CAREER: Incorporating Patient Heterogeneity and Choice into Predictive Models of Health and Economic Outcomes

职业：将患者异质性和选择纳入健康和经济结果的预测模型中

• 批准号: 1150732
• 负责人: Maria Mayorga
• 金额: $ 40万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1150732&HistoricalAwards=false
• 关键词: CAREER; Incorporating; Patient; Heterogeneity; Choice

This Faculty Early Career Development (CAREER) Program award investigates how to obtain better predictions of the effectiveness of population-level health interventions. The research strategy is to create consumer choice models that incorporate patient heterogeneity into predictive models of health and economic outcomes. The patient plays a key role in conditions that are chronic and not immediately life threatening, but that in the long term can be harmful through associated co-morbidities. This research builds on collaborations with health sciences experts to take an interdisciplinary approach. Consumer choice models will be transformed into models for patient choice. This allows for individual patient preferences to be considered (e.g., time-preferences, willingness-to-pay for treatment) which may depend on socio-demographic patient attributes. These new patient choice models will be embedded into predictive models of health outcomes (smoking cessation and maternal drinking) and long term treatment effectiveness studies will be conducted at the population level. The educational plan includes advising and mentoring activities that work with discovery-oriented "creative inquiry" projects involving multi-level student teams.If successful, the results of this research will allow health science experts to move from efficacy to effectiveness characterization by explicitly considering individual patient preferences. These new models may also be used in predictive health models of other policy-relevant issues. Ultimately, the inclusion of patient choice will result in more accurate estimates of treatment effectiveness and more accurate predictions of health and economic outcomes. This allows policy makers to choose the most efficient intervention strategies, with a richer information set than can be provided by clinical trials alone. Middle school students studying in technology programs will be introduced to the field of industrial engineering and engage with undergraduate research teams. Graduate students will mentor participating undergraduates, resulting in a pipeline of students who will be prepared to undertake technical careers in the healthcare service industry and in academia.

这个教师早期职业发展（CAREER）计划奖调查如何获得更好的预测人口水平的健康干预措施的有效性。研究策略是创建消费者选择模型，将患者异质性纳入健康和经济结果的预测模型。 患者在慢性疾病中起着关键作用，这些疾病不会立即危及生命，但从长远来看，通过相关的合并症可能是有害的。 这项研究建立在与健康科学专家合作的基础上，采取跨学科的方法。 消费者选择模型将转变为患者选择模型。 这允许考虑个体患者的偏好（例如，时间偏好、支付治疗的意愿），这可能取决于社会人口统计学患者属性。 这些新的患者选择模型将嵌入健康结果（戒烟和孕产妇饮酒）的预测模型中，并将在人群水平上进行长期治疗有效性研究。该教育计划包括咨询和指导活动，与涉及多层次学生团队的以发现为导向的“创造性探究”项目合作。如果成功，这项研究的结果将允许健康科学专家通过明确考虑患者个人偏好，从疗效转向有效性表征。 这些新模型也可用于其他政策相关问题的预测健康模型。 最终，纳入患者选择将导致更准确的治疗效果估计和更准确的健康和经济结果预测。 这使政策制定者能够选择最有效的干预策略，并提供比单独临床试验更丰富的信息集。在技术课程中学习的中学生将被引入工业工程领域，并与本科研究团队合作。研究生将指导参与的本科生，导致学生谁将准备在医疗服务行业和学术界从事技术职业的管道。