Aligning Patient Acuity with Resource Intensity after Major Surgery

大手术后使患者的敏锐度与资源强度保持一致

基本信息

批准号：
10635798
负责人：
Tyler J Loftus
金额：
$ 34.88万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2027-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10635798
关键词：
Admission activity Affect Architecture Artificial Intelligence Awareness Caring Classification Clinical Clinical Trials Cluster randomized trial Code Computer software Computers Data Decision Making Decision Support Systems Electronic Health Record Engineering Ensure Equity Face Failure General Ward Goals Health Home Hospitalization Hospitals Human Institution Intensive Care Units Investigation Learning Medical Modeling Monitor Morbidity - disease rate Operative Surgical Procedures Outcome Patient Discharge Patient Transfer Patients Performance Postoperative Period Probability Prospective Studies Provider Recommendation Reproducibility Research Resources Risk Risk Adjustment Series Specialist Stream Surgeon System Testing Time Triage Trust Uncertainty United States Validation Work adverse event risk biomedical informatics clinical implementation cost data sharing deep learning deep learning model design experimental study federated learning hospital readmission implementation science inpatient surgery interoperability mortality multimodality novel open source patient health information patient oriented patient privacy practice setting prospective sociodemographic group success usability user-friendly ward

项目摘要

Project Summary The broad, long-term objective of this application is to generate an efficient, effective decision-support system to augment postoperative triage, transfer, and discharge decisions that affect more than 15 million patients in the United States annually. Evidence from single-institution studies suggests that postoperative overtriage of low acuity patients to intensive care units (ICUs) is associated with low value of care (outcomes/costs) compared with general ward admission, and that undertriage of high acuity patients to general wards is associated with increased mortality. These associations require validation externally and prospectively. In addition, further investigation is needed to determine whether there are similar, identifiable misalignments between patient acuity and resource intensity occurring throughout postoperative hospital admission and at the time of hospital discharge. Our central hypothesis is that aligning automated, data-driven patient acuity assessments with postoperative resource intensity using explainable, fair, uncertainty-aware deep learning models will be associated with decreased mortality and increased value of care. We will test our central hypothesis by performing three sets of related but independent experiments. First, we will externally validate an interoperable version of our postoperative triage classification system, initially using retrospective data at 42 hospitals across four institutions, then performing similar analyses with retrospective data on a federated learning platform, and finally using prospective data from 15 hospitals at two institutions. Second, we will generate continuous postoperative patient acuity assessments with novel DL architectures using multicenter, multimodal (including clinical notes), retrospective EHR data at three hospitals within a single institution. Third, we will critically evaluate and optimize model certainty and fairness using retrospective data at 43 hospitals across four institutions, generate an EHR-embedded decision-support system, and perform prospective decision support usability testing and optimization at two institutions. The proposed research is intended to produce a validated, interoperable postoperative triage classification system, foundational evidence for generating continuous streams of postoperative transfer and discharge recommendations, a postoperative triage decision support system ready for clinical implementation, and open-source software for optimizing deep learning certainty and fairness. Achieving these outcomes would increase the probability of success for automated, real-time postoperative triage decision-support in subsequent clinical trials, and the ultimate goal of augmenting personalized, patient-centered decision making in surgery.

项目摘要该应用程序的广泛长期目标是生成一个高效，有效的决策系统增加影响超过1500万患者的术后分类，转移和出院决策每年美国。单机构研究的证据表明，术后过度划分低。敏锐的患者到重症监护病房（ICU）与较低的护理价值（成果/成本）相比有关随着病房一般的入院，对一般病房的高敏锐患者的训练与死亡率增加。这些关联需要外部和前瞻性验证。另外，进一步需要进行调查以确定患者敏锐度之间是否存在相似的，可识别的未对准术后医院入院和医院时发生的资源强度发生释放。我们的中心假设是，将自动数据驱动的患者敏锐度评估与使用可解释，公平，不确定性的深度学习模型的术后资源强度将是与死亡率下降和护理价值增加有关。我们将通过执行三组相关但独立的实验。首先，我们将在外部验证可互操作我们的术后分类分类系统的版本，最初使用42个医院的回顾性数据四个机构，然后在联合学习平台上使用回顾性数据进行类似的分析，最终使用来自两个机构的15家医院的潜在数据。其次，我们将产生连续的使用多中心，多模式的新型DL体系结构进行术后患者敏锐度评估（包括临床注释），单个机构内三家医院的回顾性EHR数据。第三，我们将进行批判性评估并使用四家机构的43家医院的回顾性数据优化模型的确定性和公平性，产生一个EHR包裹的决策支持系统，并执行潜在的决策支持可用性在两个机构进行测试和优化。拟议的研究旨在产生经过验证的，互操作术后分类分类系统，产生连续的基本证据术后转移和出院建议流，术后分类决策支持准备临床实施的系统和开源软件，以优化深度学习确定性和公平。实现这些结果将增加自动实时的成功可能性在随后的临床试验中，术后分类决策支持，也是增强的最终目标个性化，以患者为中心的手术决策。