Simulation methods in the healthcare systems

International audienceHealthcare systems can be considered as large-scale complex systems. They need to be well managed in order to create the desired values for its stakeholders as the patients, the medical staff and the industrials working for healthcare. Many simulation methods coming from other sectors have already proved their added value for healthcare. However, based on our experience in the French heath sector (Jean et al. 2012), we found these methods are not widely used in comparison with other areas as manufacturing and logistic. This paper presents a literature review of the healthcare issue and major simulations methods used to address them. This work is design to suggest how more systematic creation of solutions may be performed using complementary methods to resolve a common issue. We believe that this first work can help to better understand the simulation approaches used for health workers, deciders or researchers of any responsibility level

A Literature Review of Cuckoo Search Algorithm

Optimization techniques play key role in real world problems. In many situations where decisions are taken based on random search they are used. But choosing optimal Optimization algorithm is a major challenge to the user. This paper presents a review on Cuckoo Search Algorithm which can replace many traditionally used techniques. Cuckoo search uses Levi flight strategy based on Egg laying Radius in deriving the solution specific to problem. CS optimization algorithm increases the efficiency, accuracy, and convergence rate. Different categories of the cuckoo search and several applications of the cuckoo search are reviewed. Keywords: Cuckoo Search Optimization, Applications , Levy Flight DOI: 10.7176/JEP/11-8-01 Publication date:March 31st 202

A general space-time model for combinatorial optimization problems (and not only)

We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable xilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example

Soft Computing on Medical-Data (SCOM) for a Countrywide Medical System Using Data Mining and Cloud Computing Features

This paper focuses on hosting and analyzing medical diagnostic data using soft computing. This is a project proposal for medical database system using soft computing, using data mining and cloud computing features. Data mining features are added to gain more control over the system. Cloud computing is a general term for anything that involves delivering hosted services over the Internet. The proposed database system can provide new delivery models to make healthcare more efficient and effective, and at a lower cost to technology budgets

Workflow Optimization of Kennestone Sterile Processing Department

Our team took on the task to create an optimization plan for the sterile processing department of Wellstar Kennestone Hospital in Marietta. It was brought to our attention that the department experiences difficulties that result in a reduction of on-time starts for surgeries. In order to increase on-time starts, our team’s primary goal was to reduce the total cycle time of instrument trays through the Sterile Process Department. The outdated system also faces the following challenges: inconsistent supply numbers, multiple supply returns, lack of organizational flow of instruments and inefficient work patterns in instrument processing. Our team made use of Six Sigma principles and Continuous Improvement tools like 5S, Kaizen events, and simulation software such as Arena Software to perform data analyses on the current system and suggest improvements. These suggestions included the enforcement of a standardized order of operations for dirty cart processing, a modified clean zone layout, and an additional cart washer to increase throughput of carts and containers. The results gathered from simulated data supported the implementation of a standardized order of operations and the modified clean zone layout but did not provide sufficient evidence to justify the investment in an additional cart washer

Improving hospital operations management to reduce ineffective medical appointments

The main objective of this study is to meet management aspirations by promoting waste reduction and consequently improving patients` experience in a Portuguese public hospital. These aspirations include increasing hospital service quality in a continuous and efficient way. This management mindset uncovered divergences between medical appointment and magnetic resonance imaging (MRI) exam scheduling that were generating waste for both the hospital and patients. The main aspects considered in this study were the patients’ medical expectations, the quality, and cost of service provided. One-year retroactive encrypted data from medical appointments and MRI requisitions were provided for the algorithm development. Outcomes obtained from the algorithm revealed a high percentage of medical appointments occurring without the respective MRI exam results. These outcomes exposed waste existence that was hitherto unknown by the administration. Thus, the main algorithm function is to analyze future data to previously alert ineffective medical appointments. This progress contributes to reducing wasted medical and patient time. In summary, the main contribution of this article is to allow hospital managers to cross-check data from different sectors to identify divergences in future medical consultations that require exams or results of clinical analysis.This work was supported by the Fundacao para a Ciencia e a Tecnologia [POCI-01-0145-FEDER-030299]; Fundacao para a Ciencia e a Tecnologia [UIDB/00319/2020]

VA Linen Distribution Optimization

Linen is a backstage service that is critical for a hospital’s functioning. Our team created a refill and distribution system to optimize linen use at the VA Boston Healthcare System-West Roxbury Campus, by applying lean concepts to improve process efficiency with the goal of providing the best patient care. A trial implementation was conducted to evaluate the feasibility and efficiency of the design and to inform recommendations for future implementation of the project

ME-EM 2007 Annual Report

Table of Contents Research Expansion Research Groups Faculty & Staff Students Alumni Resources Graduates Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1011/thumbnail.jp