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

项目总结 在内外科病房住院的成人患者中，多达5%的患者出现临床恶化，需要 重症监护室。医疗差错在恶化事件发生之前很常见，包括延误和误判 识别、诊断和治疗，而这些错误导致发病率和死亡率增加。因此，它 改善高危病房患者的护理以减少可预防的住院死亡至关重要。 目前试图降低恶化死亡率的范例有几个局限性。第一, 大多数用于识别高危患者的早期预警评分仅基于生命体征，而且有限 精确度。临床记录是一种未得到充分利用的、丰富的信息来源，占电子病历的近80% 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危险因素 机器学习模型的注释，以提高现有工具的精确度。第二，当前的预警得分 只告诉临床医生患者处于高危状态，但不提供有关临床情况的信息 导致病人病情恶化。这会导致诊断和治疗错误，从而导致更糟糕的患者 结果。开发工具以提高高危病房患者的诊断准确性可能会导致 医疗差错、降低成本和改善结果。第三，恶化的初步治疗决定 病人是由护理危重病人经验有限的临床医生制作的，这可能会导致延误。 有可能挽救生命的疗法。通过利用大型、细粒度、多中心数据集，算法可以预测 可以开发患者应该接受的治疗，从而产生早期的、有针对性的、可能挽救生命的治疗。 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 可预防的恶化死亡。该项目的总体目标是开发一个临床决策支持系统 用于识别、诊断和治疗有高恶化风险的患者的工具。这一目标将是 追求以下三个具体目标：1)开发机器学习模型以识别高危患者 使用结构化数据和非结构化临床记录来预测恶化；2)开发模型来预测 导致恶化事件的诊断和应提供的可能挽救生命的治疗 提供给高危患者；3)开发具有图形用户界面的临床决策支持工具 目标1和目标2的模型通过以用户为中心的设计原则，然后测试其有效性、效率和 基于案例的模拟研究中的用户满意度。这项研究具有创新性，因为它将利用自然语言处理， 强化学习、可解释机器学习和多任务迁移学习方法。这个 拟议的研究具有重要意义，因为它将为临床医生提供强大的新工具，可以 在电子病历中实施，以识别、诊断和为高危患者提供治疗建议。这 将导致提供早期的个性化护理，以减少因病情恶化而可预防的死亡。

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.