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

该学院早期职业发展（Career）项目奖旨在研究如何更好地预测人口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。患者在慢性且不会立即危及生命的情况下发挥关键作用，但从长远来看，可能会通过相关的合并症造成危害。这项研究建立在与卫生科学专家合作的基础上，采取跨学科方法。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。这些新的病人选择模型将被纳入健康结果（戒烟和产妇饮酒）的预测模型，并将在人口水平上进行长期治疗效果研究。教育计划包括建议和指导活动，这些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”项目一起工作。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个体患者的偏好，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对治疗效果的更准确估计以及对健康和经济结果的更准确预测。这使决策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。在技术专业学习的中学生将被引入工业工程领域，并与本科生研究团队合作。研究生将指导参与的本科生，从而形成一个学生管道，他们将准备在医疗保健服务行业和学术界从事技术职业。

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.