Investigating the link between medical urgency and hospital efficiency - Insights from the German hospital market

Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals. We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

New Public Management and hospital efficiency: the case of Norwegian public hospital trusts

New Public Management-inspired reforms in the Norwegian hospital sector have introduced several features from the private sector into a predominantly public healthcare system. Since the late 1990s, several reforms have been carried out with the intention of improving the utilization of resources. There is, however, limited knowledge about the long-term, and sector-wide effects of these reforms. In this study, using a panel data set of all public hospital trusts spanning nine years, we provide an analysis of the efficiency of hospital trusts using data envelopment analysis (DEA), as well as a Malmquist productivity index. Thereafter we use the efficiency scores as the dependent variable in a second-stage panel data regression analysis. We show that during the period between 2011 and 2019, on average, efficiency has increased over time. Further, in the second-stage analysis, we show that New Public Management features related to incentivization are associated with the level of hospital efficiency. We find no association between degree of competition and efficiency.

Public Reporting Of Hospital Quality Measures Has Not Led To Overall Quality Improvement: Evidence From Germany

Hospital quality has been measured and made publicly available for decades in the US and for more than a decade in Germany, as part of an effort to help those countries achieve quality improvement. The German hospital market presents a unique opportunity to examine the relationship between public reporting and quality improvement in the absence of performance-linked payment incentives in a high-income country. We considered quality indicators from several important categories of health services provided in hospitals (hip, knee, obstetrics, neonatology, heart, neck artery surgery, pressure ulcers, and pneumonia), using structured hospital quality reports from the period 2012-19. Our findings support the idea that public reporting provides a quality benchmark and prevents the provision of very low quality health care services, suggesting that imposing financial punishment on low performers is not necessary and may hinder quality improvement and aggravate health disparities. Although hospitals' intrinsic motivation and market forces play roles in improving quality, they are not sufficient to maintain the quality of high-performing hospitals. Therefore, in addition to rewarding high-performing institutions, aligning quality incentives with the intrinsic professional values of clinical care may be useful in achieving quality improvement.

A mixed-integer slacks-based measure data envelopment analysis for efficiency measuring of German university hospitals

Standard Data Envelopment Analysis (DEA) models consider continuous-valued and known input and output statuses for measures. This paper proposes an extended Slacks-Based Measure (SBM) DEA model to accommodate flexible (a measure that can play the role of input and output) and integer measures simultaneously. A flexible measure's most appropriate role (designation) is determined by maximizing the technical efficiency of each unit. The main advantage of the proposed model is that all inputs, outputs, and flexible measures can be expressed in integer values without inflation of efficiency scores since they are directly calculated by modifying input and output inefficiencies. Furthermore, we illustrate and examine the application of the proposed models with 28 university hospitals in Germany. We investigate the differences and common properties of the proposed models with the literature to shed light on both teaching and general inefficiencies. Results of inefficiency decomposition indicate that "Third-party funding income" that university hospitals receive from the research-granting agencies dominates the other inefficiencies sources. The study of the efficiency scores is then followed up with a second-stage regression analysis based on efficiency scores and environmental factors. The result of the regression analysis confirms the conclusion derived from the inefficiency decomposition analysis.

Approximating the influence of external factors on the technical efficiency score of hospital care: evidence from the federal states of Germany

BACKGROUND

A good health care system and, especially, the provision of efficient hospital care are the goals of national and regional health policies. However, the scope of general hospital care in the 16 federal states in Germany varies considerably from region to region. The objectives of this paper are to evaluate the technical efficiencies of all general hospitals of the 16 federal states for the period from 2015 to 2020, to find out the relation between the exogenous factors and score of efficiency, and also the influence of the COVID-19 pandemic on the results of the technical efficiency of hospital care in the German states.

METHODS

A two-step approach was used. First, an input-oriented Data Envelopment Analysis model with constant returns to scale and variable returns to scale was applied for the 6-year period from 2015 to 2020. The calculation of technical efficiency according to the input-oriented DEA model contains the three components-total technical efficiency (TTE), pure technical efficiency (PTE) and scale efficiency (SE). In the second stage, the influence of exogenous variables on the previously determined technical efficiency was evaluated by applying the tobit regression analysis.

RESULTS

Although the level of average technical efficiency of about 90% is high, total technical efficiency deteriorated steadily from 2015 to 2020. Its lowest point at around 78%, was in the year 2020. The deterioration of the average technical efficiency is notably influenced by the lower results in the years 2019 and 2020. The decomposition of technical efficiency also revealed that the deterioration of overall average efficiency was influenced by both pure technical efficiency (PTE) and scale efficiency (SE). Based on the tobit regression analysis performed, it was possible to conclude that the change in the efficiency score can be explained by the influence of exogenous factors only from 6.4% for overall efficiency and from 7.1% for scale efficiency.

CONCLUSIONS

The results of the analysis of the overall technical efficiency reveal that the aggregated data of all general hospitals of all 16 federal states show a steadily worsening total technical efficiency every year since 2015. Although, especially, the deterioration of the year 2020 with the occurrence of COVID-19 pandemic, contributes to a deteriorated efficiency average, the deterioration of the efficiency values, based on the analysis performed, is also observable between the years 2016 and 2019. Considering the output generated, for inefficient units and the relevant policy authorities in the hospital sector, it can be recommended that the number of beds and in particular the number of physicians, should be reduced as inputs. Based on this study, it is also recommended that decisions to increase the efficiency of general hospitals should be made with consideration of exogenous factors such as the change in the number of general hospitals or the population density in the respective state, as these had explanatory value in connection with the increase in efficiency values. Due to the wide variation in the size of the federal states, the recommendation is more appropriate for federal states with low population density.

The Core of Healthcare Efficiency: A Comprehensive Bibliometric Review on Frontier Analysis of Hospitals

Parametric and non-parametric frontier applications are typical for measuring the efficiency and productivity of many healthcare units. Due to the current COVID-19 pandemic, hospital efficiency is the center of academic discussions and the most desired target for many public authorities under limited resources. Investigating the state of the art of such applications and methodologies in the healthcare sector, besides uncovering strategical managerial prospects, can expand the scientific knowledge on the fundamental differences among efficiency models, variables and applications, drag research attention to the most attractive and recurrent concepts, and broaden a discussion on the specific theoretical and empirical gaps still to be addressed in future research agendas. This work offers a systematic bibliometric review to explore this complex panorama. Hospital efficiency applications from 1996 to 2022 were investigated from the Web of Science base. We selected 65 from the 203 most prominent works based on the Core Publication methodology. We provide core and general classifications according to the clinical outcome, bibliographic coupling of concepts and keywords highlighting the most relevant perspectives and literature gaps, and a comprehensive discussion of the most attractive literature and insights for building a research agenda in the field.

Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.

Exploring characteristics of specialization as moderators of the link between specialization and patient experience of care

BACKGROUND

Hospitals are increasingly pursuing specialization as a strategy to operate efficiently while delivering high-quality care. To date, however, evidence is lacking on whether hospital specialization has a consistent effect on patients' experience of care or whether different specialization characteristics influence how specialization works.

PURPOSE

This study investigates whether specialization characteristics, that is, the within-specialty concentration and the within-specialty urgency score, moderate the link between hospital specialization and patient experience of care.

METHODOLOGY

We use patient-reported and administrative data from German hospitals between 2014 and 2017, with orthopedic and trauma care as the research setting. Our sample consists of 157,458 patient observations nested within 483 hospitals. We apply random-intercept multilevel modeling.

RESULTS

Our results indicate that the effect of specialization on patient experience of care (a) decreases as the within-specialty concentration increases and (b) increases as the within-specialty urgency score increases.

CONCLUSION

This study provides novel insights into the specialization characteristics that make hospital specialization in orthopedic and trauma care particularly effective at improving patient experiences.

PRACTICE IMPLICATIONS

Although specialization is gaining popularity as a strategy for pooling scarce resources and facilitating high-quality health care, hospital managers and policymakers should consider that certain characteristics of specialization can influence the way that specialization works and how effective it is in improving patient experiences. Within the scope of orthopedic and trauma care, our study suggests that a low concentration of diagnoses within a service area and a high average level of medical urgency make specialization particularly effective at improving patient experiences.

Exploring the nexus of equality and efficiency in healthcare

Purpose

This study aims to explore the nexus of equality and efficiency by considering public hospitals' development dynamics, capacity and technology indicators.

Design/methodology/approach

Data was collected from the Ministry of Health Public Hospital Almanacs from 2014 to 2017. The Gini index (GI) is used to estimate the inequality of distribution of hospital performance indicators. A bias-corrected efficiency analysis is calculated to obtain efficiency scores of public hospitals for the year 2017. A path analysis is then constructed to better identify patterns of causation among a set of development, equality and efficiency variables.

Findings

A redefined path model highlights that development dynamics, equality and efficiency are causally related and health technology (path coefficient = 0.57; t = 19.07; p < 0.01) and health services utilization (path coefficient = 0.24; t = 8; p < 0.01) effects public hospital efficiency. The final path model fit well (X2/df = 50.99/8 = 6; RMSEA = 0.089; NFI = 0.95; CFI = 0.96; GFI = 0.98; AGFI = 0.94). Study findings indicate high inequalities in distribution of health technologies (GI > 0.85), number of surgical operations (GI > 0.70) and number of inpatients (GI > 0.60) among public hospitals for the years 2014–2017.

Originality/value

Study results highlight that, hospital managers should prioritize equal distribution of health technology and health services utilization indicators to better orchestrate equity-efficiency trade-off in their operations.

Multilevel Zero-One Inflated Beta Regression Model for the Analysis of the Relationship between Exogenous Health Variables and Technical Efficiency in the Spanish National Health System Hospitals

Background: This article proposes a methodological innovation in health economics for the second stage analysis of technical efficiency in hospitals. It investigates the relationship between the installed capacity in regions and hospitals and their ownership structure. Methods: A multilevel zero-one inflated beta regression model is employed to model pure technical efficiency more adequately than other models frequently used in econometrics. Results: Compared to publicly managed hospitals, the mean efficiency index of hospitals with public-private partnership (PPP) formulas was 4.27-fold. This figure was 1.90-fold for private hospitals. Concerning the efficiency frontier, the odds ratio (OR) of PPP models vs. public hospitals was 42.06. The OR of private hospitals vs. public hospitals was 8.17. A one standard deviation increase in the percentage of beds in intensive care units increases the odds of being situated on the efficiency frontier by 50%. Conclusions: The proportion of hospital beds in intensive care units relates to a higher chance of being on the efficiency frontier. Hospital ownership structure is related to the mean efficiency index of Spanish National Health Service hospitals, as well as the odds of being situated on the efficiency frontier.