Integrating Lean Six Sigma and discrete-event simulation for shortening the appointment lead-time in gynecobstetrics departments: a case study

Long waiting time to appointment may be a worry for pregnant women, particularly those who need perinatology consultation since it could increase anxiety and, in a worst case scenario, lead to an increase in fetal, infant, and maternal mortality. Treatment costs may also increase since pregnant women with diverse pathologies can develop more severe complications. As a step towards improving this process, we propose a methodological approach to reduce the appointment lead-time in outpatient gynecobstetrics departments. This framework involves combining the Six Sigma method to identify defects in the appointment scheduling process with a discrete-event simulation (DES) to evaluate the potential success of removing such defects in simulation before we resort to changing the real-world healthcare system. To do these, we initially characterize the gynecobstetrics department using a SIPOC diagram. Then, six sigma performance metrics are calculated to evaluate how well the department meets the government target in relation to the appointment lead-time. Afterwards, a cause-and-effect analysis is undertaken to identify potential causes of appointment lead-time variation. These causes are later validated through ANOVA, regression analysis, and DES. Improvement scenarios are next designed and pretested through computer simulation models. Finally, control plans are deployed to maintain the results achieved through the implementation of the DES-Six sigma approach. The aforementioned framework was validated in a public gynecobstetrics outpatient department. The results revealed that mean waiting time decreased from 6.9 days to 4.1 days while variance passed from 2.46 days2 to 1.53 days2

Identifying the most appropriate classifier for underpinning assistive technology adoption for people with dementia: an integration of Fuzzy AHP and VIKOR methods

Recently, the number of People with Dementia (PwD) has been rising exponentially across the world. The main symptoms that PwD experience include AQ1 impairments of reasoning, memory, and thought. Owing to the burden faced by this chronic condition, Assistive Technology-based solutions (ATS) have been prescribed as a form of treatment. Nevertheless, it is widely acknowledged that low adoption rates of ATS have hampered their benefits within a health and social care context. It is then necessary to effectively discriminate between adopters and non-adopters of such solutions to avoid cost implications, improve the life quality of adopters, and find intervention alternatives for non-adopters. Several classifiers have been proposed as advancement towards the personalisation of self-management interventions for dementia in a scalable way. As multiple algorithms have been developed, an important step in technology adoption is to select the most appropriate classification alternative based on different criteria. This paper presents the integration of Fuzzy AHP (FAHP) and VIKOR to address this challenge. First, FAHP was used to calculate the criteria and sub-criteria weights under uncertainty and then VIKOR was implemented to rank the classifiers. A case study considering a mobile-based self-management and reminding solution for PwD is described to validate the proposed approach. The results revealed that Easiness of interpretation (GW = 0.192) and Handling of missing data (GW = 0.145) were the two most important criteria. Furthermore, SVM (Qj = 1.0) and AB (Qj = 0.891) were concluded to be the most suitable classifiers for supporting ATS adoption in PwD

Choosing the most suitable classifier For supporting assistive technology adoption In people with Parkinson’s disease: a fuzzy Multi-criteria approach

Parkinson’s disease (PD) is the second most common neurodegenerative disorder which requires a long-term, interdisciplinary disease management. While there remains no cure for Parkinson’s disease, treatments are available to help reduce the main symptoms and maintain quality of life for as long as possible. Owing to the global burden faced by chronic conditions such as PD, Assistive technologies (AT’s) are becoming an increasingly common prescribed form of treatment. Low adoption is hampering the potential of digital technologies within health and social care. It is then necessary to employ classification algorithms have been developed for differentiating adopters and non-adopters of these technologies; thereby, potential negative effects on people with PD and cost overruns can be further minimized. This paper bridges this gap by extending the Multi-criteria decision-making approach adopted in technology adoption modeling for people with dementia. First, the fuzzy Analytic Hierarchy Process (FAHP) is applied to estimate the initial relative weights of criteria and sub-criteria. Then, the Decisionmaking Trial and Evaluation Laboratory (DEMATEL) is used for evaluating the interrelations and feedback among criteria and sub-criteria. The Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS) is finally implemented to rank three classifiers (Lazy IBk – knearest neighbors, Naïve bayes, and J48 decision tree) according to their ability to model technology adoption. A real case study considering is presented to validate the proposed approach

Discrete-Event Simulation for Performance Evaluation and Improvement of Gynecology Outpatient Departments: A Case Study in the Public Sector

Gynecology outpatient units are in charge of treating different gynecological diseases such as tumorous, cancer, urinary incontinence, gynecological pain, and abnormal discharge. On-time attention is thus needed to avoid severe complications, patient dissatisfaction, and elevated healthcare costs. There is then an urgent need for assessing whether the gynecology outpatient departments are cost-effective and what interventions are required for improving clinical outcomes. Despite this context, the studies directly concentrating on diagnosis and improvement of these departments are widely limited. To address these concerns, this paper aims to provide a Discrete-event Simulation (DES) modelling framework to help healthcare managers gain a better understanding of the gynecology outpatient services and evaluate improvement strategies. First, the patient journey through the gynecology outpatient service is mapped. To correctly represent the system uncertainty, collected data is then processed through input analysis. Third, the data is used to model and simulate the real gynecology outpatient unit. This model is later validated to determine whether it is statistically equivalent to the real system. After this, using performance metrics derived from the simulation model, the gynecology outpatient department is analyzed to identify potential improvements. We finally pretest potential interventions to define their viability during implementation. A case study of a mixed-patient type environment in a public gynecology outpatient unit is presented to verify the applicability of the proposed methodology. The results evidenced that appointment lead times could be efficiently reduced using this approach. © 2019, Springer Nature Switzerland AG

Improving the Performance in Occupational Health and Safety Management in the Electric Sector: An Integrated Methodology Using Fuzzy Multicriteria Approach

The electric sector is fundamental for the economic and social development of society, impacting on essential aspects such as health, education, employment generation, industrial production, and the provision of various services. In addition to the above, the growing trend in energy consumption worldwide could increase, according to expert estimates, up to 40% by 2030, which in turn increases the efforts of the public and private sector to meet increasing demands and increase access to energy services under requirements of reliability and quality. However, the electricity sector presents challenges and complexities, one of which is the reduction of health and safety risks for workers, service users, and other stakeholders. In many countries, this sector is classified as high risk in occupational safety and health, due to its complexity and the impact of accidents and occupational diseases on the health of workers, in infrastructure, in operating costs and competitiveness of the energy sector. Worldwide, there are rigorous regulations for the electricity sector, from local and national government regulations to international standards to guarantee health and safety conditions. However, it is necessary to develop objective and comprehensive methodologies for evaluating occupational safety and health performance that provides solutions for the electricity sector, not only to comply with standards and regulations also as a continuous improvement tool that supports the decision-making processes given the complexity of the industry and the multiple criteria that are taken into account when evaluating and establishing improvement strategies. In scientific literature, different studies focus on the analysis of accident statistics, the factors that affect accidents and occupational diseases, and the risk assessment of the sector. Despite these considerations, studies that focus directly on the development of hybrid methodologies for the evaluation and improvement of performance in occupational safety and health in the electrical sector, under multiple criteria and uncertainty are mostly limited. Therefore, this document presents an integrated methodology for improving the performance in occupational health and safety in the electric sector through the application of two techniques of Multi-criteria Decision Methods (MCDM) uses in environments under uncertainly. First, the fuzzy Analytic Hierarchy Process (FAHP) is applied to estimate the initial relative weights of criteria and sub-criteria. The fuzzy set theory is incorporated to represent the uncertainty of decision-makers’ preferences. Then, the Decision-making Trial and Evaluation Laboratory (DEMATEL) used for evaluating the interrelations and feedback among criteria and sub-criteria. FAHP and DEMATEL are later combined for calculating the final criteria and sub-criteria weights under vagueness and interdependence. Subsequently, we applied the proposed methodology in a company of the energy sector for diagnosis of performance in OHS to establish improvement proposals, the work path, and implementation costs. Finally, we evaluate the impact of the strategies applied in the improvement of the performance of the company

An integrated approach of multiple correspondences analysis (MCA) and fuzzy AHP method for occupational health and safety performance evaluation in the land cargo transportation

Land cargo transportation is one of the components of the logistics chain with high impact on economic and social development worldwide. However, problems such as top logistics costs, deficiencies in transportation infrastructure and the failure to adopt good operating practices in aspects such as quality, environment, and occupational safety and health affect the ability of companies to comply with the agreements, requirements, and regulations of the clients and other interested parties. One of the most relevant problems for the sector is associated with the high accident rates that make this medium less advantageous compared to other means of transport with impact on operational costs, on logistics indicators, on compliance with legal regulations and customer satisfaction. However, although there are legal standards and management standards in occupational safety and health, evaluating performance can become a difficult and subjective process, due to the complexity of the land cargo transportation and the different interest groups involved. Besides, there is little information in the literature that provides solutions for the industry. Therefore, this document presents an integrated approach between multi-criterion decision making models (MCDM) and the Multiple Correspondences Analysis (MCA) to facilitate the evaluation and improvement of occupational health and safety performance, with a logical process, objective, robust and using both qualitative and quantitative techniques, with real application in the land cargo transportation sector. First, the multivariate method of Multiple Correspondences Analysis (MCA) was used for the evaluation of a sample of companies in the industry, considering the factors and sub-factors identified in the first stage and performing correlational analyzes among the variables. Subsequently, a multicriteria decision-making model was designed to determine the factors and sub-factors that affect occupational health and safety performance through the technique of the Fuzzy Analytic Hierarchy Process (FAHP). Finally, improvement strategies are proposed based on the approaches suggested in this document

Definition of strategies for the reduction of operational inefficiencies in a stroke unit

Stroke disease is the second common cause of death in the world and is then of particular concern to policy-makers. Additionally, it is a meaningful problem leaving a high number of people with severe disabilities, placing a heavy burden on society and incurring prolonged length of stay. In this respect, it is necessary to develop analytic models providing information on care system behavior in order to detect potential operational inefficiencies along the stroke patient journey and subsequently design improvement strategies. However, modeling stroke care is highly complex due to the multiple clinical outcomes and different pathways. Therefore, this paper presents an integrated approach between Discrete-event Simulation (DES) and Markov models so that integrated planning of healthcare services relating to stroke care and the evaluation of potential improvement scenarios can be facilitated, made more logically robust and easy to understand. First, a stroke care system from Colombia was characterized by identifying the exogenous and endogenous variables of the process. Afterward, an input analysis was conducted to define the probability distributions of the aforementioned variables. Then, both DES and Markov models were designed and validated to provide deeper analysis of the entire patient journey. Finally, the possible adoption of thrombolytic treatment on patients with stroke disease was assessed based on the proposed approaches within this paper. The results evidenced that the length of stay (LOS) decreased by 12,89% and the mortality ratio was diminished by 21,52%. Evaluation of treatment cost per patient is also carried out

Prediciendo reingresos hospitalarios no planificados antes de 15 días: una aplicación de la regresión logística

Hospital readmission is considered a key research area for improving care coordination and achieving potential savings. This is important because hospital readmissions can have negative consequences in terms of good health and recovery for patients. It is thus important to significantly reduce such readmissions. Unfortunately, there isn't a one-size-fits-all solution to preventing hospital readmissions. There are many variables outside of hospitals' direct control, such as social determinants and patient lifestyle factors, impacting readmissions. Although several studies have been undertaken to investigate 30-day readmissions, predicting revisits in shorter intervals (e.g., within 15 days after discharge) is highly needed to capture hospital-attributable returns better and develop more effective improvement plans. Hence, the aim of this paper is three-fold: i) to develop a comprehensive experimental study for identifying factors affecting 15-day readmission risk, ii) to classify patients according to the risk of 15-day readmission using logistic regression, and iii) provide general recommendations to reduce the 15-day readmission risk considering different predictors. To this end, the patients' characteristics were first described. Then, the significance of potential predictors, their interactions, and their effects were assessed. After this, a logistic regression model was derived to predict the likelihood of 15-day readmission in each patient. Finally, general recommendations were provided to reduce 15-day revisits. A real case study in Colombia was considered to validate the proposed methodology

Estudio mineralógico de los sedimentos de la Laguna de El Hito (Cuenca)

Trabajo presentado en la XXXVII Reunión de la Sociedad Española de Mineralogía (SEM)celebrada en Madrid (España) el día 12 de Julio de 2018, en la Facultad de Ciencias Geológicas de la Universidad Complutense de MadridPeer reviewe

Quality improvement in ammonium nitrate production using Six Sigma methodology

Six sigma has been used in different industries to reach operational excellence. However, in the chemical industry, the application of this methodology is limited. This research presents an implementation of the six sigma method for ammonium nitrate (AN) content optimization in condensate production for a fertilizer company in Colombia. The paper aims to determine the levels for input variables in the process, to meet desirable standards for condensate quality in terms of ammonium nitrate content. Based on the DMAIC steps implementation, it was possible to establish the main variables affecting the condensate quality and their optimal levels to reach an ammonium nitrate content below 15,000 ppm. These results demonstrate the impact that a six sigma project may have on operational effectiveness and quality improvement for meeting the customer requirements