A performance analysis of Brazilian public health: TOPSIS and neural networks application

Performance Assessment of Public Hospitals with the Entropy-Weighted TOPSIS Method: The Case of Turkey

Background: Hospitals make up the main cost factor of health systems and also face increasing pressure to improve efficiency. Improving the performance of hospitals, which are vital for public health, is very important for developing countries such as Turkey. Aims: We conducted this study to assess the performance of 544 secondary-level public hospitals in Turkey by geographical region. Methods: In the study, we used the entropy weighting method to obtain the objective weights of indicators with the evaluation criteria, and then we utilized the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to rank the performance of hospitals by geographical region. Results: The comparison of hospital performances by geographical region indicated that the highest and the lowest mean scores belonged to the Mediterranean Region and the Eastern Anatolia Region, respectively. Conclusions: The socio-economic differences between geographical regions in Turkey were in parallel with the findings of the study. Although there is a need for professional management in resource allocation and use to improve the performance of public hospitals, it is necessary to focus on the origin of the problems first.

Does the Type of Healthcare Financing Systems Matter for Efficiency?

This article aims to identify whether the type of healthcare system financing matters in terms of efficiency. The study covered 35 OECD countries, which follow either the Bismarck system or the Beveridge system. The span of analysis covers the year 2015. Data were derived from OECD statistics and Eurostat databases. In purpose to analyse the impact of the financing method on the overall efficiency of the healthcare system, the developed model of Anderson was employed. Thus, the model allowed us to examine both—the components of the healthcare system, resources, population characteristics, benefits and outcomes, and the relationship of individual components to each other, expressed through efficiency, effectiveness and equality. In addition, statistical methods were used such as descriptive analysis, the independent sample t-test and the Pearson correlation coefficient. It was found that countries of the Bismarck system possess more hospital beds and simultaneously more curative care bed days are provided. It could imply the existence of a supply-induced demand problem. In the case of efficiency, the Bismarck states were found to have a more efficient medical doctor as they provide more consultation per inhabitant than their Beveridge counterparts. However, the Beveridge states were found to have more efficient usage of curative care beds as their bed occupancy rate is higher than Bismarck counterparts.

Evaluation of health system resilience in 60 countries based on their responses to COVID-19

Introduction

In 2020, the COVID-19 epidemic swept the world, and many national health systems faced serious challenges. To improve future public health responses, it's necessary to evaluate the performance of each country's health system.

Methods

We developed a resilience evaluation system for national health systems based on their responses to COVID-19 using four resilience dimensions: government governance and prevention, health financing, health service provision, and health workers. We determined the weight of each index by combining the three-scale and entropy-weight methods. Then, based on data from 2020, we used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the health system resilience of 60 countries, and then used hierarchical clustering to classify countries into groups based on their resilience level. Finally, we analyzed the causes of differences among countries in their resilience based on the four resilience dimensions.

Results

Switzerland, Japan, Germany, Australia, South Korea, Canada, New Zealand, Finland, the United States, and the United Kingdom had the highest health system resilience in 2020. Eritrea, Nigeria, Libya, Tanzania, Burundi, Mozambique, Republic of the Niger, Benin, Côte d'Ivoire, and Guinea had the lowest resilience.

Discussion

Government governance and prevention of COVID-19 will greatly affect a country's success in fighting future epidemics, which will depend on a government's emergency preparedness, stringency (a measure of the number and rigor of the measures taken), and testing capability. Given the lack of vaccines or specific drug treatments during the early stages of the 2020 epidemic, social distancing and wearing masks were the main defenses against COVID-19. Cuts in health financing had direct and difficult to reverse effects on health systems. In terms of health service provision, the number of hospitals and intensive care unit beds played a key role in COVID-19 clinical care. Resilient health systems were able to cope more effectively with the impact of COVID-19, provide stronger protection for citizens, and mitigate the impacts of COVID-19. Our evaluation based on data from 60 countries around the world showed that increasing health system resilience will improve responses to future public health emergencies.

Socioeconomic Influence on Cardiac Mortality in the South Asian Region: New Perspectives from Grey Modeling and G-TOPSIS

Background

Measuring the potential socioeconomic factors of cardiac mortality is fundamental to identifying treatments, setting priorities, and effectively allocating resources to minimize disease burden. The study sought to present a methodology that explores the connections between urbanization, population growth, human development index (HDI), access to energy, unemployment, and cardiovascular disease (CVD) mortality within the South Asian Association for Regional Cooperation (SAARC) nations to mitigate the cardiac disease burden.

Methods

This investigation uses multiple-criteria decision-making methodologies to analyze data between 2001 and 2017 commencing with a mathematical grey incidence analysis (GIA) methodology to estimate weights and rank nations based on CVD mortality. Then, utilizing the conservative min-max model approach, we sought to determine which country contributes the most to CVD mortality among all South Asian nations. The grey preference by similarity to ideal solution (G-TOPSIS) method is adopted for further optimization by prioritizing the selected factors that have the greatest influence on CVD mortality.

Results

The estimated statistic highlights that, among SAARC nations, Pakistan has a significant proportion of the disease burden attributable to cardiac events. In addition, HDI showed a significant contribution in the reduction of CVD mortality, whereas unemployment showed a significant contribution in the rise of CVD mortality among all selected variables.

Conclusions

This investigation may facilitate researchers with a multiple-criteria decision-making roadmap to help them enhance the quality of their studies and their understanding of how to use multiple-criteria decision-making techniques to evaluate and prioritize the influencing factors of disease mortality in healthcare research. Further, the study outcomes provide additional practical knowledge for appropriate policy solutions.

Impacts of marketing decisions on delivery performance and flexibility of the operations area

Purpose

The purpose of this paper is to investigate the relations in the Marketing/Operations interface through the analysis of data of the operational reality of a Brazilian company with a low technological intensity. The study aims to quantify and determine the impacts of marketing decisions on delivery performance and on flexibility of the operations area.

Design/methodology/approach

A longitudinal case study was conducted and the variables used in the model were derived from established theories and were evaluated with artificial neural networks. The case of a food manufacturing company was selected to reflect the relations in the marketing/operations interface of a low technological intensity enterprise.

Findings

The results show that the decisions on Place/Channel, Price and Product dimensions of marketing exert a significant impact on flexibility and delivery performance of the operation area.

Research limitations/implications

The findings of the case study cannot be generalised and the outcomes are specific to just one firm. However, the approach lends itself to replication, particularly within low technological intensity companies.

Originality/value

Prior studies have focussed on coordination among functional areas as marketing and operations at higher levels of abstraction. The study contemplate empirical propositions through the data analysis of a company with a low technological intensity that can be used to improve managers' decisions and alignment in the Marketing/Operation Interface.

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy-TOPSIS methods

CONTEXT AND BACKGROUND

Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk of chronic virus carriers.

OBJECTIVES

Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.

METHODS

The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.

RESULTS

The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.

CONCLUSIONS

This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

Evaluation of lean practices in warehouses: an analysis of Brazilian reality

Purpose

This article aims to investigate the most applied lean warehouse practices in Brazilian warehouses.

Design/methodology/approach

To perform this research, three phases were conducted: a literature review, a multiple case study, and an analysis of lean warehouses practices implementation by an engineering committee. Thus, both qualitative and quantitative approaches were used. Additionally, the study has an applied nature, with an exploratory and descriptive character.

Findings

Results showed that regardless of the type of criterion used, the most implanted practices are those that do not involve investments in technology. On the other hand, practices like RFID and Cross Docking systems were not found in any of the operations, which shows numerous possibilities for improvement.

Originality/value

The main contribution of this article is to initiate a debate about the management and productivity of Brazilian warehouses, a theme still little explored by the academic community despite the importance that the logistic scenario represents for Brazil as an emerging country and leader in Latin America, participating actively in several global supply chains.

Going beyond health efficiency: What really matters?

Both citizens and policymakers demand the best possible results from a country's healthcare system. It is of utmost importance to accurately and objectively assess the efficiency of a healthcare system and to note the key indicators, where resources are lost, and possibilities for improvement. This paper evaluates the efficiency of health systems in 38 countries, mainly members of the Organization for Economic Co-operation and Development, using data envelopment analysis (DEA). In the first stage, bootstrapped Ivanovic distance is used to generate weights for the indicators, thus taking into consideration different country's goals, but not to the extent of reducing the possibility of comparison. The analysis shows that human resources are the most important health system resource and countries should pay special attention to developing and employing competent medical workers. The reorganization of human resources and the funds allocated to them could also increase efficiency. The second stage examines environmental indicators to find the causes of inefficiency. No proof is found that any one basic health system funding model produces better health outcomes than the others. Obesity is identified as a major issue.