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）正在为医疗保健提供革命性的机会。但他们 他们带来了许多负担--有些是预料之中的，有些是意料之外的。医学文献是 充满了关于如何在病人记录中找到重要信息的抱怨，部分原因是 部分原因是由于低价值数据的扩散，即所谓的“笔记膨胀”。 其他投诉集中在临床警报应用程序上，这些应用程序已被证明会发出更多的假警报 而不是真实的，导致警觉疲劳，导致临床医生错过重要的警告。回用 用于研究的EHR数据也很困难。在撰写本文时，多个团体（ACT，eMERGE，All of Us，N3 C和 其他人）正致力于使用自动识别COVID-19（SARS Var-2感染表型）患者 EHR数据-一项任务，应该是微不足道的，但显然不是由于次优的EHR内容和组织。 广泛的数据努力并没有成功地解决这些关于电子病历的投诉。 所提出的工作的前提是，有关于临床医生的想法，这是不容易的信息 如果它可以以结构化的、可计算的方式添加到EHR中， 可以进行改进。我们把这些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，为什么选择特定的测试或治疗，为什么治疗 停止了。拟议的工作将探索如何表示患者数据与此增加的知识，以更好地 了解必须向EHR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- 本发明提供了一种用于改进EHR中的患者数据的导航的方法。 该项目将包括三个连续步骤。首先，我们开发了一种方法来分解信息， 患者记录，包括来自叙述性文本（注释）的信息，到个体医疗实体（例如 问题、测试和药物）来创建患者数据集（PDS）。接下来，我们将建立在我们的初步 研究临床护理背景的概念（患者发现和状况，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 它们背后（例如将问题与一组可能的原因联系起来，旨在区分 原因，以及基于测试结果选择的治疗），以创建患者特定的知识库 （PSKB）。最后，我们将通过创建PDS和 为住院医生在诊所看到的实际病人提供PKSB，并为住院医生提供 导航工具，利用知识库，以帮助他们更好地了解他们的病人的情况。 评估将包括了解各种知识增进活动的努力和价值， 使用的方法和居民对导航工具的可用性和实用性的满意度。

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