Automated decision-support for hospital admission planning

Hospitals – like any other organization – continuously aim to improve the efficiency of their most costly resources. Faced with the uncertain and highly variable nature of the care process, they try to manage the flow of patients in such a way that operational inefficiencies (overtime in the operating theatre, bed shortages in hospital wards, etc.) are minimized, while still providing care to all patients in a timely fashion. One key instrument in this management process is the admission planning of elective patients. The research question that we pose is: what automated, intelligent tools can we provide to support this decision-making process? Our current research focuses on developing decision-support models and algorithms for admission planning in hospitals. By effectively planning the inflow of elective patients, we try to improve efficiency of a hospital’s key resources (operating theatres, hospital wards and beds, and personnel). At the same time, we also anticipate the inflow of non-elective (i.e. emergency) patients – which by definition cannot be planned. This demo presents the planning of a set of elective patients over a certain period of time. The aim is to improve operational efficiency of two key resources: the operating theatre and the hospital wards. The demo shows how a metaheuristic algorithm improves an initial admission plan to have high operating theatre load, while avoiding overtime; to have a balanced patient load in the hospital wards, ensuring a stable workload; and – finally – to meet patient (room) preferences while avoiding room conflicts (e.g. male/female patient separation).status: publishe

Operational Decision Support Models and Algorithms for Hospital Admission Planning and Scheduling

The present dissertation focuses on developing operational decision support models and algorithms for hospital admission planning and scheduling. The aim is to increase efficient usage of key hospital resources by supporting human planners at hospital admission offices with automated tools for their daily and weekly decision making. Three planning processes concerned with admission scheduling of patients are considered: assignment of admitted patients to hospital rooms, determination of admission dates for elective surgical patients, and scheduling surgical cases in operating rooms.The planning process of assigning patients to hospital rooms and wards is the subject of two studies. Firstly, a reactive and an anticipatory decision support model are presented for daily decision making on patient-to-room assignments. It is shown that the anticipatory model is better than the reactive model under various conditions. The reactive model can be seen as an idealized version of current hospital practices, implying that current decision making can be improved and efficient usage of a diverse set of hospital rooms can be increased. Secondly, the Red-Blue transportation problem (Red-Blue TP) is introduced as an abstraction of the patient-to-room assignment problem. A complexity and computational study on the Red-Blue TP provide insights into the difficulty of patient-to-room assignment planning under a gender separation policy.The third and fourth studies concentrate on the admission scheduling process and operating theatre scheduling process for surgical patients. For the admission scheduling process, the aim is to support human planners in determining when patients should be admitted such that expected operating theatre costs and patient waiting time are minimized, while considering limited bed availability. A stochastic optimization model and a heuristic algorithm are presented, that serve as the basis for developing admission scheduling strategies. It is shown that, when given sufficient planning flexibility, stochastic optimization models may improve on deterministic decision models by considering the variance in bed usage and operating theatre usage. However, this improved performance is at the expense of patient friendliness, quality of care and throughput.Finally, for the operating theatre scheduling process, a general and flexible decision support model is presented capturing many considerations encountered in practice. It aims to support human planners in determining a schedule for performing surgical cases in the operating theatre while considering a variety of resources by means of a generalized resource model. Additionally, the model's objectives are to increase throughput and the efficient usage of the operating theatre and its resources. A heuristic algorithm is developed to solve the model which scales well with problem size.status: publishe

A periodic optimization approach to dynamic pickup and delivery problems with time windows

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A real world 1D stock cutting problem: Exact and heuristic algorithms

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The Red-Blue transportation problem

This paper considers the Red-Blue Transportation Problem (Red-Blue TP), a generalization of the transportation problem where supply nodes are partitioned into two sets and so-called exclusionary constraints are imposed. We encountered a special case of this problem in a hospital context, where patients need to be assigned to rooms. We establish the problem's complexity, and we compare two integer programming formulations. Furthermore, a maximization variant of Red-Blue TP is presented, for which we propose a constant-factor approximation algorithm. We conclude with a computational study on the performance of the integer programming formulations and the approximation algorithms, by varying the problem size, the partitioning of the supply nodes, and the density of the problem

A two-phase heuristic approach to multi-day surgical case scheduling considering generalized resource constraints

The present contribution focuses on the problem of assigning and scheduling surgical cases in rooms of an operating theatre, in order to maximize efficiency. The aim is to schedule as many surgical cases in as few operating rooms as possible, within regular operating theatre opening hours and under limited resource availability. This work generalizes many surgical case scheduling aspects considered in the literature and in practice by means of a unified resource model. The performance of a heuristic algorithm designed for this rich problem formulation is evaluated and compared on a set of real-world data. Computational results demonstrate the potential improvements obtained by using the presented approach, over schedules constructed by human planners.status: publishe