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Enhancing the Performance and Efficiency of Healthcare Systems Using Industrial Economic Principles and Statistical Techniques
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.ORCID iD: 0000-0003-4620-7472
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Optimizing healthcare systems has become more crucial in recent years due to escalating healthcare demands and economic constraints. This dissertation employed industrial economic principles and advanced statistical methods to analyze the performance and efficiency of healthcare systems in Europe. The study comprehensively evaluated how healthcare systems can improve service delivery while maintaining cost-effectiveness by integrating industrial economic theories with empirical data. The dissertation was organized into a series of analyses, each focusing on different aspects of healthcare system performance and efficiency. Statistical techniques such as time series and multilevel regression analyses were used to examine the interplay between healthcare resources, healthcare systems, and health outcomes across European nations. The two main healthcare models in Europe, the Beveridge and Bismarck models, were compared in terms of performance, efficiency, and resource allocation. The main findings revealed that optimal resource allocation and efficient management practices can significantly enhance the performance of healthcare systems. The study indicated that a healthcare system's efficiency heavily depends on its ability to adjust resource allocation to changes in demographic and economic conditions. Additionally, this dissertation forecasted future demands for healthcare services, social security benefits, and pensions by incorporating macro-level determinants such as economic growth, unemployment rates, and population density into the forecasting models. The accuracy of these models provided valuable insights for policymakers to effectively plan for future healthcare, social security, and pension needs. Moreover, this dissertation employed an economic evaluation to compare the cost-effectiveness of Beveridge-type and Bismarck-type healthcare systems over the past twenty years. An effectiveness ratio was applied to measure the relationship between inputs (medical spending) and outputs (health outcomes). These cost-effectiveness ratios demonstrated which healthcare system yields better health outcomes for each dollar spent. Furthermore, the findings indicated that the efficiency of healthcare systems varies from country to country, highlighting the challenges of adopting a universal approach to healthcare policy. This dissertation contributes to the academic field by demonstrating how industrial economic principles can be applied to improve the performance and efficiency of healthcare systems. It offered a framework for evaluating healthcare performance and efficiency, which can inform future reforms to achieve sustainable, high-quality healthcare services. Ultimately, this study promotes a dynamic approach to healthcare planning that adapts to technological advancements and demographic changes to enhance population health.

 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2024. , p. 370
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 10
Keywords [en]
Healthcare system efficiency, industrial economics, performance measurement, healthcare resource allocation, healthcare model comparison, forecasting healthcare demand
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Industrial Economics a nd Managemen
Identifiers
URN: urn:nbn:se:bth-26122ISBN: 978-91-7295-483-0 (print)OAI: oai:DiVA.org:bth-26122DiVA, id: diva2:1852635
Public defence
2024-06-13, J1630, Blekinge Institute of Technology, 371 79 Karlskrona, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2024-04-19 Created: 2024-04-18 Last updated: 2024-05-15Bibliographically approved
List of papers
1. Using Time Series Analysis to Forecast the Future Life Expectancy Trends in Europe by Considering Macro-Level Determinants
Open this publication in new window or tab >>Using Time Series Analysis to Forecast the Future Life Expectancy Trends in Europe by Considering Macro-Level Determinants
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background Recently, there has been an increasing interest in forecasting the future trends of the population. Knowing the population trends is very important for policymakers to plan for their healthcare, social service, and pension systems. Life expectancy has increased more than double in many countries over the past two centuries. Many countries have increased the retirement age by linking it with life expectancy. Most previous research only used past life expectancy trends to forecast the future direction of life expectancy or included lifestyle behaviors such as smoking, drinking, and so on to predict the future trend. However, life expectancy is a health outcome indicator to represent population health. When discussing population health, it should be seen from a macro-level perspective. Prior studies revealed that economic growth, unemployment, and population density were macro-level determinants that can influence population health. Thus, forecasting life expectancy by considering these determinants could project it closer to reality.

Methods This research employed Vector Error Correction Model (VECM) by considering macro-level determinants to predict future life expectancy at birth trends. Moreover, the generalized impulse response function (GIRF) was applied to explain the reaction of life expectancy at birth to those macro-level determinants, and the generalized forecast error variance decomposition (GFEVD) was analyzed to measure the amount of information of those macro-level determinants contribute to future life expectancy at birth.

Results GIRF analysis revealed that life expectancy at birth has a positive relationship with economic growth and unemployment in many countries. However, population density has a positive relationship in some countries and a negative relationship in some countries. The results of GFEVD indicated that in many European countries, economic growth and unemployment have a medium effect on explaining future life expectancy at birth. At the same time, population density has little effect on explaining future life expectancy at birth. Conclusions The findings suggested that the population will live longer in many countries. Hence, policymakers must plan their healthcare, pension, and social services systems well to support the increasing demand in the future.

Keywords
Time series analysis, Vector Error Correction Model (VECM), forecasting, impulse response function, life expectancy at birth, Europe
National Category
Economics
Research subject
Industrial Economics a nd Managemen
Identifiers
urn:nbn:se:bth-26119 (URN)
Available from: 2024-04-18 Created: 2024-04-18 Last updated: 2024-04-25Bibliographically approved
2. Prediction of Life Expectancy at Birth in Europe: A Comparison of Univariate and Multivariate Time Series Forecasting
Open this publication in new window or tab >>Prediction of Life Expectancy at Birth in Europe: A Comparison of Univariate and Multivariate Time Series Forecasting
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background The rising life expectancy at birth marks a significant victory for modern medicine and public health initiatives. Advances in medical technology, improved healthcare services, enhanced nutrition, and greater awareness of health and wellness have all contributed to prolonging the average human lifespan. In recent decades, many countries, particularly in Europe, have seen life expectancy at birth surpass previous historical limits. This increase significantly affects public health, economics, and social structures. As populations age, the demand for healthcare services, long-term care, and pensions increases, adding strain on both public and private sectors. These shifts necessitate modifications in social services, healthcare delivery, and pension plans to accommodate an older demographic. Therefore, accurate forecasts of life expectancy at birth are crucial for governments and organizations to make well-informed decisions and effectively plan for future demographic changes. Forecasting life expectancy at birth has become central to demographic research due to its pivotal role in planning and policy formulation.

Methods Numerous studies have introduced various methods and techniques to predict future life expectancy, utilizing a broad array of statistical tools and models. These methods range from simple linear projections based on historical data to complex models that incorporate multiple variables and sophisticated statistical techniques. Despite the availability of numerous forecasting methods, there is a notable gap in the literature concerning systematic evaluations of these techniques, as most studies focus on creating new forecasting methods or refining existing models to increase predictive accuracy. This study addresses this gap by examining the performance of two principal econometric models, namely the Autoregressive Integrated Moving Average (ARIMA) and the Vector Error Correction (VEC) model, in predicting future life expectancy at birth in Europe. These models represent different approaches to time series forecasting, each with distinct assumptions and capabilities. This research used Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Diebold-Mariano (DM) tests to compare the forecast accuracy of the two models.

Results The results from RMSE and MAE showed that the ARIMA model better predicted future life expectancy at birth for half of the European countries in this study, while the VEC model was more accurate for the other half. The DM test indicated statistically significant differences in forecast accuracy between the ARIMA and VEC models in some European countries.

Conclusions This study aimed to determine which model delivers the most accurate and reliable predictions by comparing these models in predicting future life expectancy at birth across several European countries. Such insights are crucial for policymakers, planners, and researchers developing health systems and social policies tailored to an aging European population.

Keywords
life expectancy at birth, ARIMA, VECM, forecasting accuracy, Diebold-Mariano test
National Category
Computational Mathematics
Research subject
Industrial Economics a nd Managemen
Identifiers
urn:nbn:se:bth-26121 (URN)
Available from: 2024-04-18 Created: 2024-04-18 Last updated: 2024-04-25Bibliographically approved
3. Impact of Healthcare Resources and Healthcare Systems on Population Health in European Countries
Open this publication in new window or tab >>Impact of Healthcare Resources and Healthcare Systems on Population Health in European Countries
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background Recently, the demand for care has risen, while in contrast, healthcare resources remain limited. These resources include health expenditure, the number of physicians, nurses, and hospital beds. Many studies have revealed that healthcare resources are one of the most critical factors contributing to a population's health status. The healthcare system plays a key role in transforming these resources into health outcomes, which are widely used as indicators to measure population health and the performance of healthcare systems. Previous work has primarily investigated the relationship between health expenditure or the number of doctors and population health. However, the association between healthcare resources as a whole has yet to be widely examined.

Methods This study employed multilevel regression to examine the impact of healthcare resources and healthcare systems on population health across 25 European countries. The healthcare systems of these countries can be broadly categorized into Beveridge-type and Bismarck-type systems. Additionally, this study used descriptive statistics and Welch's t-test to observe the allocation patterns of healthcare resources and to compare the performance of the two types of healthcare systems.

Results The regression analysis indicated that health expenditure per capita and the number of physicians and nurses were positively correlated with life expectancy at birth, while the number of hospital beds was negatively correlated with it. In contrast, infant mortality was negatively associated with health expenditure per capita and the number of physicians and nurses, and positively associated with the number of hospital beds. However, the healthcare systems did not show statistical significance in the models for life expectancy at birth or infant mortality. Welch's t-test revealed that the Beveridge-type healthcare system performed better than the Bismarck-type system.

Conclusions The findings suggest that oversupplying healthcare resources could harm health outcomes. Countries using the same healthcare systems tend to allocate healthcare resources similarly, and these allocations might affect the performance of the healthcare systems. Thus, policymakers should consider this when deciding how to allocate funding to healthcare resources and when selecting or shifting to a healthcare model for their countries.

Keywords
Public health, population health, healthcare system performance, healthcare resources, health outcomes, multilevel regression model
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Industrial Economics a nd Managemen
Identifiers
urn:nbn:se:bth-26116 (URN)
Available from: 2024-04-18 Created: 2024-04-18 Last updated: 2024-04-25Bibliographically approved
4. Using an Economic Evaluation to Compare the Effectiveness of Healthcare Systems in Europe
Open this publication in new window or tab >>Using an Economic Evaluation to Compare the Effectiveness of Healthcare Systems in Europe
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background Worldwide, healthcare systems are struggling with rising health expenditures and inefficient healthcare systems. Moreover, an aging population, longer lifespans, and infectious diseases put more pressure on healthcare systems. Healthcare's performance, efficiency, and effectiveness need to be evaluated to prevent a further rise in health expenditure. In Europe, healthcare systems can be broadly categorized into two models: the Beveridge model and the Bismarck model. These two healthcare systems' performance, efficiency, and effectiveness have been discussed over the past decade. Many researchers have tried to determine which healthcare system performs better and more effectively. However, a clear conclusion has yet to be reached.

Methods This research compared the performance and effectiveness of 27 European countries using Beveridge-type and Bismarck-type healthcare systems from 2000 to 2020. This research used life expectancy at birth as a health outcome indicator to represent population health and evaluate the performance of two healthcare systems. The health expenditure per capita PPP was used as medical spending. A Welch's t-test was first employed to measure the performance of the two healthcare systems. This study then applied economic evaluation to compare the effectiveness of the two healthcare systems.

Results Welch’s t-test results demonstrated that life expectancy at birth in countries using the Beveridge-type healthcare system was statistically significantly higher than in countries using the Bismarck-type healthcare system. The effectiveness ratio of the period from 2000 to 2020 showed that the Beveridge-type healthcare system has a higher value of medical spending than the Bismarck-type healthcare system.

Conclusions The results revealed that the Beveridge-type healthcare system performed better and was more cost-effective than the Bismarck-type healthcare system. Policymakers should be careful when choosing or shifting a healthcare model for their countries.

Keywords
value of medical spending, economic evaluation, cost-effectiveness ratio, cost per year of life gained, life expectancy at birth
National Category
Economics
Research subject
Industrial Economics a nd Managemen
Identifiers
urn:nbn:se:bth-26117 (URN)
Available from: 2024-04-18 Created: 2024-04-18 Last updated: 2024-04-25Bibliographically approved

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