This report analyses the efficiency of hospitals in England and Germany. Data Envelopment Analysis (DEA) is utilised to estimate a best practice frontier and to evaluate the performance characteristics of different hospitals in the two countries. The measurement categories of input factors are represented by the hospital size (amount of beds), number of wards, number of employed medical specialists, sum of total inpatient admissions, demographic environment (population density in population/km²), and one output factor (mortality rates).
Regarding these factors, the main findings reveal that the allocation of inefficient hospitals in England is broadly spread and, therefore, the mean efficiency value of hospitals in England is ≈44% (whereas 100% describes efficient units). In contrast, the mean efficiency value of German hospitals equates to ≈91%. Consequently, the allocation of inefficient German hospitals is less spread and the overall performance of hospital health supply is more efficient in Germany compared to England.
Most remarkably, the amount of inpatient admissions is one of the main drivers for efficiency and especially the English hospitals suffer from high numbers of inpatient admissions, which may be reduced due to improvements in the primary healthcare supply provided by general practitioners.
Table of Contents
1 Introduction
2 Literature Review
3 Research Methodology
4 Data Envelopment Analysis
4.1 Definition and Classification
4.2 Mathematical Fundamentals
4.3 Performance measurement in healthcare
5 Data collection and explanation
5.1 Hospital funding bodies
5.2 Identification of the measurement categories
5.2.1 Input Data
5.2.2 Output Data
6 Data analysis
6.1 Overall Efficiency Distribution
6.1.1 Setup
6.1.2 Findings
6.1.3 Efficiency Plot
6.1.4 Input and Output Relations
6.1.5 Efficiency Patterns
6.2 Efficiency Frontier 'Convex Cone'
6.2.1 Setup
6.2.2 Findings
6.3 Best Performance Hospitals
6.3.1 Reference Set
6.3.2 Potential Improvements
7 Conclusion
Research Objectives & Topics
This report aims to analyze and compare the efficiency of hospitals in England and Germany by utilizing Data Envelopment Analysis (DEA). The primary research objective is to identify best-performance hospitals and derive insights into how these entities apply input factors to achieve optimal outputs, thereby providing lessons for lower-performing institutions in both healthcare systems.
- Comparative analysis of hospital efficiency in England and Germany.
- Application of Data Envelopment Analysis (DEA) as a benchmarking tool.
- Identification of key drivers of efficiency, such as hospital size and patient admission rates.
- Evaluation of the impact of different healthcare funding systems on hospital performance.
Excerpt from the Book
1 Introduction
Contemporary healthcare providers in different healthcare markets experience rapid growths in healthcare costs due to increasing complexity and competitiveness (Barnum et al., 2011). Maniadakis et al. (2009) add that especially western countries recorded a substantial trend of increasing healthcare costs during the last four decades and that this trend is expected to continue in the future. Consequently, today's healthcare managers have to face the challenging task of providing high quality care with more and more limited resources (Ozcan, 2009). This is where the efficiency analysis of healthcare providers (e.g. hospitals) comes into play, with the aim to identify best performance hospitals. Moreover, best practices of how these best performance hospitals apply input factors to produce certain measurable output can be derived.
Further, the average and low performance hospitals can adapt the identified best practices and learn how to improve their own efficiency of healthcare delivery. Although this research does not directly cover healthcare cost aspects within the efficiency analysis, it can be assumed that there is a correlation between the amount of inputs used and the associated costs. Thus, the more inputs being used to produce a certain output, the more costs can be associated. Barros (2003) emphasises the importance of healthcare efficiency of hospitals as these units represent one important part of the healthcare system. Therefore, improvements in more efficient hospital healthcare delivery may positively affect the overall healthcare system of a nation.
Summary of Chapters
1 Introduction: This chapter highlights the rising costs in healthcare systems and the necessity for managers to use tools like Data Envelopment Analysis (DEA) to identify best practices for hospital efficiency.
2 Literature Review: The chapter reviews existing studies on healthcare efficiency, emphasizing the complexity of measuring performance and the ongoing debate regarding the most appropriate methods and variables to evaluate hospital quality.
3 Research Methodology: This section details the inductive and mono-quantitative research approach, utilizing data from 140 English and 100 German hospitals to conduct a cross-sectional benchmarking analysis via the Frontier Analyst program.
4 Data Envelopment Analysis: This chapter introduces the theoretical framework of DEA, covering its definitions, mathematical foundations, and its specific application and challenges within the healthcare sector.
5 Data collection and explanation: This chapter outlines the data collection process, identifying key input factors—such as hospital size, staff, and admissions—and output factors like mortality rates, while addressing the need for adjustments due to differences in international reporting.
6 Data analysis: The core analytical section where DEA is applied to compare hospital efficiency distributions, identify efficiency frontiers, and determine potential areas for performance improvement in both countries.
7 Conclusion: The concluding chapter summarizes the main findings, suggesting that English hospitals should focus on reducing inpatient admissions while German hospitals may improve efficiency by optimizing their medical specialist allocation.
Keywords
Data Envelopment Analysis, DEA, Hospital Efficiency, Benchmarking, Healthcare Management, England, Germany, Mortality Rates, Inpatient Admissions, Performance Frontier, Healthcare Quality, Resource Allocation, Best Practice, Decision-Making Units, Health Policy
Frequently Asked Questions
What is the fundamental focus of this dissertation?
The dissertation focuses on analyzing and comparing the efficiency of hospital systems in England and Germany using quantitative benchmarking techniques to identify best-practice models.
What are the primary themes addressed in the research?
The research addresses themes such as healthcare system comparisons, the application of Data Envelopment Analysis, the impact of hospital size and staff on efficiency, and the role of patient admission rates in cost management.
What is the central research question?
The study seeks to answer: "What lessons can be learned from each other?" regarding hospital efficiency between the English and German healthcare systems.
Which scientific methodology is employed?
The author employs a mono-quantitative research methodology, specifically using Data Envelopment Analysis (DEA) via the Frontier Analyst software to measure and compare the performance of 240 hospitals in total.
What is discussed in the main body of the work?
The main body discusses the theoretical background of DEA, the data collection strategies for English and German hospitals, the analysis of efficiency distributions, and the identification of best-practice benchmarks.
Which keywords best characterize this research?
The research is best characterized by keywords such as DEA, Hospital Efficiency, Healthcare Benchmarking, Resource Allocation, and Comparative Healthcare Analysis.
Why is the "convex cone" approach used in the data analysis?
The convex cone approach is used as a supplementary DEA method to envelop inefficient units and calculate the specific performance gap, thereby providing concrete improvement targets for the hospitals analyzed.
How does the author handle the differences between English and German mortality data?
The author identifies that the datasets measure deaths differently and applies a mathematical translation approach—specifically multiplying negative output values by -1—to ensure the data is compatible for the DEA model.
- Citar trabajo
- Lucas Fandrey (Autor), 2015, Hospital efficiency analysis in England and Germany. What lessons can be learned from each other?, Múnich, GRIN Verlag, https://www.grin.com/document/317821
