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: 9248768
• 负责人: Thomas Lasko
• 金额: $ 35.17万
• 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 2019-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9248768
• 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; Health; Hemorrhage; ICD-9; Imagery; Institution; Intravenous; Laboratories; Learning; Left; Measurement; Measures; Medical; Medical History; Medicine; Methods; Online Systems; Operative Surgical Procedures; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Population; Procedures; Provider; Research; Resolution; Role; Schedule; Silicon; Techniques; Test Result; Testing; Text; Time; TimeLine; Work; base; clinical care; clinical practice; cognitive process; cognitive task; design; experience; hemodynamics; improved; innovation; iterative design; medical specialties; member; statistics; support tools; tool; trend

 DESCRIPTION (provided by applicant): 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 clnical care, increasing its efficiency, effectiveness, and quality.

 描述（由申请人提供）：该项目的目标是使用数据科学/大数据社区的尖端方法，为复杂的临床数据模式提供快速可解释的可视化，使临床医生能够快速回答他们每天多次面临的选定临床问题。表格数字数据和叙述性临床文本的传统电子健康记录显示格式使得很难同时识别多个变量之间的复杂关系和趋势，特别是如果这些关系随时间变化。我们以前已经开发了计算方法来识别医疗数据中临床重要的，随时间变化的关系和模式，在这个项目中，我们寻求扩展这些方法，并对发现的模式进行定制可视化，以支持特定的临床任务，这些任务涵盖了理解患者，程序和人群的范围。 具体来说，我们试图支持回答以下广泛的临床问题所涉及的认知任务：1）该患者的术前临床状态如何？2)这种手术的常用麻醉方法是什么？3）在明天将要接受手术的人群中，每个病人的敏锐度水平和复杂性是什么？我们从麻醉的临床领域中选择这些特定问题，因为该领域在机构之间具有相当一致的实践，但我们希望我们的解决方案可以轻松扩展到临床专业的类似问题。 该项目包括开发基于网络的工具，临床医生可以在日常临床实践中使用这些工具来回答这些问题。我们计划一种迭代开发方法，首先对工作流程和与这三个问题相关的信息需求进行定性用户研究，然后进行设计迭代，在每次迭代中包括最终用户临床医生的反馈。此外，我们将在设计时考虑采用的可能障碍，而不是 我们希望能够通过适当的设计决策来降低这些障碍。 如果成功，该项目将促进临床护理的日常实践，提高其效率，效果和质量。