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

结果驱动的订单集内容开发、管理和评估

• 批准号: 10242838
• 负责人: Yiye Zhang
• 金额: $ 17.64万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-13 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242838
• 关键词: Address; Algorithms; Area; Biological Markers; Biometry; Caring; Clinical; Clinical Pathways; Clinical Practice Guideline; Data; Data Science; Decision Making; Development; Disease; Electronic Health Record; Ensure; Environment; Evaluation; Goals; Health Information System; Health Policy; Healthcare; Heart failure; Hospitalization; Hospitals; Human; Information Management; Information Systems; Judgment; Knowledge; Lead; Learning; Literature; Logistic Regressions; Machine Learning; Management Information Systems; Measures; Medicine; Mentors; Mentorship; Methodology; Methods; Modeling; Outcome; Outcome Study; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Physicians; Policy Research; Presbyterian Church; Principal Investigator; Procedures; Process; Provider; Public Health Informatics; Randomized Clinical Trials; Records; Reporting; Research; Research Activity; Research Training; Role; Site; Standardization; Structural Models; Surveys; System; Techniques; Testing; Time; Training; Training Activity; Variant; Work; Workload; base; biomedical informatics; care outcomes; career development; clinical decision support; clinical decision-making; cohort; comorbidity; computerized; computerized physician order entry; data mining; demographics; design; evidence base; health care delivery; health care service organization; hospital readmission; implementation study; improved; improved outcome; individual patient; innovation; inpatient service; long short term memory; medical schools; multidisciplinary; multilayer perceptron; neural network algorithm; novel; outcome prediction; patient oriented; patient subsets; personalized decision; phenotyping algorithm; predictive modeling; random forest; recurrent neural network; research facility; research study; satisfaction; social health determinants; statistical and machine learning; statistics; success; support vector machine; usability; validation studies

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.

项目摘要 候选人的目标和目的： 拥有信息系统和管理以及生物统计学背景的张博士展示了 关于电子健康记录数据挖掘的研究记录，以确定医疗保健提供的模式 用于告知以患者为中心和以证据为基础的医疗保健。该提案将提供额外的培训 为张博士介绍生物医学信息学中的高级机器学习、统计和评估方法 临床决策支持(CDS)的应用。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： 威尔康奈尔医学院(WCM)为张博士提供了理想的研究设施和培训环境。Dr。 卫生政策和研究部卫生信息学司司长乔蒂什曼·帕塔克将 领导一个多学科的导师团队：WCM的Jessica Ancker博士和Fei Wang博士，以及Adam Wright博士 哈佛医学院。张医生在WCM和纽约长老会医院也有合作伙伴 将支持她的培训和研究活动，并提供临床专业知识。 研究目标 订单集是计算机化提供商订单录入(CPOE)中的一种CDS，用于标准化决策 订购流程，并鼓励遵守临床实践指南。前人关于秩序的文献 Set Use的影响主要集中在可用性、工作量和医生满意度上，但知识差距仍然存在 关于顺序设置对护理结果的影响。研究性研究的总体目标是创建一个 通过严格从数据中学习，针对护理结果对医嘱集进行持续改进。 这项研究的目标1将应用计算表型和亚型算法来识别心脏队列 故障(HF)子类型。Aim 2将评估用于护理心力衰竭患者的现有顺序集 结果定义为30天的全原因、计划外重新入院，并假设使用此顺序集 与更好的结果相关。这将通过建立一系列结果预测模型和 评价各阶集阶数作为预测值的强弱。目标3将使用以下工具优化现有的订单集 一种元启发式优化方法，使其内容集合在一起对 30天全因意外重新入院的结果。医嘱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进行测量。预期结果是一个框架，以 开发和评估HF有序集，最终可能推广到其他临床领域。培训来源： 这一建议可能导致对结果驱动的HF顺序集和实际实施的多站点R01研究。