Improving operational effectiveness of tactical master plans for emergency and elective patients under stochastic demand and capacitated resources

This paper develops a two-stage planning procedure for master planning of elective and emergency patients while allocating at best the available hospital resources. Four types of resources are considered: operating theatre, beds in the medium and in the intensive care units, and nursing hours in the intensive care unit. A tactical plan is obtained by minimizing the deviations of the resources consumption to the target levels of resources utilization, following a goal programming approach. The MIP formulation to get this tactical plan is specifically designed to account for emergency care since it allows for the reservation of some capacity for emergency patients and possible capacity excess. To deal with the deviation between actually arriving elective patients and the average number of patients on which the tactical plan is based, we consider the possibility of planning a higher number of patients than the average to create operating slots in the tactical plan (slack planning). These operating slots are then filled in the operational plan following several flexibility rules. We consider three options for slack planning that lead to three different tactical plans on which we apply three flexibility rules to get finally nine alternative weekly schedules of elective patients. We then develop an algorithm to modify this schedule on a daily basis so as to account for emergency patients’ arrivals. Scheduled elective patients may be cancelled and emergency patients may be sent to other hospitals. Cancellation rules for both types of patients rely on the possibility to exceed the available capacities. Several performance indicators are defined to assess patient service and hospital efficiency. Simulation results show a trade-off between hospital efficiency and patient service.     

Master surgery scheduling with consideration of multiple downstream units

We consider a master surgery scheduling (MSS) problem in which block operating room (OR) time is assigned to different surgical specialties. While many MSS approaches in the literature consider only the impact of the MSS on operating theater and operating staff, we enlarge the scope to downstream resources, such as the intensive care unit (ICU) and the general wards required by the patients once they leave the OR. We first propose a stochastic analytical approach, which calculates for a given MSS the exact demand distribution for the downstream resources. We then discuss measures to define downstream costs resulting from the MSS and propose exact and heuristic algorithms to minimize these costs.     

Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments

An incentive scheme aimed at reducing patients’ waiting times in accident and emergency departments was introduced by the UK government in 2000. It requires 98% of patients to be discharged, transferred, or admitted to inpatient care within 4 hours of arrival. Setting the minimal hour by hour medical staffing levels for achieving the government target, in the presence of complexities like time-varying demand, multiple types of patients, and resource sharing, is the subject of this paper. Building on extensive body of research on time dependent queues, we propose an iterative scheme which uses infinite server networks, the square root staffing law, and simulation to come up with a good solution. The implementation of this algorithm in a typical A&E department suggests that significant improvement on the target can be gained, even without increase in total staff hours.     

Control charts for monitoring ship operating conditions and CO2 emissions based on scalar‐on‐function regression

To respond to the compelling air pollution programs, shipping companies are nowadays setting‐up on their fleets modern multisensor systems that stream massive amounts of observational data, which can be considered as varying over a continuous domain. Motivated by this context, a novel procedure is proposed, which extends classical multivariate techniques to the monitoring of multivariate functional data and a scalar quality characteristic related to them. The proposed procedure is shown to be also applicable in real time and is illustrated by means of a real‐case study in the maritime field on the continuous monitoring of operating conditions (ie, the multivariate functional data) and total CO₂ emissions (ie, the scalar quality characteristic) at each voyage of a cruise ship. The real‐time monitoring is particularly helpful for promptly supporting managerial decision making by indicating if and when an anomaly occurs during the navigation.     

Reducing patient-flow delays in surgical suites through determining start-times of surgical cases

A scheduling strategy to determine starting times of surgeries in multiple operating rooms (OR) is presented. The constraints are resource limit of a downstream facility, post-anesthesia care unit (PACU), and the service time uncertainties. Given sets of surgeries that need to be done on a day, this problem is formulated as a flexible job shop model with fuzzy sets. Patient-waitings in the process flow, clinical resource idling, and total completion times are considered for evaluation. This multi-objective problem is solved by a two-stage decision process. A genetic algorithm is used for determining relative order of surgeries in the first stage and definite starting times for all the surgical cases are obtained by a decision-heuristic in the second stage. The resultant schedule is evaluated by a Monte-Carlo simulation. The performance is shown to be better than our previous approach, a simulation based scheduling which already outperforms simple scheduling rules in regional hospitals. Additionally, the ratio of PACU to OR is examined using the proposed scheduling strategy.     

Managerial Analysis and Decision-Making in Outpatient Health Clinics

This paper discusses a generalized model of an outpatient clinic which duplicates many real-life complexities, e.g. different facilities, the patient routes through the clinic, number of observers in each facility, etc. The validation tests proved that the output generates distributions which are not statistically different from the observed distributions for The Pennsylvania State Outpatient Clinic. The model is relatively fast and efficient, and one typical day can be simulated in less than one second of the computer processing time on IBM System 370/168. With slight modifications, it is capable of being transferred to many different types of health care delivery systems, e.g. hospitals, health maintenance organizations and to prehospital emergency care systems.The paper describes some basic measures of effectiveness for outpatient clinics, and using these measures evaluates various operating procedures and policies.     

Modelling activities at a neurological rehabilitation unit

A queuing model of a specialist neurological rehabilitation unit is studied. The application is to the Neurological Rehabilitation Centre at Rookwood Hospital (Cardiff, UK), the national rehabilitation unit for Wales. Due to high demand this 21-bed inpatient facility is nearly always at maximum occupancy, and with a significant bed-cost per day this makes it a prime candidate for mathematical modelling. Central to this study is the concept that treatment intensity has an effect on patient length of stay. The model is constructed in four stages. First, appropriate patient groups are determined based on a number of patient-related attributes. Second, a purpose-built scheduling program is used to deduce typical levels of treatment to patients of each group. These are then used to estimate the mean length of stay for each patient group. Finally, the queuing model is constructed. This consists of a number of disconnected homogeneous server queuing systems; one for each patient group. A Coxian phase-type distribution is fitted to the length of time from admission until discharge readiness and an exponential distribution models the remainder of time until discharge. Some hypothetical scenarios suggested by senior management are then considered and compared on the grounds of a number of performance measures and cost implications.     

考虑需求不确定的多级应急物流设施选址研究

为了对急物流设施选址问题进行合理的研究,建立了包含配送中心、配送点和需求点的多级应急物流网络。基于应急物资需求特点,使用三角模糊数表示应急物资需求的不确定性,同时考虑应急救援成本和应急救援时间两个目标,建立了应急物流设施选址模型。采用去模糊化方法将三角模糊数转化为确定数,利用成本和时间的单目标的最优结果将多目标转化为相对值,再对时间和成本目标进行加权处理,既消除了不同目标之间的单位及数量级差异,还可以进行动态调整。设计了遗传算法对模型进行求解,通过实际算例表明了模型和算法可以有效地解决应急物流设施选址问题。     

Determining Ambulance Deployment in Santo Domingo,Dominican Republic

In 1983, The Ministry of Health of the Dominican Republic (SESPAS) began planning an emergency medical service (E.M.S.) system for the capital city of Santo Domingo. This paper reports on one component of this effort, the development of options regarding the type of E.M.S. care to be delivered, by whom, via what number of types of equipment, sited at which locations. SESPAS has used the results to develop a plan for a public ‘urgency care’ system quite different from the uncoordinated mix of public, private and voluntary services operating previously. The system has been implemented according to plan with basic life support vehicles staffed by physicians.     

Using Singular Spectrum Analysis to obtain staffing level requirements in emergency units

Many operational research (OR) techniques use historical data to populate model input parameters. Although the majority of these models take into account stochastic variation of the inputs, they do not necessarily take into account seasonal variations and other stochastic effects that might arise. One of the major applications of OR lies within healthcare, where ever increasing pressure on healthcare systems is having major implications on those who plan the provision of such services. Coping with growing demand for healthcare, as well as the volatile nature of the number of arrivals at a healthcare facility makes modelling healthcare provision one of the most challenging fields of OR. This paper proposes the use of a relatively modern time series technique, Singular Spectrum Analysis (SSA), to improve existing algorithms that give required staffing levels. The methodology is demonstrated using data from a large teaching hospital's emergency unit. Using time dependent queueing theory, as well as SSA, staffing levels are obtained. The performance of our technique is analysed using a weighted mean square error measure, introduced in this paper.     

Vehicle Scheduling with Timed and Connected Calls: A Case Study

A manual method was developed for scheduling the vehicle fleet of a contract transport undertaking. The main requirements were observance of time limits on individual calls and the ability to allocate pairs or groups of related calls to the same vehicle. These objectives were achieved by introducing an initial allocation of calls to vehicles prior to sequential routing of the calls. The allocation was based on a model relating work density (calls per unit area) to vehicle loading (calls per unit of vehicle time), and on fact-finding research on the main parameters of calling time, travelling speed and distance. This relationship was embodied in a visual scheduling aid for use by the route planners. Implementation was successful and resulted in about 15 per cent of vehicle time being made available for additional revenue-earning work without increase in fleet size. When utilized this represented an effective saving in operating costs of about 12 per cent.     

Probabilistic analysis of a system with dependent units having a single repair facility subject to preventive maintenance

The paper deals with the availability and the reliability analysis of a system with dependent units having a single repair facility subject to preventive maintenance. The system initially consists of n-identical units (connected in parallel) each with failure rate λ_n. The failure rate of a unit at any given instant of time depends upon the number of units operating at that instant. The time to repair of a failed unit and the time for maintenance of the repair- facility are arbitrarily distributed whereas the time to failure of a unit is exponentially distributed. The results obtained have been compared with those obtained when the repair facility is not subject to preventive maintenance.     

A framework for predicting gross institutional long-term care cost arising from known commitments at local authority level

As the UK population ages, it is forecasted that there will be an unsustainable increase in the need for, and therefore in the costs of long-term care. Although several studies have been performed to estimate these costs, they do not take into account the impact of survival patterns on costs. Focussing only on residents already in care (known commitments), we have developed, in association with an English local authority, a framework for estimating the future gross cost incurred by this group, built around a survival model. We apply this framework to forecast the cost over a given period of time, of maintaining a group of individuals in residential and nursing care, funded by the local authority. One of the novelties in the model is that it translates survival inputs and unit fees for care into cost in a manner, which was useful and meaningful to decision makers.     

Using system dynamics principles for conceptual modelling of publicly funded hospitals

This paper examines the long-standing operational issue of patients boarding in the emergency department (ED), who have been admitted to hospital (inpatient ‘boarders’). From this analysis we design a conceptual model that provides a roadmap to create sustainable improvements in ED waiting times. The conceptual model is built using system dynamics methodology, and illustrates the use of system archetypes, a set of common causal feedback loops that illustrate how well-intended decisions have unintentional side effects. This paper outlines the journey taken by one large academic health centre to address these issues, and highlights the larger implications and recommendations that are applicable to other publicly funded hospitals.     

A mixed integer programming approach for allocating operating room capacity

We have developed a methodology for allocating operating room capacity to specialties. Our methodology consists of a finite-horizon mixed integer programming (MIP) model which determines a weekly operating room allocation template that minimizes inpatients' cost measured as their length of stay. A number of patient type priority (eg emergency over non-emergency patient) and clinical constraints (eg maximum number of hours allocated to each specialty, surgeon, and staff availability) are included in the formulation. The optimal solution from the analytical model is inputted into a simulation model that captures some of the randomness of the processes (eg surgery time, demand, arrival time, and no-show rate of the outpatients) and non-linearities (eg the MIP assumes proportional allocation of demand satisfaction (output) with room allocation (input)). The simulation model outputs the average length of stay for each specialty and the room utilization. On a case example of a Los Angeles County Hospital, we show how the hospital length of stay pertaining to surgery can be reduced.     

Reliability analysis of two-unit cold standby repairable systems under Poisson shocks

This paper analyses the reliability of a cold standby system consisting of two repairable units, a switch and a repairman. At any time, one of the two units is operating while the other is on cold standby. The repairman may not always at the job site, or take vacation. We assume that shocks can attack the operating unit. The arrival times of the shocks follow a homogeneous Poisson process and their magnitude is a random variable following a known distribution. Time on repairing a failed unit and the length of repairman’s vacation follow general continuous probability distributions, respectively. The paper derives a number of reliability indices: system reliability, mean time to first failure, steady-state availability, and steady-state failure frequency.     

Centrality-based ambulance dispatching for demanding emergency situations

One category of dispatching decisions in emergency medical service involves the selection of an ambulance among idle units when a call is received (call-initiated), and another involves the selection of a call among those waiting when a unit gets freed (ambulance-initiated). This research focuses on the ambulance-initiated dispatching and aims at developing a rule that can be flexibly used in various contexts characterized by the probability of transferring the patient to hospital. The idea behind this rule is to give a higher priority to the call that is more centrally located with respect to other calls. When the centrality along with the closeness is used to prioritize calls, the units would smoothly proceed towards dense regions while efficiently exploiting calls, thereby keeping the completion rate at maximum. This centrality-based dispatching rule is tested in various scenarios and demonstrates considerable reductions in both average and variation of response time.     

Linear methods for approximation of some classes of holomorphic functions from the Bergman space

We construct a linear method {ie910-01} for the approximation (in the unit disk) of classes of holomorphic functions {ie910-02} that are the Hadamard convolutions of the unit balls of the Bergman space A _p with reproducing kernels {ie910-03}. We give conditions for ψ under which the method {ie910-04} approximates the class {ie910-05} in the metrics of the Hardy space H _s and the Bergman space A _s , 1 ≤ s ≤ p, with an error that coincides in order with the value of the best approximation by algebraic polynomials. Translated from in Ukrains'kyi Matematychnyi Zhurnal, Vol. 60, No. 6, pp. 783–795, June, 2008.     

On exposed functions in Bernstein spaces

For σ > 0, the Bernstein space {ie427-01} consists of those L ¹(ℝ) functions whose Fourier transforms are supported by [−σ, σ]. Since {ie427-02} is separable and dual to some Banach space, the closed unit ball {ie427-03} of {ie427-04} has sufficiently large sets of both exposed and strongly exposed points: {ie427-05} coincides with the closed convex hull of its strongly exposed points. We investigate some properties of exposed points, construct several examples, and obtain as corollaries relations between the sets of exposed, strongly exposed, weak* exposed, and weak* strongly exposed points of {ie427-06}.     

On the economic order quantity under conditions of permissible delay in payments

In this note, I amend Goyal's model by considering the difference between unit price and unit cost. I then establish an easy analytical closed-form solution to the problem. The theoretical results obtained here reveal the following two managerial phenomena. (1) In certain cases, the economic replenishment interval and order quantity decreases under the permissible delay in payments, which contradicts to Goyal's conclusion. It makes economic sense for some customers to order less quantity (or shorten the replenishment time interval) and to take the benefits of the permissible delay more frequently. (2) If a supplier wants to reduce his/her large level of inventory, then he/she should charge an excessive interest rate on his/her customer's outstanding amount after the credit term expires. Consequently, his/her customers will order to buy more quantity than the classical economic order quantity. As a matter of fact, these two managerial phenomena have been demonstrated in the decision making of using credit cards. For example, most credit card companies provide card holders 25 days of grace period, and charge 18–20% interest on the amount past due (ie, the second phenomenon). However, for a well-established credit card holder, he/she will take the benefit of 25 days of grace period constantly, but will not spend over his/her limit and face an excessive finance charge (ie, the first phenomenon).