University Hospitals of North Midlands NHS Trust, Royal Stoke University Hospital, Newcastle Road, Stoke-on-Trent ST4 6QG, UK.
Department of Engineering, School of Digital, Technologies and Arts, Staffordshire University, Room B009A, Cadman Building, Stoke on Trent ST4 2DE, UK.
Int J Qual Health Care. 2021 Sep 29;33(3). doi: 10.1093/intqhc/mzab130.
Delays to the transfer of care from hospital to other settings represent a significant human and financial cost. This delay occurs when a patient is clinically ready to leave the inpatient setting but is unable to because other necessary care, support or accommodation is unavailable. The aim of this study was to interrogate administrative and clinical data routinely collected when a patient is admitted to hospital following attendance at the emergency department (ED), to identify factors related to delayed transfer of care (DTOC) when the patient is discharged. We then used these factors to develop a predictive model for identifying patients at risk for delayed discharge of care.
To identify risk factors related to the delayed transfer of care and develop a prediction model using routinely collected data.
This is a single centre, retrospective, cross-sectional study of patients admitted to an English National Health Service university hospital following attendance at the ED between January 2018 and December 2020. Clinical information (e.g. national early warning score (NEWS)), as well as administrative data that had significant associations with admissions that resulted in delayed transfers of care, were used to develop a predictive model using a mixed-effects logistic model. Detailed model diagnostics and statistical significance, including receiver operating characteristic analysis, were performed.
Three-year (2018-20) data were used; a total of 92 444 admissions (70%) were used for model development and 39 877 (30%) admissions for model validation. Age, gender, ethnicity, NEWS, Glasgow admission prediction score, Index of Multiple Deprivation decile, arrival by ambulance and admission within the last year were found to have a statistically significant association with delayed transfers of care. The proposed eight-variable predictive model showed good discrimination with 79% sensitivity (95% confidence intervals (CIs): 79%, 81%), 69% specificity (95% CI: 68%, 69%) and 70% (95% CIs: 69%, 70%) overall accuracy of identifying patients who experienced a DTOC.
Several demographic, socio-economic and clinical factors were found to be significantly associated with whether a patient experiences a DTOC or not following an admission via the ED. An eight-variable model has been proposed, which is capable of identifying patients who experience delayed transfers of care with 70% accuracy. The eight-variable predictive tool calculates the probability of a patient experiencing a delayed transfer accurately at the time of admission.
从医院到其他环境的护理交接延迟会造成巨大的人力和财务成本。当患者在临床方面已准备好离开住院环境，但由于其他必要的护理、支持或住宿条件无法满足而无法离开时，就会发生这种延迟。本研究旨在通过分析患者从急诊就诊后住院时常规收集的管理和临床数据，确定与患者出院时护理交接延迟（DTOC）相关的因素。然后，我们使用这些因素开发了一个预测模型，以识别有延迟出院风险的患者。
确定与护理交接延迟相关的风险因素，并使用常规收集的数据开发预测模型。
这是一项单中心、回顾性、横断面研究，纳入了 2018 年 1 月至 2020 年 12 月期间在英国国家医疗服务体系大学医院就诊的急诊就诊后住院的患者。使用临床信息（例如国家早期预警评分（NEWS））和与导致护理交接延迟的入院有显著关联的管理数据，使用混合效应逻辑模型开发预测模型。进行了详细的模型诊断和统计意义分析，包括接收者操作特征分析。
使用了 3 年（2018-20 年）的数据；共纳入 92444 例（70%）用于模型开发，39877 例（30%）用于模型验证。年龄、性别、种族、NEWS、格拉斯哥入院预测评分、多重剥夺指数十分位数、救护车到达和入院时间在过去 1 年内与护理交接延迟有统计学显著关联。提出的八变量预测模型具有良好的区分度，79%的敏感性（95%置信区间（CI）：79%，81%）、69%的特异性（95%CI：68%，69%）和 70%（95%CI：69%，70%）的整体准确性可识别经历 DTOC 的患者。
一些人口统计学、社会经济和临床因素与患者通过急诊就诊后是否经历 DTOC 显著相关。已经提出了一个八变量模型，能够以 70%的准确率识别经历护理交接延迟的患者。该八变量预测工具在入院时可准确计算患者经历延迟转移的概率。
