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

• 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
• 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

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 该应用程序的广泛的长期目标是生成一个高效的决策支持系统， 增加术后分诊、转移和出院决定，影响美国1500多万患者。 美国每年。来自单机构研究的证据表明，术后过度分诊的低 急性期患者到重症监护室（ICU）与较低的护理价值（结局/成本）相关， 与普通病房入院，以及高急性病人的分流到普通病房与 增加死亡率。这些关联需要外部和前瞻性的验证。此外，进一步 需要进行调查以确定在患者的敏锐度之间是否存在类似的、可识别的错位。 在术后住院期间和住院时发生的资源紧张 放电我们的中心假设是，将自动化的、数据驱动的患者急性评估与 使用可解释、公平、不确定性感知的深度学习模型， 与降低死亡率和增加护理价值相关。我们将测试我们的中心假设， 进行三组相关但独立的实验。首先，我们将从外部验证一个可互操作的 我们的术后分诊分类系统版本，最初使用全国42家医院的回顾性数据 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， 最后使用来自两个机构的15家医院的前瞻性数据。第二，我们将持续 采用多中心、多模式（包括 临床笔记），回顾性EHR数据在三家医院在一个单一的机构。第三，我们将认真评估 并使用四个机构43家医院的回顾性数据优化模型的确定性和公平性， 生成嵌入EHR的决策支持系统，并进行前瞻性的决策支持可用性 在两个机构进行测试和优化。拟议的研究旨在产生一个有效的， 可操作的术后分流分类系统，用于生成连续 术后转移和出院建议流，术后分诊决策支持 可用于临床实施的系统，以及用于优化深度学习确定性和 公平。实现这些结果将增加自动化、实时 在随后的临床试验中，术后分诊决策支持，以及增加 个性化、以患者为中心的手术决策。