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

基本信息

批准号：
10018097
负责人：
Yiye Zhang
金额：
$ 18.04万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
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/10018097
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 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

项目摘要

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.

项目摘要候选目标和目标： Zhang博士在信息系统和管理和生物统计学方面具有背景电子健康记录数据挖掘的研究记录，以识别可能是医疗保健提供模式用于告知以患者为中心的循证医疗保健。该提案将提供额外的培训对于Zhang博士，关于生物医学信息学的高级机器学习，统计和评估方法临床决策支持（CD）的申请。张博士的长期目标是带来创新CD 通过新颖的生物医学信息学和数据科学技术开发和评估。机构环境和职业发展： Weill Cornell Medicine（WCM）为张博士提供了理想的研究设施和培训环境。博士健康政策与研究部健康信息学部负责人Jyotishman Pathak将领导一个多学科的导师团队：Drs。 WCM的Jessica Ancker和Fei Wang以及Adam Wright博士在哈佛医学院。张博士还在WCM和Newyork-Presbyterian Hospital的合作者中将支持她的培训和研究活动，并提供临床专业知识。研究目的订单集是计算机化提供商订单输入（CPOE）中的一种CD，以标准化决策制定订购过程并鼓励遵守临床实践指南。以前关于秩序的文献设定的使用将其影响放在可用性，工作量和医师满意度上，但知识差距仍然存在关于订单集对护理结果的影响。研究研究的总体目标是创建一个通过严格从数据中学习，订单集的持续改进周期相对于护理结果。该研究的目标1将应用计算表型和亚型算法来识别心脏人群失败（HF）亚型。 AIM 2将评估用于护理HF患者护理的现有订单结果定义为30天全因，计划外的再读，并假设该订单集的使用是与更好的结果相关。这将通过建立一系列结果预测模型和评估每个顺序设置顺序作为预测指标的强度。 AIM 3将使用一种元启发式优化方法，使其含量统称可能对 30天全原因未计划的再入院的结果。订单设置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。预期的结果是一个框架开发和评估HF顺序集，最终可能会推广到其他临床区域。培训该建议可能会导致对结果驱动的HF订单集和实际实施的多站点R01研究。