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: 10454182
• 负责人: Matthew Michael Churpek
• 金额: $ 55.5万
• 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/10454182
• 关键词: 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; Gold; Health; Health system; Hospital Mortality; 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; Provider; Psychological reinforcement; Public Health; Recommendation; Research; Resources; Risk; Risk Factors; Savings; Sepsis; Source; Structure; Supervision; Testing; Time; Trees; United States National Institutes of Health; Work; base; case-based; clinical decision support; clinical diagnosis; clinical practice; clinical risk; common treatment; 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; sepsis induced ARDS; 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.

项目总结