Improving Electronic Health Record Usability and Usefulness with a Patient-Specific Clinical Knowledge Base

通过患者特定的临床知识库提高电子健康记录的可用性和实用性

• 批准号: 10155135
• 负责人: JAMES J CIMINO
• 金额: $ 19.38万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10155135
• 关键词: Address; Admission activity; Adoption; Back; COVID-19 patient; Caring; Clinic; Clinical; Complex; Custom; Data; Data Set; Databases; Diagnosis; Diagnostic; Diagnostic tests; Dictionary; Disease; Documentation; Electronic Health Record; Elements; Etiology; Evaluation; Evolution; Expert Systems; Fatigue; Frequencies; Healthcare; Human; Individual; Infection; Informatics; Information Resources; Intention; Journals; Knowledge; Laboratories; Link; Literature; Mainstreaming; Manuals; Measures; Medical; Medicine; Methodology; Methods; Names; Natural Language Processing; New England; Ontology; Patients; Pharmaceutical Preparations; Phenotype; Physicians; Procedures; Process; Publishing; Records; Reporting; Research; Semantics; Severe Acute Respiratory Syndrome; Signs and Symptoms; Source; Structure; Symptoms; Test Result; Testing; Text; Therapeutic; Thinking; Time; Work; Writing; base; clinical care; clinical data warehouse; clinical encounter; data access; design; diaries; experimental study; improved; intelligent personal assistant; interoperability; knowledge base; knowledge of results; satisfaction; structured data; success; tool; usability

Electronic health records (EHRs) are providing opportunities to revolutionize health care. However, they have brought with them a number of burdens – some expected and others unanticipated. The medical literature is replete with complaints about how important information in patient records is difficult to find, partly due to its absence and partly due to its obfuscation by a proliferation of low-value data in what is called “note bloat”. Other complaints focus on clinical alerting applications, which have proven to issue vastly more false alarms than true ones, leading to alert fatigue which results in clinicians missing the important warnings. Reuse of EHR data for research is also difficult. At this writing, multiple groups (ACT, eMERGE, All of Us, N3C and others) are working to automatically identify patients with COVID-19 (SARS Var-2 infection phenotype) using EHR data – a task that should be trivial, but clearly is not due to suboptimal EHR content and organization. Extensive effort to data has not succeeded in resolving these complaints about EHRs. The premise of the proposed work is that there is information about the clinicians’ thinking that is not readily available or is missing from the EHR and that if it can be added in a structured, computable way EHR improvements can follow. We refer to that information as the “why” of health care: why does the clinician think the patient has a sign or symptom, why is a particular test or treatment being chosen, why is a treatment being discontinued. The proposed work will explore way to represent patient data with this added knowledge to better understand what additional information must be added to the EHR, how the addition might be accomplished, and how the resulting knowledge base might be used. As a first step in usage, we will explore a knowledge- based method for improving the navigation of patient data in an EHR. The project will involve three sequential steps. First, we develop methods to break down the information in a patient record, including information from narrative text (notes), into individual medical entities (such as problems, tests and medications) to create patient data sets (PDSs). Next, we will build on our preliminary studies of the concepts of the clinical care context (patient findings and conditions, diagnostic tests and their results, and therapeutic plans) to add relationships between these entities that convey the clinical reasoning behind them (such as linking a problem to set of possible causes, a test intended to differentiate between the causes, and a treatment chosen on the basis of a test result) to create patient-specific knowledge bases (PSKBs). Finally, we will explore the practicality of creating PKSBs and their usability by creating PDSs and PKSBs for actual patients being seen by medical residents in clinic and providing the residents with a navigational tool that makes use of the knowledge base to help them better understand their patients’ cases. Evaluation will include an understanding of the effort and value of the various knowledge-enhancement methods to be used and the residents’ satisfaction with the usability and usefulness of the navigational tool.

电子健康记录（EHRS）正在提供革新医疗保健的机会。但是，他们有 与他们一起买了许多伯伦斯 - 有些人预期，有些则是意外的。医学文献是 替换有关患者记录中重要信息如何很难找到的投诉，部分原因是 缺席，部分原因是由于它在所谓的“ note bloat”中的低价值数据的繁殖而引起的。 其他投诉集中于临床警报应用程序，事实证明，这些应用程序发出更多的错误警报 比真实的，导致警报疲劳导致临床医生缺少重要警告。重用 EHR研究的数据也很困难。在撰写本文中，多个小组（ACT，Emerge，我们所有人，N3C和 其他）正在努力使用COVID-19患者（SARS VAR-2感染表型）自动识别 EHR数据 - 一项应该很琐碎的任务，但显然不是由于EHR的含量和组织次优。 对数据的广泛努力尚未成功解决有关EHR的这些投诉。 拟议作品的前提是有关临床医生思想的信息不容易 EHR可用或缺少，如果可以以结构化的，可计算的方式添加它 可以随后进行改进。我们将这些信息称为医疗保健的“原因”：临床为什么会认为 患者有症状或症状，为什么选择特定的测试或治疗，为什么治疗是 停产。拟议的工作将探索用这些附加知识来代表患者数据以更好的方式 了解必须在EHR中添加哪些其他信息，如何完成添加， 以及如何使用所产生的知识库。作为使用的第一步，我们将探索一个知识 - 基于改善EHR中患者数据导航的方法。 该项目将涉及三个顺序步骤。首先，我们开发了分解信息的方法 患者记录，包括来自叙事文本（注释）的信息， 问题，测试和药物）创建患者数据集（PDS）。接下来，我们将基于初步 研究临床护理环境概念（患者发现和状况，诊断测试及其 结果和理论计划）在传达临床推理的这些实体之间增加关系 在他们身后（例如将问题与一组可能的原因联系起来，旨在区分的测试 原因，以及根据测试结果选择的治疗方法）创建特定于患者的知识库 （PSKB）。最后，我们将通过创建PDS和 PKSB适用于医疗居民在诊所看到的实际患者，并为居民提供 使用知识库来帮助他们更好地了解患者的病例的导航工具。 评估将包括了解各种知识增强的努力和价值 要使用的方法以及居民对导航工具的可用性和实用性的满意度。