SCH: INT: Collaborative Research: Uniting Causal and Mental Models for Shared Decision-Making in Diabetes

糖尿病影响的人口比例越来越大，与许多慢性病一样，它主要由患者自己管理，而不需要医生的日常投入。将血糖保持在健康范围内对于预防糖尿病的长期并发症很重要，但许多2型糖尿病患者没有做到这一点。因此，对患者进行投资并了解他们的治疗目标和计划是很重要的。解决这一问题的一个有希望的方法是共享决策(SDM)，在这种方法中，患者和临床医生一起工作来了解患者的偏好并协作制定治疗计划。SDM可能会增加患者的信任和满意度，但患者、医生和其他护理人员开始时对疾病和治疗有不同的信念。这给SDM带来了挑战，因为每个参与者可能对一个行动会产生什么有不同的理解，当患者的信念与医生提供的信息不同时，这可能会导致沟通困难和信任减少。此外，治疗指南通常一次只关注一个因素，比如运动或营养的作用，很少针对个人。因果模型可能被用来帮助人们理解他们的目标和行动之间的联系，但它们可能太复杂了，人们无法推理。这个项目将导致能够自动学习个性化因果模型的方法，这些模型特定于决策情况和个人健康，并在个人知识的背景下进行交流。这项工作将通过连接因果推理、洞察决策背后的认知过程和共享决策的计算方法，弥合从数据到决策的差距。该项目还将旨在通过创建培训模块来教育临床医生有关患者信念以及这些信念如何影响信任和决策，从而减少治疗差异。这项工作的动机是改善2型糖尿病(T2D)的结果，这项工作将从根本上促进计算方法和我们对认知的理解。虽然影响血糖的因素在个体之间有很大差异，但之前的工作主要集中在寻找人群水平的模型上。为了满足个性化指导的需求，这项工作1)开发了通过利用模拟从有限的个人数据中寻找个性化因果模型(例如，影响血糖的个体因素)的新方法，以及2)考虑到患者的偏好和决策环境，开发了推断模型的个性化抽象，以减轻认知负担。这允许在决策过程中提供更多相关信息。由于决策是在现有知识的背景下做出的，因此该项目的第二个核心重点是将因果模型和心理模型联系起来。虽然之前的工作研究了心理模型的差异，但还没有显示出如何协调不同个体的模型。这项工作开发了新的方法来更有效和准确地得出个人的心理模型，将得到的模型映射到推断的因果模型，并协调不同个体之间的差异。这些方法将部署在患者-提供者和患者-护理者对之间的共享决策中，用于在线和当地诊所的T2D管理。开发的方法将适用于许多类型的共享医疗决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Diabetes affects a growing portion of the population and, like many chronic diseases, it is primarily managed by patients themselves without day-to-day input from doctors. Keeping blood glucose within a healthy range is important for prevent long-term complications of diabetes, but many patients with Type 2 diabetes do not achieve this. As a result, it is important for patients to be invested in and knowledgeable about their treatment goals and plan. One promising approach to address this is shared decision-making (SDM), where a patient and clinician work together to understand the patient's preferences and collaboratively formulate a treatment plan. SDM can potentially increase patient trust and satisfaction, but patients, doctors, and other caregivers begin with different sets of beliefs about disease and treatment. This creates challenges for SDM, as each participant may have a different understanding about what will result from an action, and when a patient's beliefs differ from information provided by a doctor this can lead to communication challenges and reduced trust. Further, treatment guidelines generally focus on one factor at a time, like the role of exercise or nutrition, and are rarely personalized to individuals. Causal models could potentially be used to help people understand the link between their goals and actions, but they can be too complex for people to reason with. This project will lead to methods that can automatically learn personalized causal models that are specific to the decision-making situation and individual's health, and communicated in the context of an individual's knowledge. This work will close the gap from data to decisions by bridging computational methods for causal inference, insight into the cognitive processes underlying decisions, and shared decision-making. The project will also aim to reduce treatment disparities by creating training modules to educate clinicians about patient beliefs and how these influence trust and decision-making. Motivated by improving outcomes in Type 2 Diabetes (T2D), this work will fundamentally advance computational methods, and our understanding of cognition. While factors affecting blood glucose differ considerably between individuals, prior work has focused on finding population-level models. To address the need for personalized guidance, this work 1) develops novel approaches for finding personalized causal models (e.g. individual factors affecting blood glucose) from limited personal data by leveraging simulation, and 2) develops personalized abstractions of the inferred models, taking into account patient preferences and decision context, to reduce cognitive burden. This allows more relevant information to be delivered during decision-making. Since decisions are made in the context of existing knowledge, the second core focus of the project is linking causal models and mental models. While prior work has examined differences in mental models, it has not shown how to reconcile models across individuals. This work develops new approaches to more efficiently and accurately elicit an individual's mental model, map the elicited model to inferred causal models, and reconcile differences across individuals. The approaches will be deployed in shared decision-making between patient-provider and patient-caregiver pairs for T2D management both online and in local clinics. The methods developed will be applicable to many types of shared healthcare decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.