A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

Formal punctured ribbons and two-dimensional local fields

We investigate formal ribbons on curves. Roughly speaking, formal ribbon is a family of locally linearly compact vector spaces on a curve. We establish a one-to-one correspondence between formal ribbons on curves plus some geometric data and some subspaces of two-dimensional local field.Comment: 38 pages, minor change

Ordu milleti

Taha Toros Arşivi, Dosya No: 182-Yahya Kemal Beyatlı. Not: Gazetenin "Günün Konuları" köşesinde yayımlanmıştır.İstanbul Kalkınma Ajansı (TR10/14/YEN/0033) İstanbul Development Agency (TR10/14/YEN/0033

Demand and Capacity Modelling of Acute Services Using Simulation and Optimization Techniques

The level of difficulty that hospital management have been experiencing over the past decade in terms of balancing demand and capacity needs has been at an unprecedented level in the UK. Due to shortage of capacity, hospitals are unable to treat patients, and in some cases, patients are transferred to other hospitals, outpatient referrals are delayed, and accident and emergency (A&E) waiting times are prolonged. So, it’s time to do things differently, because the current status quo is not an option. A whole hospital level decision support system (DSS) was developed to assess and respond to the needs of local populations. The model integrates every component of a hospital (including A&E, all outpatient and inpatient specialties) to aid with efficient and effective use of scarce resources. An individual service or a specialty cannot be assumed to be independent, they are all interconnected. It is clear from the literature that this level of generic hospital simulation model has never been developed before (so this is an innovative DSS). Using the Hospital Episode Statistics and local datasets, 768 forecasting models for the 28 outpatient and inpatient specialties are developed (to capture demand). Within this context, a variety of forecasting models (i.e. ARIMA, exponential smoothing, stepwise linear regression and STLF) for each specialty of outpatient and inpatient including the A&E department were developed. The best forecasting methods and periods were selected by comparing 4 forecasting methods and 3 periods (i.e. daily, weekly and monthly) according to forecast accuracy values calculated by the mean absolute scaled error (MASE). Demand forecasts were then used as an input into the simulation model for the entire hospital (all specialties). The generic hospital simulation model was developed by taking into account all specialties and interactions amongst the A&E, outpatient and inpatient specialties. Six hundred observed frequency distributions were established for the simulation model. All distributions used in the model were based on age groups. Using other inputs (i.e. financial inputs, number of follow ups, etc.), the hospital was therefore modelled to measure key output metrics in strategic planning. This decision support system eliminates the deficiencies of the current and past studies around modelling hospitals within a single framework. A new output metric which is called ‘demand coverage ratio’ was developed to measure the percentage of patients who are admitted and discharged with available resources of the associated specialty. In addition, a full factorial experimental design with 4 factors (A&E, elective and non-elective admissions and outpatient attendance) at 2 levels (possible 5% and 10% demand increases) was carried out in order to investigate the effects of demand increases on the key outputs (i.e. demand coverage ratio, bed occupancy rate and total revenue). As a result, each factor is found to affect total revenue, as well as the interaction between elective and non-elective admissions. The demand coverage ratio is affected by the changes in outpatient demands as well as A&E arrivals and non-elective admissions. In addition, the A&E arrivals, non-elective admissions and elective admissions are most important for bed occupancy rates, respectively. After an exhaustive review of the literature we notice that an entire hospital model has never been developed that combines forecasting, simulation and optimization techniques. A linear optimization model was developed to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from forecasting and forecasting-simulation) for each inpatient elective and non-elective specialty. In conclusion, these results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans. This hospital decision support system can become a crucial instrument for decision makers for efficient service in hospitals in England and other parts of the world

Fabrication and characterization of semiconductor core optical fibers for mid-infrared transmission

Transmission in the mid-infrared (2-15 µm) spectrum has many applications, including biomedical surgery, chemical detection, and countermeasures in defense systems, among others. This highlights the necessity of a suitable fiber for the mid-infrared (mid-IR) spectral range. Although fluoride and chalcogenide glasses have shown promise of relatively low transmission losses, they are prone to devitrification at room temperature leading to performance degradation. Semiconductors, such as germanium and silicon have low theoretical losses in the mid-IR spectral range, and are stable at room temperature, making semiconductor-core fibers worthy of exploration for mid-IR transmission. In this study, germanium (Ge)-core, borosilicate glass-cladded; silicon (Si)-core, silica-cladded; and Si-Ge alloy-core silica-cladded fibers were drawn in laboratory-made mini draw towers using the rod-in-tube method at a relatively low temperature of 1000◦C for borosilicate glass drawing and 1760◦C for silica drawing. 3 mm outer diameter core-drilled germanium and silicon rods were placed in borosilicate and silica glass tubes as preforms for the germanium-core and silicon-core fibers, respectively. 1.9 mm outer diameter core drilled germanium rods and 2 mm inner diameter, 3 mm outer diameter core drilled silicon tubes were placed concentrically in silica tubes as preforms of silicon-germanium alloy fibers. The core/cladding area ratio was controlled by adding concentric borosilicate/silica tubes to increase the preform diameter. Depending on the drawing speed and initial core/cladding diameter ratio, fibers with core diameters of 10-200 µm with cladding diameters of 130-500 µm, as well as canes with core diameters of 300-350 µm and cladding diameters of 1.3-1.4 mm were drawn. The drawn fibers were characterized by scanning/transmission electron microscopy (S/TEM), energy dispersive x-ray spectroscopy (EDX), x-ray diffraction (XRD) and electron backscatter diffraction (EBSD). It was found that there was minimal diffusion of oxygen and silicon from the cladding to the core in the Ge-core fibers. In the Si-core and Si-Ge alloy-core fibers, around 3 at % oxygen were found in the core, presumably due to enhanced diffusion at the higher drawing temperature of the silica-clad fibers. Optical characterization of the Ge canes, carried out using Fourier transform infrared spectroscopy (FTIR) in the 1.3-16 µm wavelength range, showed similar transmission characteristics, albeit with increased losses, over the entire wavelength range as the core drilled unprocessed germanium rod, even though the germanium core had undergone melting and re-solidification during the fabrication process. The transmission losses in the fibers were measured using two quantum cascade lasers, and were found to average 5.1 dB/cm for Ge-core fibers and 18.3 dB/cm for Si-core fibers in the 5.8-6.2 µm range. Transmission loss of Si-Ge alloy fibers was found to be 75 dB/cm at 6.1 µm. The higher losses of Si-Ge fibers can be attributed to compositional fluctuation in the core, due to the rapid cooling rate during fiber drawing. High temperature annealing of the fibers following by slow cooling homogenized the fiber core, and reduced the transmission losses to 28 dB/cm, but also introduced cracks. Non-linear properties of Ge-core fibers and canes were investigated using femtosecond pump-probe spectroscopy. Unprocessed 3 mm diameter rods exhibited the same detuning oscillations as 770 and 358 µm Ge-core canes and a 132 µm Ge-core fiber, indicating that the non-liner properties of the semiconductor cores were preserved during processing