Preparation of chemotherapy drugs: planning policy for reduced waiting times

This study investigates the impact of pharmacy policies on patient waiting time in the Chemotherapy Day Unit of the Netherland Cancer Institute - Antoni van Leeuwenhoek hospital (NKI-AVL). The project evaluated whether a reduction in waiting time resulting from medication orders being prepared in advance of patient appointments was justified, given that medications prepared in advance risked being wasted if patients arrived too sick for treatment. Within this context, we derive explicit expressions to approximate patient waiting times and wastage costs allowing management to see the tradeoff between these two metrics for different policies. Using a case study and a simulation model, the approximations are evaluated. The explicit expressions allow the analysis to be easily repeated when medication costs change or when new medications/protocols are introduced. In the same vein, other hospitals with different patient case mixes can easily complete the analysis in their setting. Finally, the outcome from this study resulted in a new policy at the cancer centre which is expected to decrease the waiting time by half while only increasing pharmacy’s costs by 1-2%

Analytical models to determine room requirements in outpatient clinics

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room while patients visit for consultation, we call this the Patient-to-Doctor policy (PtD-policy). A different approach is the Doctor-to-Patient policy (DtP-policy), whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We use a queueing theoretic and a discrete-event simulation approach to provide generic models that enable performance evaluations of the two policies for different parameter settings. These models can be used by managers of outpatient clinics to compare the two policies and choose a particular policy when redesigning the patient process.We use the models to analytically show that the DtP-policy is superior to the PtD-policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. In addition, to calculate the required number of consultation rooms in the DtP-policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation. We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

An exact approach for relating recovering surgical patient workload to the master surgical schedule

No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically the ward occupancy distributions, patient admission/discharge distributions, and the distributions for ongoing interventions/treatments is computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room

Interacting hospital departments and uncertain patient flows:theoretical models and applications

In this thesis we address a number of challenging problems related to health care logistics. These problems are motivated by hospital managers who collaborated in the research, and the results are applied at their hospitals. The general results and solution approaches presented in this thesis are also valid in other hospital settings. To position the research we review quantitative health care literature to examine the extent to which models encompass multiple hospital departments and account for department-to-department interactions. We provide a general overview of the relationships which exist between major hospital departments and describe how these relationships are accounted for by researchers. Our review of literature found that researchers often confine models to single departments due to system complexity and the uncertain nature of patient flows (Chapter 2). Using and developing techniques from queueing theory, mathematical programming, and simulation, we demonstrate how these characteristics can be coped with by solving multiple strategic, tactical, and operational problems faced by our partner hospitals. Using queueing theory we model the complex and uncertain relationship between capacity, case mix and patient mix. With parameters provided by this queueing model, we formulate a combinatorial optimization problem to maximize the hospital’s remuneration under a fee-for-service financing system. We thus provide a methodology for optimizing strategic capacity and case mix planning decisions. Exact solutions can be found with integer linear program solvers and approximate solutions with dynamic programming (Chapter 3). A second strategic problem is deciding whether (and to what extent) to pool resources within hospitals. Due to the uncertainty of patient arrivals and the economies of scale found in the pooled departments, access time will typically be worse in unpooled departments. However, if the service time is sufficiently lower in the unpooled departments, due to more focused care, the opposite is true. Using queueing theory we derive general results stating the extent to which focused care must decrease service time in an unpooled department in order to compensate for the lack of economies of scale. The main characteristics influencing economies of scale losses are clinic load, proportional size of the patient groups, resource divisions and appointment length variability (Chapter 4). At a tactical level, physicians and hospital managers must decide how many patients a single physician can effectively be accountable for (i.e. panel size). We formalize an extension to existing models allowing the panel size to be a random variable which accounts for the uncertainty in patient flows. Using queueing theory we provide general results related to capacity planning and provide strategies for reaching and maintaining a panel size that meets certain performance criteria (Chapter 5). Developing a surgical schedule that does not overwhelm inpatient wards is a complex problem, given that surgery durations and patient length of stays are uncertain. Using applied probability, we develop a solution approach for the tactical level master surgical scheduling problem. Our approach, used to develop a new master surgical schedule at the collaborating hospital, is readily repeatable and has been used at multiple Dutch hospitals. Using a case study, and by comparing predicted ward occupancies with post-implementation ward occupancies, we validated the approach (Chapter 6). An operational level problem faced by many pharmacies is deciding when to prepare medication. This problem is complex because medications are expensive and have a limited shelf life, and uncertain, because patient no-shows are common in hospitals. Analyzing this problem for a chemotherapy pharmacy, our case study predicted waiting times could be decreased by 30 minutes while only increasing pharmacy costs by 1-2%. The research led to analytic approximations (validated with discrete event simulation) usefull for predicting patient waiting times and costs in any pharmacy. Our analysis in Chapter 7 led to a new pharmacy policy which has been implemented at the collaborating hospital

Operating theatre planning and scheduling

In this chapter we present a number of approaches to operating theatre planning and scheduling. We organize these approaches hierarchically which serves to illustrate the breadth of problems confronted by researchers. At each hierarchicalplanning level we describe common problems, solution approaches and results from studies at partner hospitals

An exact approach for relating recovering surgical patient workload to the master surgical schedule

No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper, we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically, the ward occupancy distributions, patient admission/discharge distributions and the distributions for ongoing interventions/treatments are computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long-term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room