Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Team Size and Composition in Home Healthcare: Quantitative Insights and Six Model-Based Principles

The aim of this constructive study was to develop model-based principles to provide guidance to managers and policy makers when making decisions about team size and composition in the context of home healthcare. Six model-based principles were developed based on extensive data analysis and in close interaction with practice. In particular, the principles involve insights in capacity planning, travel time, available effective capacity, contract types, and team manageability. The principles are formalized in terms of elementary mathematical models that capture the essence of decision-making. Numerical results based on real-life scenarios reveal that efficiency improves with team size, albeit more prominently for smaller teams due to diminishing returns. Moreover, it is demonstrated that the complexity of managing and coordinating a team becomes increasingly more difficult as team size grows. An estimate for travel time is provided given the size and territory of a team, as well as an upper bound for the fraction of full-time contracts, if split shifts are to be avoided. Overall, it can be concluded that an ideally sized team should serve (at least) around a few hundreds care hours per week.

Healthcare Service Efficiency: An Empirical Study on Healthcare Capacity in Various Counties and Cities in Taiwan

As human lifespan increases and the need for elderly care grows, the demand for healthcare services and its associated costs have surged, causing a decline in the operational efficiency of universal healthcare. This has created an imbalance in medical services across different regions, posing a long-standing challenge for the public. To address this issue, strategies to enhance the capacity, efficiency, and quality of healthcare services in various regions must be developed. The appropriate allocation of medical resources is a fundamental requirement for countries to establish a robust healthcare system. This empirical study utilized data envelopment analysis (DEA) to evaluate the efficiency of medical service capacity and identify potential improvement strategies for counties and cities in Taiwan during the period from 2015 to 2020. The results of this study show that (1) the annual average efficiency of medical service capacity in Taiwan is approximately 90%, indicating that there is still room for a 10% improvement; (2) among the six municipalities, only Taipei City has sufficient healthcare capacity, whereas the efficiency of the remaining municipalities needs improvement; and (3) most counties and cities demonstrated increasing returns to scale, indicating a need to scale up the capacity of medical services as appropriate. Based on the findings of this study, it is recommended that medical personnel be increased accordingly to balance the workload, a favorable working environment be provided to stabilize the medical workforce, and urban-rural medical disparities be balanced to improve service quality and reduce cross-regional health services. These recommendations are expected to provide a reference for society as a whole to promote and enhance public health policies, leading to a continual improvement of the quality of medical services.

Levels, trends, and determinants of effectiveness on the hierarchical medical system in China: Data envelopment analysis and bootstrapping truncated regression analysis

Background

The hierarchical medical system (HMS) refers to the classification of treatment according to disease priorities based on severity and difficulty to promote the fairness of medical services for residents, which is regarded as the key to the success of medical reform in China.

Methods

In the past decade of "New Medical Reform," the efficiency of HMS, including secondary and tertiary hospitals and primary healthcare centers (PHCs), was measured horizontally and vertically by employing the combination of an output-oriented superefficiency slack-based model-data envelopment analysis (SE-SBM-DEA) model with the Malmquist total factor productivity index (MTFP). In the second stage, the overall technical efficiency (OTE) scores were regressed against a set of environmental characteristics and several managerial factors through bootstrapping truncated regression.

Results

On average, the OTE score in tertiary hospitals was 0.93, which was higher than that in secondary hospitals and PHCs (0.9 and 0.92, respectively). In terms of trend, the OTE of tertiary hospitals declined at first and then increased. The opposite was true of secondary hospitals, in which the APC of the OTE was 10.82 and -3.11% in early and late 2012, respectively. The PHCs generally showed a fluctuating downward trend. In the aspects of productivity, all institutions showed a downturn by an annual average rate of 2.73, 0.51, and 2.70%, respectively. There was a significant negative relationship between the ratio of outpatients to inpatients and tertiary hospitals. Additionally, the medical technical personnel per 1,000 population negatively affected PHCs. In contrast, the GDP per capita had a significantly positive effect on tertiary hospitals, and the number of beds per 1,000 population positively influenced PHCs.

Conclusion

The efficiency of medical institutions at various levels in HMS was unbalanced and took the form of an "inverted pyramid." Multilateral factors influence the efficiency of HMS, and to address it, multi-intervention packages focusing on sinking high-quality medical resources and improving healthcare capacity, and guiding hierarchical medical practice should be adopted.

What Counts in Nursing Homes’ Quality and Efficiency? Results From Data Envelopment Analysis in Italy

Purpose: Economic resource constrains in public spending budget in a country, such as Italy, with an ageing population with high incidence of chronic diseases calls for better strategies to improve measuring quality and efficiency in nursing homes (NHs). This paper analyses the efficiency of 40 NHs based in Tuscany considering not only structural characteristics but also quality of care, including residents, relatives and staff satisfaction.

Methodology: We run a classic data envelopment analysis (DEA) on data gathered by the NHs’ regional performance evaluation system. We include as inputs the number of total work hours as labour and the daily cost for services as economic resources. As outputs we include measures for quality of care (number of falls, urinary infections and antidepressants), satisfaction (residents, relatives and professionals) and quality of life (days of recreational activities). We run a multivariate regression to analyse the determinants of previously obtained efficiency scores considering factors such as: institutional (ownership), managerial (training) and clinical (patient’s severity).

Findings: Results find 35% efficient NHs. Moreover, management and the managerial factor (staff trained in end-of-life support) are predictors of the efficiency score.

Originality: Our study uses satisfaction (residents, relatives and professionals) measures as proxy for quality output in the DEA model and measures related to staff management (eg training) as predictors of the efficiency scores.

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

With hospital budgets remaining tight and healthcare expenditure rising due to demographic change and advances in technology, hospitals continue to face calls to contain costs and allocate their resources more efficiently. In this context, efficiency has emerged as an increasingly important way for hospitals to withstand competitive pressures in the hospital market. Doing so, however, can be challenging given unpredictable fluctuations in demand, a prime example of which are emergencies, i.e. urgent medical cases. The link between medical urgency and hospitals' efficiency, however, has been neglected in the literature to date. This study therefore aims to investigate the relationship between hospitals' urgency characteristics and their efficiency. Our analyses are based on 4094 observations from 1428 hospitals throughout Germany for the years 2015, 2016, and 2017. We calculate an average urgency score for each hospital based on all cases treated in that hospital per year and also investigate the within-hospital dispersion of medical urgency. To analyze the association of these urgency measures with hospitals' efficiency we use a two-stage double bootstrap data envelopment analysis approach with truncated regression. We find a negative relationship between the urgency score and hospital efficiency. When testing for non-linear effects, the results reveal a u-shaped association, indicating that having either a high or low overall urgency score is beneficial in terms of efficiency. Finally, our results reveal that higher within-hospital urgency dispersion is negatively related to efficiency.

Measuring productivity of healthcare services under environmental constraints: evidence from China

BACKGROUND

Despite the growing literature on the efficiency and productivity of the Chinese healthcare system, less attention has been given to examining the undesirable outputs linked to healthcare services, including environmental pollution. Taking the atmospheric environmental pollution resulting from the incineration of medical waste as an undesirable output of the healthcare system, this study analyzed the growth and decomposition of Total Factor Productivity (TFP) of healthcare services across 31 Chinese provinces during the period 2005-2016.

METHODS

The Meta-frontier undesirable super-efficiency slack-based measure (SBM) model and the Global Malmquist-Luenberger (GML) Index were employed to analyze the growth and decomposition of TFP using the Max DEA software.

RESULTS

The results revealed that the years 2009 and 2015 marked significant changes in TFP of healthcare services in Chinese provinces. During the study period, the rate of technological change (TC) slowly declined, whereas the rate of efficiency change (EC) steadily increased. With the national average being the benchmark, the results indicated that: the TFP of 17 provinces and cities exceeded the average, the EC of 16 provinces and cities exceeded the average, the TC of 9 provinces and cities exceeded the average, and the value in the Technology Gap Ratio (TGR) changes of 13 provinces and cities were above the national average.

CONCLUSIONS

(1) The TFP of the healthcare services across China continued to decline slowly during the study period. (2) The effect of technical catch-up in the eastern, central, and western regions of China was significant across the three regions, whereas the effect of technical innovation was negative. (3) The TFP varied considerably among the Chinese provinces. These findings suggest that, under existing environmental constraints, relevant government departments should improve technical innovation in the supply of healthcare services and medical waste treatment, increase technical efficiency in the factor of healthcare production, strengthen regional health planning, and balance the development of regional healthcare.

A Systematic Literature Review of Efficiency Measurement in Nursing Homes

Background: As our population ages at an increasing rate, the demand for nursing homes is rising. The challenge will be for nursing homes to maintain efficiency with limited resources while not compromising quality. This study aimed to review the nursing home efficiency literature to survey the application of efficiency methods and the measurements of inputs, outputs, facility characteristics and operational environment, with a special focus on quality measurement. Methods: We systematically searched three databases for eligible studies published in English between January 1995 and December 2018, supplemented by an exhaustive search of reference lists of included studies. The studies included were available in full text, their units of analysis were nursing homes, and the analytical methods and efficiency scores were clearly reported. Results: We identified 39 studies meeting the inclusion criteria, of which 31 accounted for quality measures. Standard efficiency measurement techniques, data envelopment analysis and stochastic frontier method, and their specifications (orientation, returns to scale, functional forms and error term assumptions) were adequately applied. Measurements of inputs, outputs and control variables were relatively homogenous while quality measures varied. Notably, most studies did not include all three quality dimensions (structure, process and outcome). One study claimed to include quality of life; however, it was not a well-validated and widely used measure. The impacts of quality on efficiency estimates were mixed. The effect of quality on the ranking of nursing home efficiency was rarely reported. Conclusions: When measuring nursing home efficiency, it is crucial to adjust for quality of care and resident’s quality of life because the ultimate output of nursing homes is quality-adjusted days living in the facility. Quality measures should reflect their multidimensionality and not be limited to quality of throughput (health-related events). More reliable estimation of nursing home efficiencies will require better routine data collection within the facility, where well-validated quality measures become an essential part of the minimum data requirement. It is also recommended that different efficiency methods and assumptions, and alternative measures of inputs, outputs and quality, are used for sensitivity analyses to ensure the robustness and validity of findings.

Road map for progress and attractiveness of Iranian hospitals by integrating self-organizing map and context-dependent DEA

Hospitals play an important role in healthcare systems and usually stay on the end node of the healthcare chain. Thus, determining their road map to get close to the desired efficiency frontier and developing short-term and long-term plans could help to manage costs and resources, efficiently. As the efficiency frontier depends on the size of the hospital and the complexity of its structure, the homogeneity in benchmarking must be considered. For tackling this problem, the self-organizing map (SOM) is used to create homogeneous groups. On the other hand, data envelopment analysis (DEA) is a well-known methodology for evaluating decision-making units. Each unit obtains the efficiency score based on the ratio of weighted outputs to weighted inputs, where each unit can take the desirable weights for inputs and outputs to provide the maximum value. One of the problems of DEA is the selection of the reference set and distinguishing between the efficient hospitals. To overcome these problems, the context-depended DEA has been applied and the progress and attractiveness of hospitals are obtained. To evaluate the capability of the proposed approach, data of 288 Iranian hospitals are utilized. By applying SOM the hospitals are clustered into appropriate homogeneous groups and by applying context-dependent DEA, the road map for progress and attractiveness of each hospital is determined. In other words, using the proposed approach the hospitals are able to determine the short and long-term goals according to their strategic plans.

A facility location problem for sustainability-conscious power generation decision makers

The practical implementation of a performance measurement system is an inevitable necessity for all levels of management to direct business operations towards maximal efficiency. In particular, academics' and practitioners' expectations have resulted in notable initiatives that incorporate sustainability assessment in managerial decision-making. The sustainability paradigm investigates practices and policies that simultaneously account for economic, environmental, and social aspects. Drawing on the perspective of a sustainability-conscious decision maker, this paper centers on the evaluation of logistical locations with capacitated production levels through a two-phase assessment procedure. To design a supervisory framework to measure the sustainability performance of the supplying facilities under investigation, we develop a double bootstrap data envelopment analysis model with undesirable factors in the first phase. We, then, use the obtained bias-corrected efficiency estimates in the second phase to establish sustainability-based policies in the context of facility location problems. The proposed model is applied to an energy case study of fossil fuel power plants in the United States. We present empirical evidence corroborating a considerable difference between the results of the case where the decision maker explores the environmental and social impacts of the power plants and the conventional formulation by which only financial outcomes are considered. Our numerical findings reveal that the proposed approach substantially diminishes greenhouse gas emissions at the cost of slight increases in total expenses. In addition to such an improvement in the environmental efficiency, we provide evidence of a dramatic enhancement in the social performance.