Monahan Ann Corneille, Feldman Sue S
Department of Epidemiology & Public Health, School of Public Health, University College Cork, Cork, Ireland.
Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL, United States.
JMIR Med Inform. 2021 Sep 16;9(9):e30022. doi: 10.2196/30022.
Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding.
The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding.
We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients' imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies.
Potential biases were found in most studies, which suggested that each model's predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments.
There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.
急诊科滞留和医院出院受阻是急诊科拥挤的主要原因，并且已被确凿地证明与不良患者结局以及对患者安全的重大威胁相关。当患者由于功能失调的转诊或床位分配流程而延迟或受阻于转出急诊科时，就会发生滞留。用于估计此类事件发生概率的预测模型可能有助于减少或预防急诊科滞留和医院出院受阻，从而减轻急诊科拥挤状况。
本研究的目的是识别和评估利用院前成年患者数据并旨在解决急诊科拥挤问题的医院入院预测模型的预测性能、预测指标效用、模型应用和模型效用。
我们检索了多个数据库，查找从数据库建立至2019年9月30日期间评估利用院前患者数据和回归分析预测成年患者即将住院的模型的研究。我们使用PROBAST（预测模型偏倚风险评估工具）和CHECKS（预测建模研究系统评价的关键评估和数据提取清单）对研究进行严格评估。
在大多数研究中发现了潜在偏倚，这表明每个模型的预测性能都需要进一步研究。我们发现，特定的院前患者数据有助于识别需要住院的患者。生物标志物预测指标可能会为模型增添更高的价值和优势。然而，需要注意的是，没有模型与信息系统或工作流程集成，没有作为电子设备独立运行，也没有在护理环境中实时运行。有几个模型可以在无数字设备的情况下在护理现场实时使用，这将使其适用于低技术或无电环境。
院前入院预测模型在改善患者护理和医院运营方面具有巨大潜力。患者数据可被用作预测指标以及数据驱动的可操作工具，以识别可能需要紧急住院的患者，并减少急诊科的患者滞留和拥挤情况。预测模型可用于证明更早的患者入院和护理的合理性，以降低发病率和死亡率，并且利用生物标志物预测指标的模型具有额外优势。
