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: 10470304
• 负责人: Tyler J Loftus
• 金额: $ 16.27万
• 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/10470304
• 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 model; design; experience; health record; indexing; individual patient; innovation; inpatient surgery; machine learning model; 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.

抽象的 该提案的关键目的是为候选人提供开发必要的培训和资源 使用电子健康记录的大规模，多机构信息研究的专业知识和经验 数据和机器学习以开发临床决策支持工具。该提议建立在候选人的 长期职业目标是成为具有设计和实施专业知识的独立外科医生科学家 机器学习系统以增加临床决策。为了实现这一目标，候选人和 导师提出了术后“患者敏锐度”的系统投资（即重病和死亡的风险） 和“护理强度”（即生命体征和实验室测量的分类目的和频率）。后 重大手术，未对准患者的敏锐度和护理强度会导致可预防的伤害和不适当 资源使用，仅在美国，每年都会影响大约1500万个住院手术。当高品质时 患者接受低强度的护理，术后并发症可以发展为危重疾病和心脏骤停。 向低强度患者提供高强度护理的价值较低，可能会因不必要而造成伤害 治疗。由于没有经过验证的统一，很难系统地解决这些问题 “护理强度”定义。该应用的总体目的是了解护理决策的强度 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 生成精度，自主决策支持工具。该应用程序的中心假设是 不适当的术后护理强度是常见的，可预测的，并且与短期和增加 长期死亡率，发病率和医院费用。这项工作的理由是整合电子健康 记录数据，机器学习和临床领域专业知识，提供了了解术后的机会 护理决策的强度和发展决策支持工具，能够优化临床结果和 资源使用。该建议的具体目的是（1）发展和验证术后护理强度 定义，（2）开发和验证可解释的可行的敏锐度评估，以阐明决策空间， （3）识别和预测护理表型的术后强度。拟议的研究很重要 因为它解决了每年影响数百万患者的问题，并且可能与 可预防的危害和次优的资源使用。这种方法是创新的，因为候选人和导师 没有意识到任何先前试图对护理的术后进行分类和调整的尝试，并了解 接受不足或过度护理的患者的表型和特征。在奖励期内， 候选人将申请NIH-R01调查员因前瞻性临床实施的授予 可解释的，可行的决策支持工具，结合了经过验证的护理定义强度并知道 从表型聚类中获得，最初是在沉默的数据收集期间，然后在 为哪些临床医生提供了模型输出和临床上可行的建议。