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: 10176571
• 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/10176571
• 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）是慢性下背痛的最有效治疗方法之一。但是，只有一半的退伍军人可以使用训练有素的CBT治疗师，并且计划扩展成本很高。此外，VA CBT程序由使用与逐步护理模型的方法进行的10个小时的10个小时的会话组成，旨在确保尽可能有效地使用稀缺资源。先前CBT试验的数据已证明患者对扩展治疗的需求的实质性差异，有效程序的特征差异很大。一些患者在前几个后改善 会议虽然其他人需要更广泛的联系。最初制定了行为计划后，其他退伍军人可能还能够使用手动的个性化组合，与治疗师的较短后续联系以及自动电话监控和自我保健支持呼叫达到行为和症状目标。通过与国家疼痛管理计划合作，我们建议将“强化学习”（人工智能或人工智能领域或AI领域）应用于机器人技术和在线消费者的目标中）来开发基于证据的个性化的CBT疼痛管理服务，以自动适应每个老将独特的和不断变化的需求（AI-CBT）。 AI-CBT将使用患者的反馈，以了解他们每天通过计步计测量的疼痛功能进展，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，并有可能允许有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具有与内部疼痛相关的无效预后； （2）证明AI-CBT可以通过更有效地利用稀缺的临床医生资源来实现这些结果，这是由于整体治疗师的时间较少，并且没有增加其他VA卫生服务的使用； （3）证明了干预措施对与治疗反应相关的近端结果的影响，包括计划参与，疼痛管理技能获取，对护理的满意度以及患者的辍学可能性。我们将对患者，临床医生和VA运营合作伙伴进行定性访谈，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。 278例慢性腰痛患者将从VA Connecticut Healthcare系统和VA Ann Arbor Healthcare系统中招募，并随机分为标准的10年级电话CBT与AI-CBT。所有患者都将从每周一小时的电话咨询开始，但是对于AI-CBT组的患者，表现出明显治疗反应的患者将通过较低的资源密集型替代替代方案来逐步进行长时间的接触，包括：（a）与治疗师的15分钟联系人，以及（b）CBT Clinician Clinician通过交互式语音反应提供了（IVR）。 AI发动机将根据IVR每日反馈患者的个人量化治疗计划，了解患者的计算机测量步骤计数以及CBT技能练习和身体功能。我们将使用的AI算法旨在提高效率，以便该系统可以根据其他类似患者以及个人自己的历史的集体经验来学习最适合给定患者的方法。我们的假设是AI-CBT将导致 在与标准方法相比，与疼痛相关的功能结果并不比标准方法更糟糕（并且可能更好），但是通过缩减无法导致疼痛控制边际增长的接触强度，AI-CBT方法在治疗时间方面的成本将大大降低。次要假设是AI-CBT会导致患者的参与度和患者满意度更高。结局将在招募后三个月和六个月后进行测量，并包括与疼痛相关的干扰，治疗满意度和治疗辍学。