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: 10214973
• 负责人: Rohan Khera
• 金额: $ 19.35万
• 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/10214973
• 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; 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/antagonist; mortality; open source; patient oriented; patient subsets; 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600万美国成年人，住院率高，死亡率近50%， 诊断后5年。这些患者中有近一半患有收缩期HF， 经证明可降低该亚组患者住院和死亡风险的选择。评价 这些疗法的适当利用目前仅限于对手动提取的 参与质量改进注册的有限数量医院的患者记录。这些手动 抽象策略并没有提供实时改善护理的机会，即使在医院也是如此。 在质量改进方面，只有1/5的合格HF患者接受了所有一线循证医学治疗， 治疗。在这个以病人为导向的指导职业发展奖的建议，博士罗汉凯拉建议， 利用电子健康记录（EHR）中无处不在的医疗记录数字化， 在HF中充分利用循证医学治疗。他建议使用一个大型的，公众可以访问的， 去识别EHR数据库，以开发和验证使用基于深度学习的自然语言的算法 在住院HF患者的非结构化临床文档中进行NLP处理，以识别 收缩期HF（目标#1）。他将让临床医生设计基于共识的算法， HF治疗的禁忌症，作为EHR中的算法开发（目标#2）。最后，他将构建 原型临床决策支持（CDS）工具，使用 算法和评估潜在的实施策略，使用定性评估的反馈， 临床医生和患者（目标#3）。虽然提出作为一种战略，以评估个别病人的护理质量， 所提出的研究还将模拟用于HF的全自动电子临床质量测量。的算法 将开放源代码，允许机构验证并将其应用于个人护理环境。的 该提案得到了来自质量测量设计，信息学，高级NLP， CDS设计和定性研究方法。耶鲁成果研究中心的设施和 评价，设计和评价国家质量措施，并可使用计算资源 完成研究目标所需的能力以及耶鲁EHR验证模型的能力是主要优势 应用程序的。拟议的指导研究期将支持凯拉博士在医学领域的培训， 信息学，先进的分析工具，如NLP和定性研究方法。的经验和 在此期间获得的技能将支持凯拉博士向独立过渡，他计划在那里领导 多机构合作，以评估自动化工具在衡量和改善 HF的医疗质量。建议书所附的职业发展计划旨在 支持Khera博士的长期职业目标，成为信息学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。