Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
Lean Care Solutions Corp, Bloomington, IN, USA.
Health Care Manag Sci. 2019 Jun;22(2):318-335. doi: 10.1007/s10729-018-9439-5. Epub 2018 Mar 13.
The decision of whether to admit a patient to a critical care unit is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient health risk metrics can be incorporated while considering the congestion that will occur. The hospital is modeled as a complex loss queueing network with a stochastic model of how long risk-stratified patients spend time in particular units and how they transition between units. A Mixed Integer Programming model approximates an optimal admission control policy for the network of units. While enforcing low levels of patient blocking, we optimize a monotonic dual-threshold admission policy. A hospital network including Intermediate Care Units (IMCs) and Intensive Care Units (ICUs) was considered for validation. The optimized model indicated a reduction in the risk levels required for admission, and weekly average admissions to ICUs and IMCs increased by 37% and 12%, respectively, with minimal blocking. Our methodology captures utilization and accessibility in a network model of care pathways while supporting the personalized allocation of scarce care resources to the neediest patients. The interesting benefits of admission thresholds that vary by day of week are studied.
是否将患者收入重症监护病房是一个关键的运营问题，对医院绩效和患者预后都有重大影响。目前，医院缺乏一种方法，可以在考虑拥堵情况的同时，有选择地将患者收入这些病房，将患者健康风险指标纳入其中。该医院被建模为一个复杂的损失排队网络，具有风险分层患者在特定病房停留时间以及他们在病房之间转移的随机模型。混合整数规划模型为单元网络近似最优入院控制策略。在执行低水平的患者阻塞的同时，我们优化了单调双阈值入院策略。为了验证，考虑了包括中级护理单元 (IMC) 和重症监护病房 (ICU) 的医院网络。优化后的模型表明，入院所需的风险水平降低了，每周 ICU 和 IMC 的平均入院人数分别增加了 37%和 12%，而阻塞最小。我们的方法在护理途径的网络模型中捕获了利用率和可及性，同时支持将稀缺的护理资源分配给最需要的患者。研究了按星期几变化的入院阈值的有趣好处。
