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: 10458471
• 负责人: JAMES J CIMINO
• 金额: $ 16.15万
• 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/10458471
• 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(EHR)正在为医疗保健带来革命性的变化。然而，他们有 随之而来的是一些负担--有些是意料之中的，有些是意想不到的。医学文献是 充斥着抱怨患者记录中的重要信息很难找到，部分原因是它 这在一定程度上是因为它被低价值数据的扩散所混淆，也就是所谓的“笔记膨胀”。 其他投诉集中在临床警报应用程序上，事实证明，这些应用程序发出的错误警报要多得多 导致警觉疲劳，从而导致临床医生错过重要的警告。重复使用 对于EHR数据的研究也很困难。在撰写本文时，多个组织(ACT、Emerge、Our All、N3C和 其他)正在努力使用以下工具自动识别新冠肺炎患者(SARS Var-2感染表型) 电子病历数据-这项任务应该很琐碎，但显然不是因为电子病历的内容和组织不够理想。 对数据的广泛努力并没有成功地解决这些关于电子健康记录的投诉。 拟议工作的前提是，有关于临床医生想法的信息不是很容易 是否可以通过结构化的、可计算的方式将其添加到EHR中 改进可以随之而来。我们将这一信息称为医疗保健的“为什么”：为什么临床医生认为 患者有体征或症状，为什么选择特定的检查或治疗，为什么选择治疗 停产。拟议的工作将探索用这些增加的知识来表示患者数据的方法，以更好地 了解必须向电子病历添加哪些附加信息、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，我们将探索一种知识-- 一种改进电子病历中患者数据导航的方法。 该项目将包括三个连续的步骤。首先，我们开发方法将信息分解为 患者记录，包括从叙述性文本(注释)到单个医疗实体的信息(例如 问题、测试和药物)来创建患者数据集(PDS)。接下来，我们将在我们初步的基础上 研究临床护理背景的概念(患者的发现和情况、诊断测试及其 结果和治疗计划)来添加这些实体之间的关系，以传达临床推理 它们背后的原因(例如将一个问题与一组可能的原因联系起来，一个旨在区分 原因和基于测试结果选择的治疗)来创建特定于患者的知识库 (PSKBS)。最后，我们将通过创建PDS和PSB来探索创建PKSB的实用性和可用性 为实际患者提供的PKSB由住院医师在临床上看到，并为住院医师提供 一种导航工具，利用知识库帮助他们更好地了解患者的病例。 评价将包括对各种知识增强的努力和价值的理解 使用的方法和居民对导航工具的可用性和有用性的满意度。

项目成果

Semantically oriented EHR navigation with a patient specific knowledge base and a clinical context ontology.

具有患者特定知识库和临床背景本体的面向语义的 EHR 导航。

• 发表时间: 2023
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Colicchio,TiagoK;Osborne,JohnD;DoRosario,ClementinoV;Anand,Ankit;Timkovich,NicholasA;Wyatt,MatthewC;Cimino,JamesJ
• 通讯作者: Cimino,JamesJ

Physicians' perceptions about a semantically integrated display for chart review: A Multi-Specialty survey.

医生对用于图表审查的语义集成显示的看法：多专业调查。

• DOI: 10.1016/j.ijmedinf.2022.104788
• 发表时间: 2022
• 期刊: International journal of medical informatics
• 影响因子: 4.9
• 作者: Colicchio,TiagoK;Liang,WayneH;Dissanayake,PavithraI;DoRosario,ClementinoV;Cimino,JamesJ
• 通讯作者: Cimino,JamesJ