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: 10375578
• 负责人: Rohan Khera
• 金额: $ 19.2万
• 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/10375578
• 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; Eligibility Determination; Ensure; Environment; Evaluation; Evidence based treatment; Feedback; Focus Groups; Functional disorder; Goals; Gold; 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; Qualitative Evaluations; Qualitative Research; Quality of Care; Recommendation; Recording of previous events; Records; Registries; Reporting; Research; Research Methodology; Resources; Risk; Source; Structure; Surveys; Systolic heart failure; Technology; Testing; Therapeutic; Therapeutic Agents; Time; Training; Treatment Failure; Validation; Ventricular; advanced analytics; analytical tool; antagonist; automated algorithm; base; 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; recruit; 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.

项目摘要 心力衰竭（HF）影响超过600万的美国成年人，住院率很高，死亡率近50％ 诊断5年。这些患者中有将近一半具有多种循证治疗的收缩期HF 选项证明，这是这一患者亚组的住院风险和死亡的风险。评估 这些疗法的适当利用目前仅限于对手动抽象的事后评估 参加质量改善注册表的医院数量有限的患者记录。这些手册 抽象策略没有提供实时改善护理的机会，甚至在从事的医院中 质量改进工作，只有五分之一的合格HF患者接受所有基于一线证据的医疗 治疗。在这个面向患者的修改职业发展奖提案中，罗汉·凯拉（Rohan Khera）博士提议 利用电子健康记录（EHR）中医疗记录的普遍数字化来解决 在HF中充分利用基于证据的药物治疗。他提议使用大型，公共访问 去识别的EHR数据库开发和验证使用基于深度学习的自然语言的算法 住院的HF患者的非结构化临床文档中的处理（NLP），以识别患有 收缩HF（AIM＃1）。他将聘请临床医生设计基于共识的算法以识别 HF治疗的禁忌症，在EHR内开发为算法（AIM＃2）。最后，他将建造 原型临床决策支持（CDS）工具，使用该工具确定HF治疗资格的实时资格 使用定性评估反馈的算法和评估潜在实施策略 临床医生和患者（AIM＃3）。虽然提议作为评估个别患者护理质量的策略，但 拟议的研究还将对HF进行全自动电子临床质量测量。算法 将成为开源，以允许机构验证并将其应用于其个人护理环境。 提案得到了质量测量设计专家，信息信息，高级NLP的强烈指导的支持， CD设计和定性研究方法。耶鲁大学结果研究中心的设施 评估，设计和评估国家质量措施，并可以使用计算资源 实现研究目标以及耶鲁EHR以验证模型所需的是主要优势 申请。拟议的Mendored研究期将支持Khera博士在医疗方面的培训 信息信息，NLP等高级分析工具和定性研究方法。经验和 在此期间获得的技能将支持Khera博士向独立的过渡，他计划领导 多机构合作申请评估自动工具在测量和改进中的使用 HF的医疗质量。涉及该提案的职业发展计划旨在 支持Khera博士的长期职业目标，以成为信息的设计和实施的国家领导者 - 提供高质量，以患者为中心的心血管护理的方法。