School of Management, University of Bath, Bath, United Kingdom.
Health Data Research UK, South West Better Care Partnership, Bristol, United Kingdom.
PLoS One. 2022 Jun 7;17(6):e0268837. doi: 10.1371/journal.pone.0268837. eCollection 2022.
While there has been significant research on the pressures facing acute hospitals during the COVID-19 pandemic, there has been less interest in downstream community services which have also been challenged in meeting demand. This study aimed to estimate the theoretical cost-optimal capacity requirement for 'step down' intermediate care services within a major healthcare system in England, at a time when considerable uncertainty remained regarding vaccination uptake and the easing of societal restrictions.
Demand for intermediate care was projected using an epidemiological model (for COVID-19 demand) and regressing upon public mobility (for non-COVID-19 demand). These were inputted to a computer simulation model of patient flow from acute discharge readiness to bedded and home-based Discharge to Assess (D2A) intermediate care services. Cost-optimal capacity was defined as that which yielded the lowest total cost of intermediate care provision and corresponding acute discharge delays.
Increased intermediate care capacity is likely to bring about lower system-level costs, with the additional D2A investment more than offset by substantial reductions in costly acute discharge delays (leading also to improved patient outcome and experience). Results suggest that completely eliminating acute 'bed blocking' is unlikely economical (requiring large amounts of downstream capacity), and that health systems should instead target an appropriate tolerance based upon the specific characteristics of the pathway.
Computer modelling can be a valuable asset for determining optimal capacity allocation along the complex care pathway. With results supporting a Business Case for increased downstream capacity, this study demonstrates how modelling can be applied in practice and provides a blueprint for use alongside the freely-available model code.
虽然有大量研究关注 COVID-19 大流行期间急性医院面临的压力，但对于下游社区服务的关注较少，这些服务在满足需求方面也面临挑战。本研究旨在估算英格兰一个主要医疗保健系统内“降级”中级护理服务的理论成本最优容量需求，当时关于疫苗接种率和社会限制放宽仍存在很大不确定性。
使用流行病学模型（用于 COVID-19 需求）和回归公共流动性（用于非 COVID-19 需求）来预测中级护理需求。将这些输入到急性出院准备床位和家庭 D2A（出院评估）中级护理服务的患者流动计算机模拟模型中。成本最优容量被定义为产生最低中级护理提供总成本和相应急性出院延迟的容量。
增加中级护理能力可能会降低系统层面的成本，额外的 D2A 投资将大大抵消昂贵的急性出院延迟的减少（也导致改善患者的结果和体验）。结果表明，完全消除急性“床位阻塞”在经济上不太可能（需要大量下游能力），而卫生系统应该根据特定途径的特征，以适当的容忍度为目标。
计算机建模可以成为确定复杂护理途径中最佳容量分配的有价值的工具。由于结果支持增加下游能力的商业案例，本研究展示了如何在实践中应用建模，并提供了一个蓝图，可与免费提供的模型代码一起使用。
