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

糖尿病影响到越来越多的人口，像许多慢性病一样，它主要由患者自己管理，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，对患者来说，投资并了解他们的治疗目标和计划是很重要的。解决这个问题的一个有希望的方法是共同决策（SDM），患者和临床医生一起工作，了解患者的偏好，共同制定治疗计划。SDM可以潜在地增加患者的信任和满意度，但患者、医生和其他护理人员一开始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行动的结果有不同的理解，当患者的信念与医生提供的信息不同时，这可能导致沟通挑战和信任降低。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，很少针对个人进行个性化处理。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，但它们可能过于复杂，人们无法推理。该项目将产生能够自动学习特定于决策情况和个人健康的个性化因果模型的方法，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的计算方法，洞察决策背后的认知过程和共享决策，缩小从数据到决策的差距。该项目还旨在通过创建培训模块，教育临床医生了解患者的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。在改善2型糖尿病（T2D）预后的激励下，这项工作将从根本上推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存在很大差异，但之前的工作主要集中在寻找人群水平的模型。为了满足个性化指导的需求，本工作1)开发了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；2)开发了推断模型的个性化抽象，考虑到患者的偏好和决策背景，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焦点是将因果模型和心智模型联系起来。虽然先前的工作已经研究了心理模型的差异，但它并没有显示如何在个体之间协调模型。这项工作开发了新的方法，以更有效和准确地引出个人的心理模型，将引出的模型映射到推断的因果模型，并调和个体之间的差异。这些方法将用于在线和当地诊所T2D管理的患者-提供者和患者-护理人员对之间的共同决策。所开发的方法将适用于许多类型的共享医疗保健决策。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.