Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients

开发用于住院患者危重疾病识别、诊断和治疗的临床决策支持工具

• 批准号: 10683402
• 负责人: Matthew Michael Churpek
• 金额: $ 56.72万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 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 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683402
• 关键词: Acute Respiratory Distress Syndrome; Admission activity; Adult; Algorithms; Antibiotics; Caring; Cessation of life; Clinical; Complex; Congestive Heart Failure; Critical Care; Critical Illness; Data; Data Set; Decision Making; Deterioration; Diagnosis; Diagnostic; Early Diagnosis; Effectiveness; Electronic Health Record; Etiology; Event; Fatigue; Goals; Health; Health system; Hospital Mortality; Hospitalization; Hospitals; Hour; Human; Intensive Care; Laboratories; Lead; Learning; Life; Machine Learning; Manuals; Medical; Medical Errors; Mission; Modeling; Morbidity - disease rate; Natural Language Processing; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Prediction of Response to Therapy; Provider; Psychological reinforcement; Public Health; Recommendation; Research; Resources; Risk; Risk Factors; Sepsis; Source; Structure; Testing; Time; Trees; United States National Institutes of Health; Work; case-based; clinical decision support; clinical diagnosis; clinical practice; clinical risk; common treatment; convolutional neural network; cost outcomes; design; diagnostic accuracy; disability; discrete time; experience; graphical user interface; high risk; improved; improved outcome; innovation; iterative design; learning strategy; machine learning framework; machine learning method; machine learning model; mortality; multitask; novel; optimal treatments; personalized care; personalized intervention; predictive modeling; preventable death; recurrent neural network; satisfaction; simulation; structured data; support tools; tool; transfer learning; user centered design; ward

PROJECT SUMMARY Up to 5% of hospitalized adult patients on the medical-surgical wards develop clinical deterioration requiring intensive care. Medical errors are common before deterioration events, including delays and misjudgments in identification, diagnosis, and treatment, and these errors lead to increased morbidity and mortality. Therefore, it is critically important to improve the care of high-risk ward patients to decrease preventable in-hospital deaths. The current paradigm for attempting to decrease mortality from deterioration has several limitations. First, most early warning scores designed to identify high-risk patients are based only on vital signs and have limited accuracy. Clinical notes are an underutilized, rich source of information comprising nearly 80% of electronic health record (EHR) data. Natural language processing (NLP) can extract important risk factors from clinical notes for machine learning models to improve accuracy over existing tools. Second, current early warning scores only tell clinicians that a patient is at high risk but provide no information regarding what clinical condition is causing a patient’s deterioration. This leads to diagnostic and treatment errors, which results in worse patient outcomes. Developing tools to enhance diagnostic accuracy for high-risk ward patients could lead to fewer medical errors, decreased costs, and improved outcomes. Third, the initial treatment decisions for deteriorating patients are made by clinicians with limited experience caring for critically ill patients, which can result in delays of potentially life-saving therapies. By utilizing a large, granular, multicenter dataset, algorithms to predict the treatments a patient should receive can be developed, resulting in early, targeted, potentially life-saving therapy. The long-term goal is to develop and implement clinically useful decision support tools to decrease preventable death from deterioration. The overall objective of this project is to develop a clinical decision support tool for the identification, diagnosis, and treatment of patients at high risk of deterioration. This objective will be pursued in the following three specific aims: 1) Develop machine learning models to identify patients at high risk of deterioration using both structured data and unstructured clinical notes; 2) Develop models to predict the diagnosis that is causing the deterioration event and the potentially life-saving treatments that should be provided to high-risk patients; 3) Develop a clinical decision support tool with a graphical user interface incorporating the models from Aims 1 and 2 via user-centered design principles and then test its effectiveness, efficiency, and user satisfaction in a case-based simulation study. This research is innovative because it will utilize NLP, reinforcement learning, interpretable machine learning, and multi-task transfer learning approaches. The proposed research is significant because it will provide clinicians with powerful new tools that can be implemented in the EHR to identify, diagnose, and make treatment recommendations for high-risk patients. This will result in the delivery of early, personalized care to decrease preventable death from deterioration.

项目摘要 在医学手术病房中，多达5％的住院的成年患者发展了临床确定 重症监护。医疗错误在定义事件之前很常见，包括延迟和错误判断 识别，诊断和治疗以及这些错误导致发病率和死亡率增加。因此，它 对于改善高风险病房患者的护理以减少可预防的院内死亡至关重要。 试图降低恶化死亡率的当前范式有几个局限性。第一的， 旨在识别高危患者的大多数预警评分仅基于生命体征，并且有限 准确性。临床笔记是一个未充分利用的，丰富的信息来源，填写了近80％的电子 健康记录（EHR）数据。自然语言处理（NLP）可以从临床中提取重要的风险因素 机器学习模型的注释，以提高现有工具的准确性。第二，当前的预警得分 仅告诉临床医生，患者处于高风险，但没有提供有关哪种临床状况的信息 导致患者的决心。这导致诊断和治疗错误，导致患者较差 结果。开发工具以提高高风险病房患者的诊断准确性可能会导致更少 医疗错误，成本降低并改善了结果。第三，确定的最初治疗决策 患者是由经验有限的临床医生制造的，照顾重症患者，这可能导致延迟 潜在的挽救生命的疗法。通过使用大型，颗粒状的多中心数据集，算法来预测 患者应接受的疗法可以开发，从而导致早期，有针对性的，潜在的挽救生命的疗法。 长期目标是开发和实施临床上有用的决策支持工具以减少 可预防的死亡定义。该项目的总体目的是建立临床决策支持 识别，诊断和治疗的工具，以确定高风险。这个目标将是 在以下三个特定目标中追求：1）开发机器学习模型以识别高风险的患者 确定使用结构化数据和非结构化临床笔记； 2）开发模型以预测 导致定义事件的诊断和应提供的潜在挽救生命的治疗 对高危患者； 3）使用编码图形用户界面来开发临床决策支持工具 AIMS 1和2通过以用户为中心的设计原理的模型，然后测试其有效性，效率和 在基于病例的仿真研究中的用户满意度。这项研究具有创新性，因为它将利用NLP， 强化学习，可解释的机器学习和多任务转移学习方法。 拟议的研究很重要，因为它将为临床医生提供强大的新工具 在EHR中实施，以识别，诊断和为高危患者提出治疗建议。这 将导致提供早期的个性化护理，从而减少可预防的死亡从定义中减少。