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.

项目总结 心力衰竭(HF)影响着600多万美国成年人，住院率高，死亡率近50%。 5年后确诊。这些患者中有近一半患有收缩期心衰，并接受了多种循证治疗 事实证明，这些选择可以降低这一亚组患者的住院风险和死亡率。评估 这些疗法的适当应用目前仅限于对人工提取的 参与质量改进登记的有限数量的医院的患者记录。这些手册 抽象策略不能提供实时改善护理的机会，甚至在参与的医院也不能 质量改进的努力，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。在这份以患者为导向的指导式职业发展奖提案中，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 在心力衰竭中充分利用循证医学治疗。他建议使用一个大型的、可公开访问的、 确定的EHR数据库，以开发和验证使用基于自然语言的深度学习的算法 处理(NLP)住院心力衰竭患者的非结构化临床文件，以识别那些 收缩心衰(目标1)。他将聘请临床医生设计基于共识的算法来识别 心衰治疗的禁忌症，作为《电子病历》(目标2)内的算法制定。最后，他将构建 一种原型临床决策支持(CDS)工具，使用 算法和使用反馈的定性评估评估潜在的实施策略 临床医生和患者(目标3)。虽然被建议作为评估个别患者的护理质量的策略， 这项拟议的研究还将为心衰的全自动电子临床质量测量建立模型。算法 将是开源的，以允许机构验证它们并将其应用于其个人护理环境。这个 提案得到了来自质量度量设计、信息学、高级自然语言处理、 CDS设计和定性研究方法。耶鲁大学结果研究和研究中心的设施 评估，设计和评估国家质量衡量标准，并可访问计算资源 需要完成研究目标以及耶鲁大学电子健康记录来验证模型是主要的优势 应用程序的。拟议的指导性研究期间将支持Khera博士的医学培训 信息学，先进的分析工具，如自然语言处理，和定性研究方法。体验和 在此期间获得的技能将支持Khera博士过渡到他计划领导的独立 多机构协作，以评估自动化工具在衡量和改进 心衰患者的医疗服务质量。该提案附带的职业发展计划旨在 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。