Maximizing Online Obesity Treatment Response in the Primary Care Environment Using Clinical Decision Support

项目摘要/摘要 长期的职业目标是成为领先的独立研究人员，使用技术驱动 在初级保健中改善肥胖结局的精准医学方法，候选人哈莉·埃斯佩尔博士- Huynh博士提出了一个有指导的研究项目和职业发展计划，这将使她做好使用 先进的分析技术，为肥胖患者提供个性化的常规临床护理。尽管在线服务前景看好 在初级保健中最大限度地获得有效的行为肥胖治疗的干预措施，许多患者这样做 不能因无回应而受益。循证救援干预(EBRI)可以改善这些患者的预后 然而，初级保健临床医生需要指导患者何时以及如何进行干预，以及这样的工具 还不存在。临床决策支持(CDS)有可能通过预测风险来填补这一空白 无反应，向临床医生发送关于这一风险的警报，并使干预能够逆转这一风险。整体而言 此培训应用程序的目标是开发用于初级保健的CDS，并测试其可用性 初级保健临床医生。该提案旨在(1)利用利益相关者的意见来协调 CDS符合初级保健提供者的需求，(2)建立机器学习模型以预测在线早期风险 肥胖治疗无反应整合到CDS中；(3)设计CDS原型并对其进行测试 与初级保健临床医生的可用性，重点关注结果的可行性、可接受性和适宜性 目标初级保健设置。该项目是第一个将精确的无反应风险预测与 利益相关者知情的CDS生产一种有可能最大限度地提高肥胖治疗结果的工具 通过提供CDS促进的、临床医生提供的救援干预进行初级保健。这项研究将导致 一种完整的CDS工具，可用于未来对初级保健患者的临床测试，并且可以 极大地增强了在线肥胖治疗在这种背景下的潜在影响。研究计划是 辅之以职业发展活动，包括正规的技术辅助肥胖培训和 初级保健、以利益相关者为中心的CDS开发、机器学习和混合领域的CVD管理 以病人为中心的实施研究方法。在经验丰富的导师的指导下 团队，执行拟议的研究和培训计划将导致Espel-Huynh博士提交一份 竞争性R01授权申请，以在实用的随机临床试验中测试CDS的有效性。

PROJECT SUMMARY/ABSTRACT With the long-term career goal of becoming a leading independent researcher using technology-driven precision medicine approaches to improve obesity outcomes in primary care, the candidate, Dr. Hallie Espel- Huynh, PhD, proposes a mentored research project and career development plan that will prepare her to use advanced analytics to personalize routine clinical care for patients with obesity. Despite the promise of online interventions to maximize access to effective behavioral obesity treatments in primary care, many patients do not benefit due to nonresponse. Evidence-based rescue interventions (EBRI) can improve outcomes for these patients, however, primary care clinicians require guidance on when and how to intervene, and such a tool does not yet exist. Clinical decision support (CDS) has the potential to fill this gap by predicting risk for nonresponse, delivering alerts about this risk to clinicians, and enabling interventions to reverse it. The overall objective of this training application is to develop such a CDS for use in primary care and test its usability with primary care clinicians. The proposal aims to (1) use stakeholder input to align the content and format of the CDS with primary care providers’ needs, (2) build a machine learning model to predict early risk for online obesity treatment nonresponse for integration into the CDS, and (3) design the prototype CDS and test its usability with primary care clinicians, focusing on outcomes of feasibility, acceptability, and appropriateness for the target primary care setting. This project is the first to combine precise nonresponse risk prediction with stakeholder-informed CDS to produce a tool that has the potential to maximize obesity treatment outcomes in primary care via delivery of CDS-facilitated, clinician-delivered rescue interventions. This research will result in a complete CDS tool that is ready for future clinical testing with patients in primary care, and which could greatly enhance the potential impact of online obesity treatment in this setting. The research plan is complemented by career development activities that include formal training in technology-assisted obesity and CVD management in primary care, stakeholder-centered CDS development, machine learning, and mixed methods for patient-oriented implementation research. Under the guidance of an experienced mentorship team, execution of the proposed research and training plan will lead Dr. Espel-Huynh to submission of a competitive R01 grant application to test CDS effectiveness in a pragmatic randomized clinical trial.