Identification, Extraction and Display of Clinical Data Patterns with Application to Anesthesia Workflows

临床数据模式的识别、提取和显示及其在麻醉工作流程中的应用

• 批准号: 9420613
• 负责人: Thomas Lasko
• 金额: $ 40.68万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9420613
• 关键词: Address; Adoption; Anesthesia procedures; Anesthetics; Apgar Score; Big Data; Caring; Characteristics; Charge; Clinical; Clinical Data; Code; Communities; Complex; Computing Methodologies; Data; Data Display; Data Science; Development; Drug Delivery Systems; Effectiveness; Electronic Health Record; Ensure; Face; Feedback; Fortune; Goals; Hemorrhage; ICD-9; Imagery; Institution; Intravenous; Laboratories; Learning; Measurement; Measures; Medical; Medical History; Medicine; Methods; Operative Surgical Procedures; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Population; Procedures; Provider; Research; Research Project Grants; Resolution; Risk; Role; Schedule; Seeds; Silicon; Techniques; Test Result; Testing; Text; Time; TimeLine; Visualization software; Work; clinical care; clinical practice; cognitive process; cognitive task; cohort; design; experience; hemodynamics; improved; innovation; iterative design; medical specialties; member; statistics; support tools; tool; trend; web-based tool

The goal of this project is to use cutting-edge methods from the data science/Big Data community to provide rapidly interpretable visualizations of complex clinical data patterns that allow clinicians to quickly answer selected clinical questions that they face many times a day. The traditional Electronic Health Record display formats of tabular numeric data and narrative clinical text make it difficult to identify complex relationships and trends among several variables at once, particularly if those relationships change over time. We have previously developed computational methods to identify clinically important, time-changing relationships and patterns among medical data, and in this project we seek to extend those methods and produce tailored visualizations of the discovered patterns to support specific clinical tasks that cover the spectrum of understanding patients, procedures, and populations. Specifically, we seek to support the cognitive tasks involved in answering the following broad clinical questions: 1) What is the preoperative clinical status of this patient? 2) What are the common anesthetic approaches for this surgical procedure? And 3) What is the acuity level and complexity of each patient in the population of those who will be operated on tomorrow? We selected these specific questions from the clinical domain of anesthesia because that domain has fairly consistent practices between institutions, but we intend for our solutions to be easily extendable to analogous questions across clinical specialties. This project includes developing web-based tools that clinicians can use to answer these questions during their daily clinical practice. We plan an iterative development approach, starting with qualitative user studies of workflows and information needs relevant to the three questions, and followed by design iterations that include end-user clinician feedback at each iteration. Additionally, we will consider at design time possible barriers to adoption, rather than leaving this until deployment time, and we expect to be able to lower those barriers with appropriate design decisions. If successful, this project will facilitate the daily practice of clinical care, increasing its efficiency, effectiveness, and quality.

该项目的目标是使用数据科学/大数据社区的尖端方法， 复杂临床数据模式的快速可解释的可视化，使临床医生能够快速回答 选择他们每天要面对很多次的临床问题。传统的电子健康记录显示 表格数字数据和叙述性临床文本的格式使得难以识别复杂的关系 以及几个变量之间的趋势，特别是如果这些关系随着时间的推移而变化。我们有 以前开发的计算方法，以确定临床上重要的，随时间变化的关系， 在这个项目中，我们试图扩展这些方法，并为我们的医疗数据提供定制的服务。 可视化所发现的模式，以支持特定的临床任务， 了解患者、程序和人群。 具体来说，我们寻求支持参与回答以下广泛临床问题的认知任务。 问题：1）该患者术前的临床状况如何？2)常用麻醉药有哪些 这种手术的方法吗以及3）在治疗中每个患者的敏锐度水平和复杂性是什么？ 明天要做手术的人有多少？我们从临床上选择了这些特定的问题， 麻醉领域，因为该领域在机构之间有相当一致的实践，但我们打算 我们的解决方案可以很容易地扩展到跨临床专业的类似问题。 该项目包括开发基于网络的工具，临床医生可以用来回答这些问题， 日常临床实践。我们计划一个迭代的开发方法，从定性的用户研究开始 的工作流程和信息需求相关的三个问题，然后是设计迭代， 包括终端用户临床医生反馈。此外，我们将在设计时考虑可能的 采用的障碍，而不是将其留到部署时，我们希望能够降低这些障碍 合理的设计决策。 如果成功，该项目将促进临床护理的日常实践，提高其效率， 效率和质量。

项目成果

Visualization of aggregate perioperative data improves anesthesia case planning: A randomized, cross-over trial.

• DOI: 10.1016/j.jclinane.2020.110114
• 发表时间: 2021-03
• 期刊: Journal of clinical anesthesia
• 影响因子: 6.7
• 作者: Wanderer JP;Lasko TA;Coco JR;Fowler LC;McEvoy MD;Feng X;Shotwell MS;Li G;Gelfand BJ;Novak LL;Owens DA;Fabbri DV
• 通讯作者: Fabbri DV

A Perioperative Care Display for Understanding High Acuity Patients.

用于了解高危患者的围手术期护理展示。

• DOI: 10.1055/s-0041-1723023
• 发表时间: 2021
• 期刊: Applied clinical informatics
• 影响因子: 2.9
• 作者: Novak,LaurieLovett;Wanderer,Jonathan;Owens,DavidA;Fabbri,Daniel;Genkins,JulianZ;Lasko,ThomasA
• 通讯作者: Lasko,ThomasA