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

Reconfiguration of inpatient services to reduce bed pressure in hospitals

Healthcare systems around the world are facing an inpatient bed crisis. This crises has been highlighted, more than ever before, during the recent Covid-19 pandemic. Our aim in this doctoral dissertation is to propose a cost-effective solution to the ongoing bed-crisis with a focus on reconfiguration of inpatient services. The configuration of inpatient services, which identifies the set of specialties and bed numbers allocated to each ward, has a substantial impact on performance, which we measure by the cost of patients waiting for services or abandoning the services plus the cost of nursing teams. Reviewing the existing configurations proposed in the literature, we choose the clustered overflow configuration as the basis for our study due to its versatility. Given a set of specialties, a total number of beds, and a (potentially infinite) waiting time threshold for patients, we then propose a heuristic methodology for finding a good allocation of beds and specialties for this configuration. This methodology relies on a novel performance evaluation model for overflow delays systems, i.e., hierarchical queueing systems involving several dedicated pools and a single overflow pool. We illustrate the application of our methodology by applying it on a comprehensive inpatient dataset obtained from a UK hospital. A simulation study shows substantial savings can potentially be made by using the configurations proposed by our methodology as compared to the existing configuration of the hospital or other major configurations proposed in the literature

On characterization of the core of lane covering games via dual solutions

The lane covering game (LCG) is a cooperative game where players cooperate to reduce the cost of cycles that cover their required lanes on a network. We discuss the possibilities/impossibilities of a complete characterization of the core via dual solutions in LCGs played among a collection of shippers, each with a number of service require-ments along some lanes, and show that such a complete characterization is possible if each shipper has at most one service requirement

Tracebook : a dynamic checklist support system

It has recently been demonstrated that checklist scan enable significant improvements to patient safety. However, their clinical acceptance is significantly lower than expected. This is due to the lack of good support systems. Specifically, support systems are too static: this holds for paper-based support as well as for electronic systems that digitize paper-based support naively. Both approaches are independent from clinical process and clinical context. In this paper, we propose a process-oriented and context-aware dynamic checklist support system: Tracebook. This system supports the execution of complex clinical processes and rules involving data from Electronic Medical Record systems. Workflow activities and forms are specific to individual patients based on clinical rules and they are dispatched to the right user automatically based on a process model. Besides describing the Tracebook functionality in general, this paper demonstrates the support system specifically on an example application that we are preparing for a controlled clinical evaluation. At last we discuss the difference between Tracebook and other support systems which also rely on a checklist format

A unified race algorithm for offline parameter tuning

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods