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

利用临床决策支持在初级保健环境中最大限度地提高在线肥胖治疗反应

• 批准号: 10301403
• 负责人: Hallie Espel-Huynh
• 金额: $ 18.74万
• 依托单位: MIRIAM HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2022-02-26
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10301403
• 关键词: Address; Applications Grants; Behavior Therapy; Behavioral; Blood; Body Weight; Body Weight Changes; Body Weight decreased; Cardiovascular Diseases; Clinic; Clinical; Clinical Decision Support Systems; Clinical Trials; Complement; Data; Data Science; Development; Development Plans; Disease Management; Disease Outcome; Doctor of Philosophy; Early treatment; Eating Disorders; Effectiveness; Ensure; Environment; Future; Goals; Guidelines; Health Benefit; Health Care Costs; Heart; Intervention; Interview; Lead; Lung; Machine Learning; Mentors; Mentorship; Methods; Modeling; Monitor; National Heart, Lung, and Blood Institute; Non obese; Obesity; Outcome; Patient-Focused Outcomes; Patients; Population; Preparation; Primary Health Care; Provider; Public Health; Randomized Clinical Trials; Randomized Controlled Trials; Research; Research Personnel; Research Project Grants; Research Training; Risk; Scientist; Sleep; Structure; System; Technology; Testing; Time; Training; Treatment outcome; Weight; advanced analytics; base; burden of illness; cardiovascular disorder risk; care providers; career; career development; clinical care; clinical center; clinical decision support; clinical effect; clinically significant; comorbidity; cost; design; evidence base; experience; frontier; i(19); implementation research; improved; improved outcome; machine learning algorithm; obese patients; obesity treatment; online intervention; patient oriented; patient population; personalized care; pragmatic trial; precision medicine; predictive modeling; premature; prevent; primary care setting; prototype; research clinical testing; risk prediction; routine care; success; support tools; tool; treatment response; treatment risk; usability

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.

项目总结/摘要 随着成为一个领先的独立研究人员使用技术驱动的长期职业目标， 精准医学方法，以改善肥胖的结果在初级保健，候选人，博士哈莉埃克塞特- Huynh博士提出了一个指导性的研究项目和职业发展计划，这将使她准备使用 先进的分析技术，为肥胖患者提供个性化的常规临床护理。尽管在线的承诺 干预措施，以最大限度地获得有效的行为肥胖治疗在初级保健，许多患者做 不受益于无响应。循证救援干预措施（EBRI）可以改善这些患者的结局。 然而，患者，初级保健临床医生需要指导何时以及如何干预，这样的工具 还不存在临床决策支持（CDS）有可能通过预测以下风险来填补这一空白： 无反应，向临床医生提供有关此风险的警报，并使干预措施能够逆转它。 本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）利用利益攸关方的投入， CDS与初级保健提供者的需求，（2）建立一个机器学习模型，以预测早期风险的在线 肥胖治疗无反应整合到CDS，和（3）设计原型CDS和测试其 初级保健临床医生的可用性，重点是可行性、可接受性和适当性的结果， 目标初级保健设置。该项目是第一个将联合收割机精确的无响应风险预测与 知情的CDS产生一种工具，有可能最大限度地提高肥胖治疗的结果， 通过提供CDS促进的、临床医生提供的救援干预措施进行初级保健。这项研究将导致 一个完整的CDS工具，准备在初级保健患者的未来临床测试， 大大增强了在线肥胖治疗在这种环境中的潜在影响。研究计划是 辅以职业发展活动，包括技术辅助肥胖的正式培训， 初级保健中的CVD管理，以患者为中心的CDS开发，机器学习和混合 以患者为导向的实施研究方法。在经验丰富的导师指导下， 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R 01拨款申请，以测试CDS的有效性，在一个务实的随机临床试验。