Evaluating Healthcare System Efficiency of OECD Countries: A DEA-Based Study. In: Kahraman C, Topcu YI, editors. Operations Research Applications in Health Care Management. Cham: Springer International Publishing; 2018. pp. 141-58. [DOI: 10.1007/978-3-319-65455-3_6]

Comparative Efficiency Analysis of OECD Health Systems: FDH vs. Machine Learning Approaches with Efficiency Analysis Trees (EAT and RFEAT)

BACKGROUND

As health expenditure continues to rise due to income growth, technological advancements, and an aging population, it has become increasingly important to accurately measure and improve the efficiency of health systems. This is because financial resources are limited, and the allocation of resources can significantly influence the quality of health systems and health outcomes.

METHODS

This study applies machine learning techniques-Efficiency Analysis Trees (EAT) and Random Forest for Efficiency Analysis Trees (RFEAT)-to evaluate the efficiency of health systems in 36 OECD countries, comparing the results with those from the traditional free disposal hull (FDH) method.

RESULTS

Analysis shows high discrimination power in the order of RFEAT, EAT, and FDH. The correlation in efficiency rankings shows more than 80% similarity between RFEAT and EAT, while both show less than 80% similarity with FDH. According to RFEAT estimates, the countries with the highest efficiency are South Korea, Switzerland, and Costa Rica, whereas the United States, Lithuania, and Latvia are identified as the least efficient. The group-level analysis reveals that Asian countries, on average, perform more efficiently followed by Oceania, Europe, and the Americas. The groups with higher out-of-pocket healthcare expenditures per capita tend to show slightly better efficiency and the group with the smallest elderly population proportion exhibits the highest average health system efficiency.

CONCLUSION

Traditional methods like FDH are prone to inefficiency underestimation, especially in small samples with multiple variables. This study demonstrates the potential of machine learning approaches like EAT and RFEAT to provide more reliable efficiency estimates. These methods can help policymakers make better resource allocation decisions by mitigating inefficiency underestimation and offering greater discrimination power.

Assessing the performance of Portuguese public hospitals before and during COVID-19 outbreak, with optimistic and pessimistic benchmarking approaches

The COVID-19 pandemic had a profound impact on the tertiary sector, particularly in healthcare, which faced unprecedented demand despite the existence of limited resources, such as hospital beds, staffing resources, and funding. The magnitude and global scale of this crisis provide a compelling incentive to thoroughly analyse its effects. This study aims to identify best practices within the Portuguese national healthcare service, with the goal of improving preparedness for future crises and informing policy decisions. Using a Benefit-of-the-Doubt (BoD) approach, this research constructs composite indicators to assess the pandemic's impact on the Portuguese public hospitals. The study analyzes monthly data from 2017 to May 2022, highlighting critical trends and performance fluctuations during this period. The findings reveal that each COVID-19 wave led to a decline in hospital performance, with the first wave being the most severe due to a lack of preparedness. Furthermore, the pandemic worsened the disparities among examined hospitals. Pre-pandemic top performers in each group improved their performance and were more consistently recognized as benchmarks, with their average benchmark frequency increasing from 66.5% to 83.5%. These top entities demonstrated greater resilience and adaptability, further distancing themselves from underperforming hospitals, which saw declines in both performance scores and benchmark frequency, widening the performance gap. The superior performance of top entities can be attributed to pre-existing strategic tools and contextual factors that enabled them to withstand the pandemic's challenges more effectively. HIGHLIGHTS: • The pandemic aggravated the differences between the hospitals examined. • The top-performing entities further distanced themselves from the remaining entities after the pandemic • Entities considered benchmarks before the pandemic remained the same, and became even more consistent during the pandemic. • The top-performing entities achieved higher scores than their pre-pandemic performance levels. • Benchmarking models for composite indicators with diverse decision-making preferences, and treatment of imperfect knowledge of data.

Efficiency and productivity analysis of maternal and infant healthcare services in Sub-Saharan Africa

The paper examines the efficiency and productivity of Sub-Saharan African (SSA) countries towards maternal and infant healthcare services between 2015 and 2019. Data envelopment analysis is utilised to evaluate efficiency, and Malmquist-Luenberger's (ML) productivity estimation is employed for productivity analysis. The results indicate inefficiency in SSA maternal and infant healthcare services. Average efficiency is pegged at 85%, and 60% of the countries evaluated had below-average efficiency. Effects of socioeconomic dynamics of countries were analysed. Preliminary estimations on the impact of Gross domestic product (GDP), education, urban population, and total population on efficiency are not significant. Although GDP and education sometimes show that they influence efficiency positively. Sensitivity analysis indicates efficiency to be more responsive to health expenditure, as well as to nurses and midwives. ML Productivity decomposition into technical efficiency change and technological change indicates improvement in technical efficiency as the principal driver of efficiency and productivity. Policy recommendations are made in line with the findings, requirements, and constraints of SSA countries.

A network Data Envelopment Analysis to estimate nations' efficiency in the fight against SARS-CoV-2

The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages - population, contagion, triage, hospitalisation, and intensive care unit admission -, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant.

A network Data Envelopment Analysis to estimate nations' efficiency in the fight against SARS-CoV-2

The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages - population, contagion, triage, hospitalisation, and intensive care unit admission -, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant.

Measuring the efficiency of the healthcare sector in Poland – a window-DEA evaluation

Purpose

The main aim of the study was to measure and assess the efficiency of the healthcare system in Poland.

Design/methodology/approach

An output-oriented Data Envelopment Analysis model with a 2-years window analysis extension was used between 2013 and 2018. The analysis was completed with a determination of the sources of productivity changes (between the first and last year of the study period) and factors that influence efficiency.

Findings

Efficient regions have been identified and the spatial diversity in their efficiency was confirmed. The study identified individual efficiency trends together with “all-windows” best and worst performers. Using panel modeling, it was confirmed that the efficiency of health protection is influenced by, among others, accreditation certificates, the length of the waiting list or the number of medical personnel.

Research limitations/implications

Although the analysis was conducted at the voivodeship level (NUTS2), which was fully justified, it would be equally important to analyze data with a lower aggregation level. It would be extremely valuable from the perspective of difficulties faced by the healthcare system in Poland.

Practical implications

The identification of areas and problems affecting the efficiency of the healthcare system in Poland may also be a hint for other countries with similar system solutions that also struggle with the same problems.

Originality/value

The paper explains the efficiency of the country's healthcare system while also paying attention to changes in its level, factors influencing it, spatial diversity and impact on the sector functioning.

Health systems efficiency in Eastern Mediterranean Region: a data envelopment analysis

BACKGROUND

One of the most important issues in public policy and welfare state is health care. Poor management leads to the waste of resources, including money, human resources, facilities, and equipment.

AIMS

This paper seeks to answer the question of which eastern Mediterranean countries are more effective in allocating their health resources, and does Iran, in relation to those countries, have an effective health system.

METHODS

This study examined technical efficiency among eastern Mediterranean countries in 2018. Data were extracted from Global Health Observatory data World Health Organization. We applied input-oriented Data Envelopment Analysis (DEA) models to estimate efficiency scores. Inputs are Physicians density per 10,000 populations, Total hospital beds per 10,000 populations, Current expenditure on health, % of gross domestic product and outputs are infant survival rate and Life expectancy.

RESULTS

The most efficient health systems in the eastern Mediterranean were Bahrain, Egypt, Iran, Lebanon, Morocco, Oman, Pakistan, Qatar, Tunisia and the United Arab Emirates. The inefficient countries are Iraq, Jordan, Kuwait, Libya, Palestine and Saudi Arabia.

CONCLUSIONS

Among the efficient countries, one category of high-entry countries such as Bahrain and Qatar with high input especially in health expenditure had higher output. The second group of countries with lower inputs such as Iran and Morocco has been able to produce similar output with other countries. Also, inefficiency in countries such as Saudi Arabia can be attributed to this with higher input such as health expenditure has lower output such life expectancy and infant survival rate.

Assessing the efficiency of Iran health system in making progress towards universal health coverage: a comparative panel data analysis

BACKGROUND

Building upon decades of continuous reforms, Iran has been implementing various initiatives to reach universal health coverage (UHC). Improving efficiency is a crucial intermediate policy objective for UHC. Therefore, this article aimed to measure the efficiency and productivity changes of the Iranian health system in making progress towards UHC during 2010-2015 in comparison with 36 selected other upper-middle-income countries.

METHODS

We used panel data to measure the variations in technical efficiency (TE) and total factor productivity (TFP) through an extended data envelopment analysis (EDEA) and Malmquist productivity index, respectively. General government health expenditure (GGHE) per capita (International dollar) was selected as the input variable. Service coverage of diphtheria, tetanus and pertussis; family planning; antiretroviral therapy; skilled attendants at birth; Tuberculosis treatment success rate; and GGHE as  % of total health expenditure (THE) were considered as output variables. The data for each indicator were taken from the Global Health Observatory data repository and World Development Indicator database, for 6 years (2010-2015).

RESULTS

The TE scores of Iran's health system were 0.75, 0.77, 0.74, 0.74, 0.97, and 0.84 in the period 2010-2015, respectively. TFP improved in 2011 (1.02), 2013 (1.01), and 2014 (1.30, generally). The overall efficiency and TFP increased in 2014. Changes made in CCHE per capita and GGHE/THE attributed to the increase of efficiency.

CONCLUSION

There is a growing demand for efficiency improvements in the health systems to achieve UHC. While there are no defined set of indicators or precise methods to measure health system efficiency, EDEA helped us to draw the picture of health system efficiency in Iran. Our findings highlighted the essential need for targeted and sustained interventions, i.e., allocation of enough proportion of public funds to the health sector, to improve universal financial coverage against health costs aiming to enhance the future performance of Iran's health system, ultimately. Such tailored interventions may also be useful for settings with similar context to speed up their movement towards improving efficiency, which in turn might lead to more resources to reach UHC.

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.

The effects of the economic recession on spending on private health insurance in Spain

The paper seeks to analyse the evolution of expenditure on private health insurance (PHI) in Spain. We consider the factors that influence PHI demand and level of spending before and during the economic recession, along with identifying the effect of the recession on these factors. The data is obtained from the Spanish Family Budget Survey (SFBS) for 2006 and 2012. Due to the data structure and the demand function, the analysis is performed using a sample selection model in order to avoid sample selection bias. We estimate three models: a pre-recession model (2006), a model for the recession period (2012) and a third one covering both periods (2006 and 2012) and where we include a dummy variable that establishes the effect of the economic recession. The results show that the effect of the economic recession on PHI demand is not significant, but it is on the level of spending.