Patient Admission Scheduling Research Articles

Solving the patient admission scheduling problem using constraint aggregation

Patient admission scheduling (PAS) consists of assigning patients to beds over a planning horizon to maximize treatment efficiency, patient satisfaction, and hospital utilization while meeting all necessary medical constraints and considering patient preferences as much as possible. There are several different variants of the PAS problem in the literature, which differ mainly in the constraints that must be satisfied (hard) or can be violated (soft). Due to the intrinsic difficulty of the PAS problem, solving large integer programming (IP) models to optimality is challenging. In this paper, we consider the widely studied variant of the PAS problem that has the maximum number of soft constraints, and focus on how to reduce the size of IP formulations of the PAS problem to improve the solving efficiency. We employ a two-stage optimization method where the first stage builds reduced models by constraint aggregation to improve the typical formulation of the PAS problem. Experimental results on the 13 benchmark instances in the literature indicate that our method can obtain new improved solutions (new upper bounds) for 6 instances, including one proven optimal solution. For the 5 other instances whose optimal solutions are known, our approach can reach these known optimal solutions in a shorter computation time compared to the existing methods. In addition, we apply our method to the original PAS problem, which has the maximum number of hard constraints, and perform computational experiments on the same 13 benchmark instances. Our method yields 5 new best solutions and proves optimality for 6 instances.

Optimization of Static Patient Admission Scheduling using the Variable Neighborhood Search Method

Planning and resource management are important aspects of a company's operational sustainability. With good management, companies can achieve their targets while minimizing operational costs. The same goes for hospitals. Various challenges related to resources are experienced by hospitals, such as scheduling nurses, patient surgeries, and patient appointments. Therefore, this paper aim at optimizing patient admission scheduling in order to improve hospital resource efficiency. To be more specific, patient admission scheduling, also known as the Patient Admission Scheduling Problem (PASP), is a scheduling problem that considers patient preferences and needs, bed availability, resource efficiency, and utilization. This problem is highly relevant, especially for large hospitals. The number of rooms, specialists, and facilities makes manual scheduling extremely difficult. It is due to the varied preferences, needs, and lengths of stay for patients in hospital. Various methods, both heuristic and exact, have been proposed, including the use of integer programming methods. However, for a large search space, this method requires a very long computational time. To address the PASP, this study applies the Variable Neighborhood Search (VNS) algorithm and random selection as optimization algorithms. The method was chosen because it has been proven effective to solve some combinatorial optimization problems in prior studies. Seven types of neighborhoods are implemented to find the best combinations in optimizing the PASP. The results show that the VNS algorithm outperforms the random selection algorithm, as it is able to generate 5 out of 7 solutions that are better, reducing penalties by 27.84% to 55.29%. The expected impact of this study is to increase the hospital patient satisfaction whereas in the same time minimize the operational cost.

Mixed Integer Programming For Patient Admission Scheduling in Hospital Network

The Patient Admission Scheduling (PAS) process involves efficiently managing the admission of patients to specific beds within relevant departments while addressing all their medical needs over a defined time horizon. This study delves into PAS within hospital network, emphasizing the collaborative nature of their interactions. Collaborative interactions are a common challenge in hospitals, where they need to collaborate and share resources to allocate patients to a limited number of beds within a specified timeframe, ensuring all necessary medical conditions are met. To address this challenge, a mixed-integer mathematical programming model for the PAS problem within hospital network is proposed with the aim of minimizing a weighted sum of unsatisfied constraints.

Generating balanced workload allocations in hospitals

As pressure on healthcare systems continues to increase, it is becoming more and more important for hospitals to properly manage the high workload levels of their staff. Ensuring a balanced workload allocation between various groups of employees in a hospital has been shown to contribute considerably towards creating sustainable working conditions. However, allocating work to different organizational units in a fair manner is not straightforward when it involves complex decision-making processes. In this paper we set out to balance the workload of heterogeneous hospital wards by optimizing the patient admission scheduling problem. Given the multi-period nature of patient admission scheduling, we introduce a new equity objective that captures both spatial (between hospital wards) and temporal (between days in the planning period) workload balancing. The resulting bi-objective problem is solved using an exact criterion space search algorithm. Our computational study employs problem instances that have been generated based on real-world data. The results demonstrate how spatially and temporally balanced workload allocations can be generated by minimizing the proposed equity objective. Moreover, we analyze sets of nondominated solutions to gain various insights into the trade-off between schedule cost and workload balance.

A hybrid Hill-ABC algorithm for patient admission scheduling problem

The patient admission scheduling (PAS) problem is a well-known combinatorial optimization problem that entails the allocation of patients to some restricted bedspace for a certain period while adhering to a set of established constraints such as medical preferences and prerequisites. The existing algorithmic techniques utilized in solving the formulation of the PAS could not efficiently navigate the deep solution space due to the highly constrained nature of the formulation. Therefore, to address the drawbacks exhibited by the existing search methodologies when applied to the PAS problem, this paper proposed a solution template that hybridized the artificial bee colony (ABC) with hill climbing heuristic (HCH) named the Hill-ABC algorithm. Hill climbing is integrated within the operator of employed bees of the ABC algorithm to exploit the PAS’s rugged solution space and strike the right balance between diversification and intensification during search activities. The design of the proposed Hill-ABC approach is in two phases: The first phase is aimed at generating initial feasible solutions using a room selection-based method (RSM). Similarly, the second phase employed three neighbourhood structures to improve the quality of previously generated initial solutions. The proposed hybrid Hill-ABC algorithm is validated using the PAS standard datasets. The experiment analysis shows that the proposed method achieved better-quality solutions than most of the existing state-of-the-art algorithms.

Patient admission scheduling problems with uncertain length of stay: optimization models and an efficient matheuristic approach

AbstractIn this paper, we study two well‐known problems that are attracting increasing attention in the last years: patient admission and patient‐to‐room assignment problems. These two problems are often complicated by some patients, which may have fluctuations in length of stay (LOS). We propose an optimization model that plans patient admissions and patient stays considering fluctuations in LOS and does not allow overcrowded rooms, as typically required in real‐world cases. We develop an efficient matheuristic approach based on large neighborhood search and fix and optimize heuristic. We tested our optimization model and some variations of the objective function on benchmark instances from the literature. The computational results indicate the effectiveness of the proposed methods in improving the quality of care offered to the patients. Furthermore, the proposed approach can provide a useful support to decision makers for patient flow management and avoid disruptions in access to care due to bed shortages.

A room-oriented artificial bee colony algorithm for optimizing the patient admission scheduling problem

Patient admission scheduling (PAS) is a tasking combinatorial optimization problem where a set of patients is assigned to limited facilities such as rooms, timeslots, and beds subject to satisfying a set of predefined constraints. The investigations into the performance of population-based algorithms that utilized to tackle the PAS problem considered in this paper reveal their weaknesses in obtaining quality solutions that create a space to investigate the performance of another population-based method. Thus, in this paper, an Artificial Bee Colony Algorithm (ABC) is proposed to tackle the formulation of the PAS problem under consideration. It is a class of swarm intelligence metaheuristic algorithms based on the intelligent foraging behaviour of honey bees developed to solve continuous and complex optimization problems. Due to the discretization of the PAS, the continuous nature of the ABC algorithm is changed to cope with the rugged solution space of the PAS. The initial feasible solution to the PAS problem is obtained using the room-oriented approach. Then the ABC algorithm optimizes the feasible solutions with the aid of three neighbourhood structures embedded within the employed bee and the onlooker bee operators of the algorithm. The performance of the proposed ABC algorithm based on three different parameters, the solution number (SN), limit value (LV), and the maximum cycle number (MCN) is evaluated on six standard benchmark datasets of the PAS. Two of these main parameters (i.e. SN and LV) are fine-tuned to obtain the best solutions on instances like Test-data 1 = 679.80, Test-data 2 = 1180.40, Test-data 3 = 787.40, Test-data 4 = 1198.60, Test-data 5 = 636.80, and Test-data 6 = 818.60. The best solutions obtained by the proposed method are evaluated against the results of the 19 comparative algorithms comprising five population-based methods, eleven heuristic, and hyperheuristic-based methods, and three integer programming-based methods. The proposed method shows its supremacy in the performance by achieving the best results in all the instances of the dataset when compared with five population-based methods (DFPA, HSA, MBBO-GBS, BBO-GBS, and BBO-RBS) and producing the best results in five instances when compared with eleven heuristic and hyperheuristic-based methods (LAHC, DHS-GD, HTS, DHS-SA, ADAPTIVE GD, GD, HH-GD, DHS-IO, HH-SA, HH-IE, TA) and Finally, it had a competitive performance with the other three Integer programming methods (MIP warm start, MIP-Heuristic, CG) that worked on the same formulations of the PAS. In a nutshell, the proposed ABC algorithm could be adopted as a new template algorithm for the PAS community.

Window-Based Multi-Objective Optimization for Dynamic Patient Scheduling with Problem-Specific Operators

The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality.

Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology.

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.

A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector

The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, outsourcing, and tele-medication facilities. The Asian countries and less developed countries with a high population rate are facing health care services related issues. One of these countries is India. India has two types of health care services systems: (i) public service system and (ii) private system. The public health system, i.e., the government, provides facilities to patients as primary health centers (PHCs) through limited secondary and tertiary health institutions like hospitals in rural areas while the private service is owned by local practitioners and institutions. Both of these service providers are facing bed occupancy issues for patients due to a highly populated country. To overcome this issue, we propose a machine learning solution for patient admission scheduling autonomously. The proposed framework helps hospitals to enhance the decision process for bed occupancy for patients concerning their departments and their diseases. We have deployed our framework in real time environment and find that it facilitates the overall performance of bed allocation in the prescribed hospitals.

A modified biogeography-based optimization algorithm with guided bed selection mechanism for patient admission scheduling problems

One of the complex combinatorial optimization problems is the Patient admission scheduling problem (PASP), which is concerned with assigning the patients arriving into a hospital to available beds to get medical services. The objective of PASP is to maximize the patients comfort, medical treatment effectiveness, and hospital utilization. This research proposes a new approach based on Biogeography-Based Optimization (BBO) algorithm for tacking the PASP. BBO was inspired from the idea of species migration between different habitats. Due to the complexity of the search space in PASP, the original BBO has been equipped with a guided bed selection (GBS) mechanism in order to improve its results and performance, as well as the operator capabilities of BBO which are modified to improve its diversity. These three variants of BBO are compared with each other using six de facto data sets that are widely used in the literature with varying sizes and complexity. The modified BBO is able to yield better results than the other variants. In a nutshell, this paper provides a new PASP method that can be considered an efficient alternative for the scheduling domain to be used by other researchers.

Compatibility of short and long term objectives for dynamic patient admission scheduling

When applying periodic re-optimization for handling a dynamic scheduling problem, the objective of the problem solved in each period (its short term objective) significantly impacts the quality of final solutions (its long term solutions). Meanwhile, designing a short term objective consistent with the dynamic problem’s long term objective remains a very challenging problem in its own right. This paper studies the compatibility of short term and long term objectives in the context of the Dynamic Patient Admission Scheduling Problem (DPAS). A new short term strategy — which considers idle resource penalties and anticipatory information — is presented for the problem. The resulting approach is then applied to the available DPAS benchmark, with its long term solutions evaluated with respect to new lower bounds calculated using Dantzig–Wolfe decomposition and column generation. The results demonstrate that the proposed short term strategy produces long term solutions of significantly better quality than the best-known strategy for 26 out of the 30 instances.

Harmony Search Algorithm for Patient Admission Scheduling Problem

Abstract The patient admission scheduling (PAS) problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferences. In this paper, we present a novel solution to the PAS problem using the harmony search (HS) algorithm. We tailor the HS to solve the PAS problem by distributing patients to beds randomly in the harmony memory (HM) while respecting all hard constraints. The proposed algorithm uses five neighborhood strategies in the pitch adjustment stage. This technique helps in increasing the variations of the generated solutions by exploring more solutions in the search space. The PAS standard benchmark datasets are used in the evaluation. Initially, a sensitivity analysis of the HS algorithm is studied to show the effect of its control parameters on the HS performance. The proposed method is also compared with nine methods: non-linear great deluge (NLGD), simulated annealing with hyper-heuristic (HH-SA), improved with equal hyper-heuristic (HH-IE), simulated annealing (SA), tabu search (TS), simple random simulated annealing with dynamic heuristic (DHS-SA), simple random improvement with dynamic heuristic (DHS-OI), simple random great deluge with dynamic heuristic (DHS-GD), and biogeography-based optimization (BBO). The proposed HS algorithm is able to produce comparably competitive results when compared with these methods. This proves that the proposed HS is a very efficient alternative to the PAS problem, which can be efficiently used to solve many scheduling problems of a large-scale data.

Late acceptance hill climbing algorithm for solving patient admission scheduling problem

This article tackles a patient admission scheduling using Late Acceptance Hill Climbing Algorithm (LAHC). The LAHC algorithm is among the newly proposed metaheuristic-based algorithm which belongs to the one-point solution technique. Patient admission scheduling is a complex combinatorial optimization problem which have been proven to belongs to class of NP-hard problem in almost all its variations. This problem is concerns with assigning a set of patients arriving for medical services in the hospital to a set of rooms, timeslots and beds subject to satisfying a set of predefined constraints. The proposed adaptation of LAHC to the patient admission scheduling named LAHC-based PAS comes in two stages: the first stage involves generating of initial feasible solution using the room oriented-based approach (ROP), while the second stage utilizes three neighbourhood structures which are embedded within the component of LAHC-based PAS to further enhances the initial solution generated at the initial stage. The proposed LAHC method is evaluated using the standard benchmark dataset. The experimental results shows that the proposed method is an effective technique for tackling the PAS problem. It is observed that the method outperform many of the existing techniques when compared with the state-of-the-arts.

Mixed integer programming based heuristics for the Patient Admission Scheduling problem

The Patient Admission Scheduling (PAS) problem is a combinatorial optimization problem where elective patients are automatically assigned to beds for the duration of their stay considering not only the medical necessity but also the patient preferences. Due to its combinatorial nature, solving the previously published problem instances to optimality is a difficult task. In this paper, we present two Mixed Integer Programming (MIP) based heuristics namely Fix-and-Relax (F&R) and Fix-and-Optimize (F&O) where PAS problem instances are decomposed into sub-problems and then the sub-problems are optimized. Our approach uses patient decomposition considering patient length-of-Stay (LoS), room preference, admission date, specialism choice, and age, as well as time decomposition considering different optimization window sizes. Quick solutions generated by F&R heuristic are used as an input to the F&O heuristic and are improved in an iterative nature. Main goal of the study is to provide high quality solutions in shorter run times. Computational findings show that the proposed heuristics provide promising results towards the solution of the problem in faster CPU times than the previously reported values where less than 15 percent gap from the best known solutions is achieved for most of the test instances, and as low as 5 percent gap for some of the sample data.

An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem.

The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting patient comfort and treatment efficiency are maximized. Consideration must be given to uncertainty in the length of stays of patients as well as the possibility of emergency patients. We develop an adaptive large neighborhood search (ALNS) procedure to solve the problem. This procedure utilizes a Simulated Annealing framework. We thoroughly test the performance of the proposed ALNS approach on a set of 450 publicly available problem instances. A comparison with the current state-of-the-art indicates that the proposed methodology provides solutions that are of comparable quality for small and medium sized instances (up to 1000 patients); the two approaches provide solutions that differ in quality by approximately 1% on average. The ALNS procedure does, however, provide solutions in a much shorter time frame. On larger instances (between 1000-4000 patients) the improvement in solution quality by the ALNS procedure is substantial, approximately 3-14% on average, and as much as 22% on a single instance. The time taken to find such results is, however, in the worst case, a factor 12 longer on average than the time limit which is granted to the current state-of-the-art. The proposed ALNS procedure is an efficient and flexible method for solving the DPAS problem.

Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays

We revisit and extend the patient admission scheduling problem, in order to make it suitable for practical applications. The main novelty is that we consider constraints on the utilisation of opera...

An adaptive non-linear great deluge algorithm for the patient-admission problem

The patient-admission scheduling problem is a combinatorial optimisation problem that is receiving attention in health care practice. This is because hospitals are experiencing increasing pressure to optimise the usage of their resources while simultaneously increasing patients’ comfort. The goal of this work is to propose a meta-heuristic approach called an adaptive non-linear great deluge algorithm to address patient admission problems. We investigate the effect of adaptively updating the non-linear decay rate of the water level in the great deluge algorithm during the course of optimisation. The great deluge algorithm needs only to set up the water level as its basic parameter, which makes it naturally very attractive for solving optimisation problems. The performance of the proposed approach is verified using standard benchmark datasets. Empirical results demonstrate that the proposed algorithm obtains competitive results when compared to other approaches in the scientific literature.

A column generation approach for solving the patient admission scheduling problem

This paper addresses the Patient Admission Scheduling (PAS) problem. The PAS problem entails assigning elective patients to beds, while satisfying a number of hard constraints and as many soft constraints as is possible, and arises at all planning levels for hospital management. There exist a few, different variants of this problem. In this paper we consider one such variant and propose an optimization-based heuristic building on branch-and-bound, column generation, and dynamic constraint aggregation to solve it. We achieve tighter lower bounds than previously reported in the literature and, in addition, we are able to produce new best known solutions for five out of twelve instances from a publicly available repository.

The Patient Admission Scheduling of an Ophthalmic Hospital Using Genetic Algorithm

Currently, FCFS scheduling method is widely used in hospitals for patient admission scheduling, which ignores the impacts of patient length of stay and surgery arrangement on the usage of hospital resources. This paper proposes a more comprehensive mathematical model and evaluation mechanism for the patient admission scheduling of an ophthalmic hospital. A genetic algorithm (GA) is proposed to optimize the model, which can provide detailed scheduling of patient admission in the hospital for everyday. The result is compared with that of the traditional FCFS method, which indicates that the GA helps to reduce the preoperative waiting time for patients. Besides, GA can provide different kinds of scheduling for the hospital to select by adjusting the relative weights of different objectives in the algorithm.