SHF: Small: Collaborative Research: Designing a Patient-Oriented Prescription Language: An Executable Medical Algorithm for Gestational Diabetes Mellitus

医疗保健中可预防的错误是患者受伤和死亡的主要原因。尽管付出了巨大的努力和数十亿美元的支出，计算机化仍未提高医疗保健的有效性或安全性。对于这个问题，pi提出了一个新颖的见解：处方就是一个项目。也就是说，他们假设处方写作是软件工程失散已久的兄弟，使用许多计算机编程结构，但没有计算机科学提供的广泛概念框架的好处。这个项目将通过利用软件工程和编程语言知识来测试这个假设，从而为构建和执行处方创建一个高度直观的领域特定语言。然后，pi将建立并验证一种“以患者为导向的处方”，以管理妊娠糖尿病（GDM）患者的护理，这种疾病每年影响60万名妇女。目前GDM管理的局限性包括患者依从性低，干预措施的效果适中，临床医生的时间负担和成本。使用来自真实患者的大量丰富数据档案，包括饮食、活动、血糖读数和胰岛素使用，pi将执行纯计算机模拟，这将帮助他们确定POP-GDM程序如何响应真实数据。他们还将与真正的临床医生、演员患者一起进行模拟，并在我们最先进的医疗模拟设施中模拟智能手机、血糖仪和加速度计的输入。pi期望这项工作能够产生关于多阶段程序的构建和维护的多种新见解，开发新的程序分析类型，并发现开发高可靠性软件的新通用方法，以促进机器和人类之间的协作。

Preventable errors in healthcare are a leading cause of patient injury and death. Despite considerable effort and the expenditure of billions of dollars, computerization has yet to improve the efficacy or safety of healthcare. To this problem, the PIs bring a novel insight: a prescription is a program. That is, they hypothesize that prescription writing is a long-lost sibling of software engineering, using many computer programming constructs but without the benefit of the extensive conceptual framework provided by Computer Science. This project will test this hypothesis by exploiting software engineering and programming languages know-how to create a highly intuitive domain-specific language for building and executing prescriptions. The PIs will then build and validate a "patient-oriented prescription" to manage the care of patients with Gestational Diabetes Mellitus (GDM), a disease affecting 600,000 women annually.Limitations of current GDM management include low patient adherence, modest efficacy of interventions, time burden for clinicians, and cost. Using extensive archives of rich data from real patients, including diet, activity, glucose readings, and insulin use, the PIs will perform pure computer-based simulations that will help them determine how their POP-GDM program will respond to real-world data. They will also perform simulations with real clinicians, actor patients, and simulated inputs from smartphones, glucometers, and accelerometers in our state-of-the-art medical simulation facility. The PIs expect this work to generate multiple new insights regarding the building and maintenance of multi-stage programs, the development of new kinds of analyses of programs, and the discovery of new general methods for developing high-reliability software that facilitates collaboration between machines and humans.