长达一小时的电话咨询，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逐步减少长达一小时的接触，包括：（a）与治疗师进行15分钟的接触，以及（B）通过交互式语音应答电话（IVR）提供CBT临床医生反馈。人工智能引擎将根据患者通过IVR测量的步数以及他们的CBT技能实践和身体功能的日常反馈，了解患者个性化治疗计划的最佳效果。我们将使用的人工智能算法被设计得尽可能高效，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病史来学习最适合特定患者的方法。我们的假设是，AI-CBT将导致 疼痛相关的功能结果并不比标准方法更差（也可能更好），但通过降低接触强度，不会导致疼痛控制的边际收益，AI-CBT方法在治疗时间方面的成本将显著降低。次要假设是，AI-CBT将导致更大的患者参与度和患者满意度。将在招募后3个月和6个月测量结局，包括疼痛相关干扰、治疗满意度和治疗脱落。