Evaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health Record

使用电子健康记录中的自动化工具评估和改善心力衰竭患者循证医学治疗的使用

• 批准号: 10594487
• 负责人: Rohan Khera
• 金额: $ 19.17万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594487
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Adrenergic beta-Antagonists; Adult; Affect; Algorithm Design; Algorithms; Anaphylaxis; Angiotensin Receptor; Angiotensin-Converting Enzyme Inhibitors; Angiotensins; Automated Clinical Decision Support; Automation; Bradycardia; Cardiovascular Diseases; Cardiovascular system; Caring; Clinical; Code; Consensus; Coughing; Critical Care; Data; Databases; Decision Making; Development Plans; Diagnosis; Documentation; Echocardiography; Electronic Health Record; Electronics; Eligibility Determination; Ensure; Environment; Evaluation; Evidence based treatment; Failure; Feedback; Focus Groups; Functional disorder; Goals; Healthcare; Heart failure; Hospitalization; Hospitals; Individual; Informatics; Institution; Intensive Care; K-Series Research Career Programs; Laboratories; Lead; Left; Low Cardiac Output; Machine Learning; Manuals; Measurement; Measures; Medical; Medical History; Medical Informatics; Medical Records; Mentors; Mentorship; Methodology; Mineralocorticoid Receptor; Modeling; Natural Language Processing; Neprilysin; Outcomes Research; Outpatients; Patient Care; Patient-Focused Outcomes; Patients; Peptidyl-Dipeptidase A; Pharmaceutical Preparations; Phenotype; Physicians; Process; Provider; Qualifying; Qualitative Evaluations; Qualitative Research; Quality of Care; Random Allocation; Recommendation; Recording of previous events; Records; Registries; Reporting; Research; Research Methodology; Resources; Risk Reduction; Source; Structure; Surveys; Systolic heart failure; Technology; Testing; Therapeutic; Therapeutic Agents; Time; Training; Treatment Failure; Validation; Ventricular; advanced analytics; analytical tool; antagonist; automated algorithm; care delivery; career; career development; clinical care; clinical decision support; computing resources; deep learning; design; evidence base; experience; follow-up; health data; hospitalization rates; hyperkalemia; implementation strategy; improved; improved outcome; individual patient; inhibitor; mortality; open source; patient oriented; patient subsets; pilot test; point of care; programs; prospective; prototype; recruit; skills; structured data; support tools; time use; tool

PROJECT SUMMARY Heart failure (HF) affects over 6 million US adults, with high rates of hospitalization and nearly 50% mortality at 5 years from diagnosis. Nearly half of these patients have systolic HF with multiple evidence-based therapeutic options proven to reduce the risk of hospitalization and mortality in this subgroup of patients. Evaluating the appropriate utilization of these therapies is currently limited to post-hoc assessments of manually abstracted patient records at a limited number of hospitals participating in quality improvement registries. These manual abstraction strategies do not offer opportunities to improve care in real-time, and even at hospitals engaged in quality improvement efforts, only 1 in 5 of eligible patients with HF receive all first-line evidence based medical treatments. In this patient-oriented mentored career development award proposal, Dr. Rohan Khera proposes to leverage the ubiquitous digitization of medical records in the electronic health record (EHR) to address the adequate utilization of evidence based medical therapy in HF. He proposes to use a large, publicly accessible, deidentified EHR database to develop and validate an algorithm that uses deep learning based natural language processing (NLP) within unstructured clinical documentation for hospitalized HF patients to identify those with systolic HF (Aim #1). He will engage clinicians to design consensus-based algorithms to identify contraindications to HF treatments, developed as algorithms within the EHR (Aim #2). Finally, he will construct a prototypic clinical decision support (CDS) tool identifying HF treatment eligibility in real-time using the algorithms and evaluate potential implementation strategies using qualitative evaluation of feedback from clinicians and patients (Aim #3). While proposed as a strategy to evaluate quality of care of individual patients, the proposed research will also model a fully automated electronic clinical quality measure for HF. The algorithms will be made open source to allow institutions to validate and apply them to their individual care setting. The proposal is supported by strong mentorship from experts in quality measure design, informatics, advanced NLP, CDS design, and qualitative research methodology. The facilities at Yale Center of Outcomes Research and Evaluation, which designs and evaluates national quality measures, and has access to computational resources required to accomplish the research goals as well as to the Yale EHR to validate the models are major strengths of the application. The proposed period of mentored research will support Dr. Khera’s training in medical informatics, advanced analytic tools such as NLP, and qualitative research methodology. The experience and skillset acquired during this period will support Dr. Khera’s transition to independence where he plans to lead multi-institutional collaboratives to evaluate the use of automated tools in the measurement and improvement of the quality of medical care in HF. The career development plan that accompanies the proposal is designed to support Dr. Khera’s long-term career goal to be a national leader in the design and implementation of informatics- based approaches of delivering high quality, patient-centered, cardiovascular care.

项目概要 心力衰竭 (HF) 影响超过 600 万美国成年人，住院率很高，死亡率接近 50% 诊断后 5 年，这些患者中近一半患有收缩性心力衰竭，并接受多种循证治疗。 经证明可以降低该亚组患者住院和死亡风险的选择。 目前对这些疗法的适当利用仅限于对手动提取的事后评估 参与质量改进登记的数量有限的医院的患者记录。 抽象策略无法提供改善实时护理的机会，甚至在从事以下工作的医院也是如此： 质量改进工作，只有五分之一的符合条件的心力衰竭患者接受了所有一线循证医学治疗 在这项以患者为导向的职业发展奖提案中，Rohan Khera 博士建议： 利用电子健康记录 (EHR) 中无处不在的医疗记录数字化来解决 他建议在心力衰竭中充分利用基于证据的药物治疗。 去识别化的 EHR 数据库，用于开发和验证使用基于深度学习的自然语言的算法 在住院心力衰竭患者的非结构化临床文档中进行处理（NLP），以识别那些患有心力衰竭的患者 他将致力于设计基于共识的算法来识别收缩性心力衰竭。 心力衰竭治疗的禁忌症，作为 EHR 内的算法开发（目标#2）。 原型临床决策支持 (CDS) 工具，使用 算法并使用来自实施反馈的定性评估来评估潜在策略 Foreman 和患者（目标#3）虽然被提出作为评估个体患者护理质量的策略， 拟议的研究还将对心力衰竭的全自动电子临床质量测量进行建模。 将开源，以允许机构验证它们并将其应用到他们的个人护理环境中。 该提案得到了质量衡量设计、信息学、高级 NLP 领域专家的大力指导支持， CDS 设计和定性研究方法。耶鲁成果研究中心的设施。 评估，设计和评估国家质量措施，并有权使用计算资源 完成研究目标以及耶鲁电子病历验证模型所需的能力是主要优势 拟议的指导研究期将支持 Khera 博士的医学培训。 信息学、高级分析工具（例如 NLP）和定性研究方法。 在此期间获得的技能将支持凯拉博士向独立过渡，他计划领导 多机构合作来评估自动化工具在测量和改进中的使用情况 该提案附带的职业发展计划旨在提高心力衰竭的医疗质量。 支持 Khera 博士的长期职业目标，即成为信息学设计和实施方面的国家领导者- 提供高质量、以患者为中心的心血管护理的方法。