Aligning Patient Acuity with Intensity of Care after Surgery

使患者的敏锐度与术后护理强度保持一致

• 批准号: 10266829
• 负责人: Tyler J Loftus
• 金额: $ 15.98万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-21 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266829
• 关键词: Address; Affect; Attention; Award; Caring; Cessation of life; Characteristics; Clinical; Complication; Consumption; Critical Illness; Data; Data Collection; Decision Making; Destinations; Electronic Health Record; Florida; Frequencies; General Hospitals; Goals; Heart Arrest; Hospital Costs; Hospitals; Hour; Informatics; Inpatients; Institution; Intensive Care Units; Investigation; Judgment; Knowledge; Label; Laboratories; Lead; Location; Machine Learning; Manuals; Measurement; Mentors; Modeling; Monitor; Morbidity - disease rate; Operative Surgical Procedures; Outcome; Output; Patient risk; Patients; Performance; Phenotype; Physiological; Postoperative Complications; Postoperative Period; Provider; Recommendation; Research; Research Personnel; Resources; Risk; Scientist; Surgeon; System; Testing; Time; Training; Triage; United States National Institutes of Health; Universities; Work; adjudicate; career; clinical decision support; clinical decision-making; clinical implementation; clinical phenotype; clinically actionable; clinically relevant; cost; deep learning; design; experience; health record; indexing; individual patient; innovation; inpatient surgery; mortality; patient population; patient subsets; prospective; response; support tools; tertiary care; unnecessary treatment; ward

ABSTRACT A key aim of this proposal is to equip the candidate with the training and resources necessary to develop expertise and experience in large-scale, multi-institutional informatics research using electronic health record data and machine learning to develop clinical decision-support tools. This proposal builds toward the candidate’s long-term career goal of becoming an independent surgeon-scientist with expertise in design and implementation of machine learning systems to augment clinical decision-making. To accomplish this goal, the candidate and mentors propose a systematic investigation of postoperative ‘patient acuity’ (i.e., risk for critical illness and death) and ‘intensity of care’ (i.e., triage destination and frequency of vital sign and laboratory measurements). After major surgery, misaligned patient acuity and intensity of care can lead to preventable harm and inappropriate resource use, affecting approximately 15 million inpatient surgeries annually in the US alone. When high-acuity patients receive low-intensity care, postoperative complications can progress to critical illness and cardiac arrest. Providing high-intensity care to low-acuity patients has low value and may cause harm through unnecessary treatments. It is difficult to address these problems systematically because there is no validated, unifying ‘intensity of care’ definition. The overall objective of this application is to understand intensity of care decision spaces in surgical patients and match them to clinical phenotypes and outcomes, leveraging this knowledge to generate precise, autonomous decision-support tools. The central hypothesis of this application is that inappropriate postoperative intensity of care is common, predictable, and associated with increased short- and long-term mortality, morbidity, and hospital costs. The rationale for this work is that integrating electronic health record data, machine learning, and clinical domain expertise offers opportunities to understand postoperative intensity of care decisions and develop decision-support tools capable of optimizing clinical outcomes and resource use. The specific aims of this proposal are to (1) develop and validate postoperative intensity of care definitions, (2) develop and validate interpretable, actionable acuity assessments that elucidate decision spaces, and (3) identify and predict postoperative intensity of care phenotypes. The proposed research is significant because it addresses a problem that affects millions of patients annually and is associated with potentially preventable harm and suboptimal resource use. The approach is innovative because the candidate and mentors are unaware of any prior attempts to classify and adjudicate postoperative intensity of care and understand the phenotypes and characteristics of patients receiving insufficient or excessive care. During the award period, the candidate will apply for an NIH-R01 investigator-initiated award for the prospective clinical implementation of an interpretable, actionable decision support tool incorporating validated intensity of care definitions and knowledge garnered from phenotype clustering, initially in a silent data collection period followed by a live period during which clinicians are provided with model outputs and clinically actionable recommendations.

摘要 该建议的一个主要目的是为候选人提供必要的培训和资源， 利用电子健康记录进行大规模、多机构信息学研究的专业知识和经验 数据和机器学习来开发临床决策支持工具。这一提议有助于候选人的 长期职业目标是成为一名独立的外科医生-科学家，拥有设计和实施方面的专业知识 机器学习系统来增强临床决策。为了实现这一目标，候选人和 指导者提出了术后“患者敏锐度”的系统研究（即，重大疾病和死亡风险） 和“护理强度”（即，分诊目的地和生命体征和实验室测量的频率）。后 大手术、不一致的患者敏锐度和护理强度可能导致可预防的伤害和不适当的 资源使用，仅在美国每年就影响约1500万例住院手术。当高敏度 患者接受低强度护理，术后并发症可发展为危重病和心脏骤停。 为低敏度患者提供高强度护理的价值很低，可能会通过不必要的治疗造成伤害。 治疗。很难系统地解决这些问题，因为没有有效的、统一的 “护理强度”的定义。本申请的总体目标是了解护理强度决策 手术患者的空间，并将其与临床表型和结果相匹配，利用这些知识， 生成精确的自主决策支持工具。本申请的中心假设是， 不适当的术后护理强度是常见的、可预测的，并且与短期和长期护理的增加有关。 长期死亡率、发病率和住院费用。这项工作的基本原理是， 记录数据、机器学习和临床领域专业知识为了解术后 护理决策的强度，并开发能够优化临床结果的决策支持工具， 资源利用。该提案的具体目标是（1）制定和验证术后护理强度 定义，（2）开发和验证阐明决策空间的可解释的、可操作的敏锐度评估， 和（3）识别和预测术后护理强度表型。所提出的研究是有意义的 因为它解决了一个每年影响数百万患者的问题， 可预防的危害和次优的资源利用。这种方法是创新的，因为候选人和导师 不知道之前曾尝试对术后护理强度进行分类和判定， 接受不充分或过度护理的患者的表型和特征。在颁奖期间， 候选人将申请NIH-R 01认证机构发起的奖项，用于前瞻性临床实施 可解释、可操作的决策支持工具，包含经验证的护理强度定义和知识 从表型聚类中获得，最初是在沉默的数据收集期，然后是在 向这些临床医生提供模型输出和临床上可行的建议。