Modeling and Mitigating Bottlenecks in Healthcare Systems for Older Adults

This thesis investigates the healthcare system for older adults using both a flow-oriented and capacity-oriented approach. Chapter 2 explores how older adults travel through the healthcare network. This chapter combines national healthcare data of older adults from 2017-2019 with a process mining technique. The results show that 44% of the older adults experienced one or more patient journeys during this period of time. These patient journeys were often short and simple, involving hospitalizations or ED visits. A small group, often older and with more extensive use of drugs, follows a more complex healthcare journey. Although this sub-population is smaller, reducing the frequency of their transitions can lead to significant improvements within the healthcare system. Chapter 3 focuses on the risks of older adults with home care on emergency department visits, acute hospitalizations, institutionalization, and mortality. We show that the type of home care influences the risks of these adverse outcomes, even after accounting for age. Older adults with personal care has more than double the risk of hospitalization and recurrent admissions. The sub-population receiving nursing home care at home faces the greatest risks of institutionalization and death. These results highlight the need for prevention strategies within home care, particularly targeted toward personal care recipients, who make up a significant portion of this population. Chapter 4 introduces a framework to determine the optimal policy for monitoring and admitting patients to the nursing home to avoid a crisis situation and minimize overall cost. By modeling health deterioration and combining realistic costs for crises, monitoring, and nursing home care, the study finds that under current cost estimates, the optimal strategy delays nursing home admission until the worst health states. The optimal strategy recommends to increase monitoring frequency as patients deteriorate to worse health states. This framework also enables policy makers to consider the cost of preventive care and avoiding crisis situations. Chapter 5 focuses on after-service blocking in tandem queues, a mechanism that resembles bed-blocking in healthcare settings. These types of systems are notoriously difficult to analyze; therefore, we introduce a heuristic that enables fast evaluations. Our heuristic outperforms other heuristics and allows to be used in optimization and real-time decision support to support resource allocation to reduce the effect of after-service blocking. Building on this, Chapter 6 addresses a related issue: deadlocks in queueing networks. While after-service blocking occurs when downstream capacity is temporarily unavailable, deadlocks are situations in which jobs, or patients, are directly or indirectly blocked by themselves from transitioning to their next care form. Simulation studies show that our deadlock resolution method can reduce the number of blocked servers and job rejections. A case study on elderly care in Amsterdam demonstrates that coordinated deadlock resolution can reduce patient rejections by up to 10% and wrong-bed days by 20% over a ten-year period. These results highlight how this method can directly improve patient flow without the need for additional capacity. Chapter 7 turns to helplines, with a focus on mental health helplines. In this chapter, data from 113 Suicide Prevention is used to forecast the number of phone calls and chats on their helpline. Seasonality and trend were found to be important factors for forecasting, while the effect of media events was omitted due to the absence of effects. This forecast is then combined with a detailed simulation model of the helpline to generate a staffing advice. Incorporating counselor feedback through surveys further links objective workload measures with perceived strain, supporting better staff scheduling and well-being. The results demonstrate how predictive analytics and queueing models can improve both service quality and staff sustainability in mental health support systems.