Bassem K. Ouda

Improving operating theatre design using facilities layout planning

Operating theatre (OT) is one of the most critical departments within the hospital. In developing countries, no allocation of sufficient areas for OT, that results in sharing of many services in the same area while ignoring others. Facilities layout planning (FLP) is widely used in industrial engineering for designing block layout plans as it considers the assignment of services to the proper locations. Hence, FLP can solve management and hospital design problems, reduce nursing staff effort and improve overall healthcare environment. The problem presented in this study is an optimization one, which maximizes the subjective closeness rating between different services in the OT considering international standards. Some heuristic approaches have been developed for solving FLP problem; the most successful were based on graph theoretic concepts. This paper proposes a solution by generating an OT layout design based on the graph theoretic approach. It is divided into two sub-problems; the first one is the adjacency problem that defines the desirability of locating pair of spaces adjacent to each other. The second one is the block layout problem which was solved by using manual traditional qualitative technique (the spiral technique). The output of the technique was several possible designs and a layout score that was calculated for each design. This allows for selecting the most appropriate design for each end user. Computing the layout score, before and after reallocation of the OT spaces, resulted in an increase by 18.5% in the first hospital. For the second hospital, more services had to be added in addition to the reallocation process, this resulted in an increase in the layout score by 45%.

A simple quantitative model for replacement of medical equipment proposed to developing countries

Along the medical equipment life cycle, hospitals need to take decisions on medical equipment acquisition, maintenance, use and replacement on the basis of complete and reliable information. In this paper, the authors focus on the replacement criteria in developing countries where there is a lack of scientific, realistic and comprehensive assessment. In the proposed model, we use Fault Tree analysis (FTA) to model the replacement process using a set of indicators that impact directly or indirectly the replacement decision. We include the vendor support as a fundamental technical indicator in the analysis. This model considers the replacement decision as a final and undesirable event. Using probability theory, the medical equipment status is classified into 4 groups. According to the final event score, the replacement decision is approved or not. Neonatal Intensive Care Unit (NICU) equipment of 8 different types, along three years, are utilized to investigate the proposed model. Our model proposes a priority list of equipment that should be replaced. The type and number of equipment to be purchased is determined according to the available budget. The results show that 15% of equipment should be replaced, 33% need to be tested, 33% are under surveillance and 19% could be maintained.

A Fuzzy Logic Model for Medical Equipment Risk Classification

In developing countries, hospitals often suffer from insufficient funds and the lack of qualified technical personnel. This leads to a number of problems, among which is the improper and irregular maintenance of medical equipment. This situation calls for an effective approach to prioritize maintenance jobs based on certain importance criteria to make the best use of the available budget. In this article, we adopt the notion that medical equipment maintenance should be prioritized based on their risk level. Existing risk classification models express risk in terms of equipment function, maintenance requirements, and physical risk. They overlook other important factors such as the operational conditions of equipment. Other models have failed to classify critical devices as high risk because they did not incorporate the criticality of equipment as part of the global mission of the hospital. We argue that because hospitals in developing countries rarely implement coherent management standards, same level-of-care hospitals are not technologically equal. This research proposes a new risk assessment model based on fuzzy logic. Because fuzzy logic is closer to the human way of thinking, it is expected to improve the way devices are prioritized. The proposed model was tested on 136 different medical devices in 4 hospitals. Results show that, in certain cases, the same equipment type may have different risk scores depending on the operating conditions within the hospital. Meanwhile, some mission-critical equipment such as steam sterilizers, electrosurgical units, and hematology analyzers attain higher risk levels than obtained by existing models. We attribute this improvement to the fact that risk scores are now dynamic rather than static.

Analysis of fMRI Data Using Classical and Bayesian Approaches: A Comparative Study

Detection of active areas in a human brain by functional magnetic resonance imaging (fMRI) is a challenging problem in medical imaging. Moreover, determining the onset and end of activation signals can determine temporal relationships required for brain mapping. In this paper, a comparative study for detecting active areas in fMRI data using Bayesian and classical approaches was introduced. It has been found that using Bayesian model provides accurate and sensitive detection.

A system dynamics based model for medical equipment maintenance procedure planning in developing countries

Clinical Engineering (CE) department activities include acquisition, maintenance of medical instrumentation, health technology assessment, medical informatics, and risk management. In this work the authors focus on maintenance activities in developing countries where there is a lack of acquisition planning, assessment, budgeting planning, and critical equipment breakdown. In the past decade there has been an explosion in the use of system dynamics modeling in healthcare. In this paper a system dynamics based model for medical equipment maintenance is designed; it incorporates 8 key variables that influence the progress of medical equipment maintenance. This model shows the effect of changing a key variable on the others. This model is intended to maximize the quality and to minimize the cost and time of medical equipment maintenance. This is developed in a causal loop diagram, which is a cause and effect diagram. iThink software has been used in the development of this model, together with vensim in the development of the causal loop diagram. The results show that the critical variables for maintenance are the defect rate, breakdown rate, and maintenance cost. In conclusion the medical equipment maintenance cost determines the decision for acquiring new equipment. The type and number of equipment to be acquired is determined according to the available budget.

Denoising Functional MRI: A Comparative Study of Denoising Techniques (2D)

this paper presents a comparative study of different denoising techniques applied to functional magnetic resonance imaging (fMRI) data. The performance of these techniques was investigated using a simulated fMRI time series data with a set of predefined noise levels. The performance of these techniques was evaluated with respect to two quantitative measures; signal-to-noise ration (SNR), and shape preservation. As a result of the comparative study it has been found that denoising using Wavelet transform with reverse biorthogonal basis functions provides the best performance among all denoising techniques.

Optimal Design of Emergency Department in Mass Disasters

A disaster situation for a healthcare facility occurs when the need for medical treatment overwhelms the actual treatment capacity. Emergency department (ED) overcrowding occurs because of the sudden patient influx in a disaster situation. Consequences of overcrowding range from death and permanent disability to lengthening of treatment duration. Reduction of these adverse events through better ED design is the main motivation behind this study. There are 2 possible approaches to analyze this problem (1) by measuring patient waiting times in order to determine the bottlenecks in terms of duration and frequency or (2) by improving the physical design of the ED. In this study, we adopt the second approach for both newly designed and existing EDs. We construct new ED design using the facility layout planning algorithms. As for existing designs, we use simulation software in order to analyze the impact of new ideas, rules, and strategies without causing disruptions in ED service and before implementing any changes. The result shows a new construction layout design for ED using facility layout planning algorithm, in a manner conducive to overcoming the overcrowding consequences as much as possible. Also, the patient’s waiting time to receive treatment is decreased by 71% after improving the design for ED in Malta, which suffers from overcrowding by using simulation software. Finally, we do not claim that this is the best design for ED, but we can consider it as a step on the way toward a better ED design.

The effect of useful life and vendor performance on replacement decision of medical equipment

Medical equipment management life cycle considers various stages ranging from planning to disposal or replacement. Replacement decision is critical and essential stage of medical equipment. A variety of criteria contributes to make an intelligent replacement decision. We enhance the Fault Tree Analysis (FTA) model for replacement of medical equipment, considering a combination of technical, financial, and safety criteria. The considered criteria are; hazard and alerts, cost, useful life, and vendor support. Throughout this model, we classify medical equipment life status into four groups; replacement, test, surveillance and keep. According to the final event score, the replacement decision is approved or not. Neonatal Intensive Care Unit (NICU) equipment of 12 different types for 3 hospitals, along three years, is utilized to investigate the proposed model. Our model proposes a priority list of equipment that should be replaced. According to the analysis to the proposed factors, the useful life factor is found to be the dominant factor. In addition, a high correlation between vendor performance and expended costs is realized.

Adaptive denoising technique for robust analysis of functional magnetic resonance imaging data

A new adaptive signal-preserving technique for noise suppression in functional magnetic resonance imaging (fMRI) data is proposed based on spectrum subtraction. The proposed technique estimates a model for the power spectrum of random noise from the acquired data. This model is used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data for event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components. Moreover, further analysis using principal component analysis (PCA) and independent component analysis (ICA) shows a significant improvement in both convergence and clarity of results when the new technique is used. This suggests the value of the new technique as a useful preprocessing step for this type of signal.

Simple mathematical model for functional magnetic resonance imaging data

The objective of this study is to introduce a simple mathematical model for the brain image under both resting and activated states to facilitate both the understanding of the underlying neurophysiology and the realization of data sets. Two data sets were composed to simulate fMRI data. First set consists of a small spot that simulates the activated region superimposed on real baseline data. To simulate the signal enhancement, the hemodynamic response vector multiplies all pixels in the activated spot. Therefore, the resultant spots were added sequentially to the baseline images to create the first data set. The second set was formed by using the proposed model. The model took into account both random and physiological noise that are found in fMRI data. The random noise was assumed to vary from one frame to another while the physiological pattern was assumed of similar pattern throughout the brain with smooth spatial variations. A threshold cross-correlation technique was used on both data sets to compare the resultant activation maps. A falsehood measure was proposed and used as to test the accuracy of the activation detection. Finally, the results between the two data sets are compared to demonstrate the accuracy of the model.