Clustering clinical departments for wards to achieve a prespecified blocking probability

When the number of available beds in a hospital is limited and fixed, it can be beneficial to cluster several clinical departments such that the probability of not being able to admit a patient is acceptably small. The clusters are then assigned to the available wards such that enough beds are available to guarantee a blocking probability below a prespecified value. We first give an exact formulation of the problem to be able to achieve optimal solutions. To reduce computation times, we also introduce two heuristic solution methods. The first heuristic is similar to the exact solution method, however, the number of beds needed is approximated by a linear function. The second heuristic uses a local search approach to determine the assignment of clinical departments to clusters and a restricted version of the exact solution method to determine the assignment of clusters to wards

Improve OR-schedule to reduce number of required beds

After surgery most of the surgical patients have to be admitted in a ward in the hospital. Due to financial reasons and an decreasing number of available nurses in the Netherlands over the years, it is important to reduce the bed usage as much as possible. One possible way to achieve this is to create an operating room (OR) schedule that spreads the usage of beds nicely over time, and thereby minimizes the number of required beds. An OR-schedule is given by an assignment of OR-blocks to specific days in the planning horizon and has to fulfill several resource constraints. Due to the stochastic nature of the length of stay of patients, the analytic calculation of the number of required beds for a given OR-schedule is a complex task involving the convolution of discrete distributions. In this paper, two approaches to deal with this complexity are presented. First, a heuristic approach based on local search is given, which takes into account the detailed formulation of the objective. A second approach reduces the complexity by simplifying the objective function. This allows modeling and solving the resulting problem as an ILP. Both approaches are tested on data provided by Hagaziekenhuis in the Netherlands. Furthermore, several what-if scenarios are evaluated. The computational results show that the approach that uses the simplified objective function provides better solutions to the original problem. By using this approach, the number of required beds for the considered instance of HagaZiekenhuis can be reduced by almost 20%

Scheduling non-urgent patient transportation while maximizing emergency coverage

Many ambulance providers operate both advanced life support (ALS) and basic life support (BLS) ambulances. Typically, only an ALS ambulance can respond to an emergency call, whereas non-urgent patient transportation requests can be served by either an ALS or a BLS ambulance. The total capacity of BLS ambulances is usually not enough to fulfill all non-urgent transportation requests. The remaining transportation requests then have to be performed by ALS ambulances, which reduces the coverage for emergency calls. We present a model that determines the routes for BLS ambulances while maximizing the remaining coverage by ALS ambulances. Different from the classical dial-a-ride problem, only one patient can be transported at a time, and not all requests are known in advance. Throughout the day, new requests arrive, and we present an online model to deal with these requests

Models for ambulance planning on the strategic and the tactical level

Ambulance planning involves decisions to be made on different levels. The decision for choosing base locations is usually made for a very long time (strategic level), but the number and location of used ambulances can be changed within a shorter time period (tactical level). We present possible formulations for the planning problems on these two levels and discuss solution approaches that solve both levels either simultaneously or separately. The models are set up such that different types of coverage constraints can be incorporated. Therefore, the models and approaches can be applied to different emergency medical services systems occurring all over the world. The approaches are tested on data based on the situation in the Netherlands and compared based on computation time and solution quality. The results show that the solution approach that solves both levels separately performs better when considering minimizing the number of bases. However, the solution approach that solves both levels simultaneously performs better when considering minimizing the number of ambulances. In addition, with the latter solution approach it is easier to make a good trade-off between minimizing the number of bases and ambulances because it considers a weighted objective function. However, the computation time of this approach increases exponentially with the input size whereas the computation time of the approach that solves both levels separately follows a more linear trend

Operating room rescheduling

Due to surgery duration variability and arrivals of emergency surgeries, the planned Operating Room (OR) schedule is disrupted throughout the day which may lead to a change in the start time of the elective surgeries. These changes may result in undesirable situations for patients, wards or other involved departments, and therefore, the OR schedule has to be adjusted. In this paper, we develop a decision support system which assists the OR manager in this decision by providing the three best adjusted OR schedules. The system considers the preferences of all involved stakeholders and only evaluates the OR schedules that satisfy the imposed resource constraints. The decision rules used for this system are based on a thorough analysis of the OR rescheduling problem. We model this problem as an Integer Linear Program (ILP) which objective is to minimize the deviation from the preferences of the considered stakeholders. By applying this ILP to instances from practice, we determined that the given preferences mainly lead to (i) shifting a surgery and (ii) scheduling a break between two surgeries. By using these changes in the decision support system, less surgeries are canceled and the perceived workload of all departments is reduced. The system can also be used to judge the acceptability of a proposed initial OR schedule

Minimizing the waiting time for emergency surgery

Hospitals aim to deliver the highest quality of care. One key priority is to schedule emergency surgeries as quickly as possible, because postponing them generally increases a patient’s risk of complications and even death. In this paper, we consider the case that emergency surgeries are scheduled in one of the elective Operating Rooms (ORs). In this situation, emergency patients are operated once an going elective surgery has finished. We denote these completion times of the elective surgeries by ‘break-in-moments’ (BIMs). The waiting time for emergency surgeries can be reduced by spreading these BIMs as evenly as possible over the day. This can be achieved by sequencing the surgeries in their assigned OR, such that the maximum interval between two consecutive BIMs is minimized. In this paper, we discuss several exact and heuristic solution methods for this new type of scheduling problem. However, in practice, emergency surgeries arising throughout the day and the uncertainty of the durations of elective surgeries, may disrupt the initial schedule. As a result, the completion times of the elective surgeries, and therefore, the BIMs change, leading also to a change of the maximum distance between two BIMs. To estimate this effect and investigate the robustness of the created schedules, we conduct a simulation study. Computational results show that the best approaches reduce the waiting time of emergency surgeries by approximately 10%

Comparing population and incident data for optimal air ambulance base locations in Norway

Background: Helicopter emergency medical services are important in many health care systems. Norway has a nationwide physician manned air ambulance service servicing a country with large geographical variations in population density and incident frequencies. The aim of the study was to compare optimal air ambulance base locations using both population and incident data. Methods: We used municipality population and incident data for Norway from 2015. The 428 municipalities had a median (5-95 percentile) of 4675 (940-36,264) inhabitants and 10 (2-38) incidents. Optimal helicopter base locations were estimated using the Maximal Covering Location Problem (MCLP) optimization model, exploring the number and location of bases needed to cover various fractions of the population for time thresholds 30 and 45 min, in green field scenarios and conditioned on the existing base structure. Results: The existing bases covered 96.90% of the population and 91.86% of the incidents for time threshold 45 min. Correlation between municipality population and incident frequencies was -0.0027, and optimal base locations varied markedly between the two data types, particularly when lowering the target time. The optimal solution using population density data put focus on the greater Oslo area, where one third of Norwegians live, while using incident data put focus on low population high incident areas, such as northern Norway and winter sport resorts. Conclusion: Using population density data as a proxy for incident frequency is not recommended, as the two data types lead to different optimal base locations. Lowering the target time increases the sensitivity to choice of data

Comparison of static ambulance location models

Over the years, several ambulance location models have been discussed in the literature. Most of these models have been further developed to take more complicated situations into account. However, the existing standard models that are often used in case studies have never been compared computationally according to the criteria used in practice. In this paper, we compare several ambulance location models on coverage and response time criteria. In addition to four standard ambulance location models from the literature, we also present two models that focus on average and expected response times. The computational results show that the maximum expected covering location problem (MEXCLP) and the expected response time model (ERTM) perform the best over all considered criteria. However, as the computation times for ERTM are long, the average response time model (ARTM) could be a good alternative. Based on these results, we also propose four alternative models that combine the good coverage provided by MEXCLP and the quick response times provided by ARTM. All four considered models provide balanced solutions in terms of coverage and response times. However, the multiple response times target model (MRTTM) outperforms the other models based on computation time