Outcome-driven Order Set Content Development, Management, and Evaluation

项目摘要 候选人目标和目的： 张博士拥有信息系统和管理以及生物统计学的背景， 研究电子健康记录数据挖掘记录，以确定可能 用于告知以患者为中心和循证医疗保健。该提案将提供额外的培训 为张博士提供生物医学信息学中先进的机器学习，统计和评估方法， 临床决策支持（CDS）。张博士的长期目标是将创新CDS 通过新的生物医学信息学和数据科学技术进行开发和评估。 制度环境与职业发展： 威尔康奈尔医学（WCM）为张博士提供了理想的研究设施和培训环境。博士 卫生政策和研究部卫生信息学司司长Jyotishman Pathak将 领导一个多学科导师团队：WCM的Jessica Ancker博士和王飞博士，以及 哈佛医学院。张博士还在WCM和纽约长老会医院有合作者， 将支持她的培训和研究活动，并提供临床专业知识。 研究旨在 订单集是计算机化供应商订单输入（CPOE）中的一种CDS，用于标准化 订购过程，并鼓励遵守临床实践指南。先前订购的文献 集合使用的影响集中在可用性、工作量和医生满意度上，但仍然存在知识差距 关于顺序集对护理结果的影响。研究性学习的总体目标是创建一个 通过严格地从数据中学习，实现关于护理结果的订单集的持续改进周期。 本研究的目的1将应用计算表型和亚型算法来识别心脏队列， 失败（HF）亚型。目标2将评估用于HF患者护理的现有订单集， 结局定义为30天全因计划外再入院，假设使用该顺序集 与更好的结果相关。这将通过建立一系列结果预测模型和 评估每个订单集订单的强度作为预测器。目标3将优化现有的订单集， 一种元启发式优化方法，使得其内容可以共同地对 30天全因计划外再入院的结果。顺序集使用对护理结果的影响是 在每次迭代中使用因果推理技术测量。预期成果是一个框架， 开发和评估最终可能推广到其他临床领域的HF顺序集。训练 该提议可以导致结果驱动HF阶集的多站点R01研究和实际实现。

Project Summary Candidate Goals and Objectives: With a background in Information Systems and Management, and Biostatistics, Dr. Zhang has demonstrated research records on electronic health record data mining to identify patterns of healthcare delivery that may be used to inform patient-centered and evidence-based healthcare. The proposal will provide additional training for Dr. Zhang on advanced machine learning, statistics, and evaluation methods in biomedical informatics for applications on clinical decision support (CDS). Dr. Zhang's long-term goal is to bringing innovation CDS development and evaluation through novel biomedical informatics and data science techniques. Institutional Environment and Career Development: Weill Cornell Medicine (WCM) provides ideal research facilities and training environment for Dr. Zhang. Dr. Jyotishman Pathak, Chief of Division of Health Informatics at Department of Health Policy and Research, will lead a multidisciplinary team of mentors: Drs. Jessica Ancker and Fei Wang at WCM, and Dr. Adam Wright at Harvard Medical School. Dr. Zhang also has collaborators in WCM and NewYork-Presbyterian Hospital who will support her in her training and research activities and provide clinical expertise. Research Aims Order sets are a type of CDS in computerized provider order entry (CPOE) to standardize decision making in the ordering process and encourage compliance with clinical practice guidelines. Previous literature on order set use has focused its effect on usability, workload, and physician satisfaction, but a knowledge gap remains with respect to the effect of order sets on care outcomes. The overall goal of the research study is to create a continuous improvement cycle for order sets with respect to a care outcome by rigorously learning from data. Aim 1 of the study will apply computational phenotyping and subtyping algorithms to identify cohorts of heart failure (HF) subtypes. Aim 2 will evaluate an existing order set intended for the care of HF patients on a care outcome defined as 30-day all-cause, unplanned readmission with a hypothesis that the use of this order set is associated with a better outcome. This will be achieved by building a range of outcome prediction models and evaluating the strength of each order set order as a predictor. Aim 3 will optimize the existing order sets using a metaheuristic optimization method such that its content collectively may have the largest positive effect on the outcome of 30-day all-cause unplanned readmission. The effects of order set use on the care outcome is measured using a causal inference technique in each iteration. The expected outcome is a framework to develop and evaluate HF order sets which may eventually be generalized to other clinical areas. Training from this proposal may lead to multi-site R01 studies of outcome-driven HF order sets and actual implementations.