利用机器学习、工作流程分析和模拟降低学术医院神经外科再入院率的计划。

A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation.

机构信息

Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.

Neuroinformatics Laboratory, Department of Neurosurgery, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.

出版信息

Appl Clin Inform. 2024 May;15(3):479-488. doi: 10.1055/s-0044-1787119. Epub 2024 Jun 19.

DOI:10.1055/s-0044-1787119

PMID:38897230

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11186699/

Abstract

BACKGROUND

Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of neurosurgical readmissions do not report factors related to clinical implementation.

OBJECTIVES

Train individual predictive models with good performance (area under the receiver operating characteristic curve or AUROC > 0.8), identify potential interventions through semi-structured interviews, and demonstrate estimated clinical and financial impact of these models.

METHODS

Electronic health records were utilized with five ML methodologies: gradient boosting, decision tree, random forest, ridge logistic regression, and linear support vector machine. Variables of interest were determined by domain experts and literature. The dataset was split divided 80% for training and validation and 20% for testing randomly. Clinical workflow analysis was conducted using semi-structured interviews to identify possible intervention points. Calibrated agent-based models (ABMs), based on a previous study with interventions, were applied to simulate reductions of the 30-day readmission rate and financial costs.

RESULTS

The dataset covered 12,334 neurosurgical intensive care unit (NSICU) admissions (11,029 patients); 1,903 spine surgery admissions (1,641 patients), and 2,208 traumatic brain injury (TBI) admissions (2,185 patients), with readmission rate of 13.13, 13.93, and 23.73%, respectively. The random forest model for NSICU achieved best performance with an AUROC score of 0.89, capturing potential patients effectively. Six interventions were identified through 12 semi-structured interviews targeting preoperative, inpatient stay, discharge phases, and follow-up phases. Calibrated ABMs simulated median readmission reduction rates and resulted in 13.13 to 10.12% (NSICU), 13.90 to 10.98% (spine surgery), and 23.64 to 21.20% (TBI). Approximately $1,300,614.28 in saving resulted from potential interventions.

CONCLUSION

This study reports the successful development and simulation of an ML-based approach for predicting and reducing 30-day hospital readmissions in neurosurgery. The intervention shows feasibility in improving patient outcomes and reducing financial losses.

摘要

背景

预测 30 天内的医院再入院率对于改善患者预后、优化资源配置和实现财务节约至关重要。现有的报告神经外科再入院预测机器学习（ML）模型开发的研究并未报告与临床实施相关的因素。

目的

使用电子病历，使用五种 ML 方法（梯度提升、决策树、随机森林、岭逻辑回归和线性支持向量机）训练具有良好性能（接收者操作特征曲线下面积或 AUROC>0.8）的个体预测模型，通过半结构化访谈确定潜在的干预措施，并展示这些模型的估计临床和财务影响。

方法

结果

数据集涵盖了 12334 例神经外科重症监护病房（NSICU）入院（11029 例患者）、1903 例脊柱手术入院（1641 例患者）和 2208 例创伤性脑损伤（TBI）入院（2185 例患者），再入院率分别为 13.13%、13.93%和 23.73%。随机森林模型在 NSICU 中表现最佳，AUROC 得分为 0.89，有效捕捉潜在患者。通过 12 次半结构化访谈确定了 6 项干预措施，目标是术前、住院期间、出院和随访阶段。校准后的基于代理的模型（ABM）模拟了中位再入院率的降低，结果分别为 13.13%至 10.12%（NSICU）、13.90%至 10.98%（脊柱手术）和 23.64%至 21.20%（TBI）。潜在干预措施可节省约 1300614.28 美元。