Efficiency of Thai provincial public hospitals during the introduction of universal health coverage using capitation

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.

Improving the technical efficiency of public health centers in Cambodia: a two-stage data envelopment analysis

BACKGROUND

Cambodia is undergoing a series of reforms with the objective of reaching universal health coverage. Information on the causes of inefficiencies in health facilities could pave the way for a better utilization of limited resources available to ensure the best possible health care for the population.

OBJECTIVES

The purpose of this study is to evaluate the technical efficiency of health centers and the determinants for inefficiencies.

METHODS

This cross-sectional study used secondary data from a costing study on 43 health centers in six Cambodian provinces (2016-2017). Firstly, the Data Envelopment Analysis method with output-orientation was applied to calculate efficiency scores by selecting multiple input and output variables. Secondly, a tobit regression was performed to analyze potential explanatory variables that could influence the inefficiency of health centers.

RESULTS

Study findings showed that 18 (43%) health centers were operating inefficiently with reference to the variable returns to scale efficiency frontier and had a mean pure technical efficiency score of 0.87. Overall, 22 (51%) revealed deficits in producing outputs at an optimal scale size. Distance to the next referral hospital, size and quality performance of the health centers were significantly correlated with health center inefficiencies.

CONCLUSION

Differences in efficiency exist among health centers in Cambodia. Inefficient health centers can improve their technical efficiency by increasing the utilization and quality of health services, even if it involves higher costs. Technical efficiency should be continuously monitored to observe changes in health center performance over time.

Effectiveness of hospital payment reforms in low- and middle-income countries: a systematic review

Payment mechanisms have attracted substantial research interest because of their consequent effect on care outcomes, including treatment costs, admission and readmission rates and patient satisfaction. Those mechanisms create the incentive environment within which health workers operate and can influence provider behaviour in ways that can facilitate achievement of national health policy goals. This systematic review aims to understand the effects of changes in hospital payment mechanisms introduced in low- and middle-income countries (LMICs) on hospital- and patient-level outcomes. A standardised search of seven databases and a manual search of the grey literature and reference lists of existing reviews were performed to identify relevant articles published between January 2000 and July 2019. We included original studies focused on hospital payment reforms and their effect on hospital and patient outcomes in LMICs. Narrative descriptions or studies focusing only on provider payments or primary care settings were excluded. The authors used the Risk of Bias in Non-Randomized Studies of Interventions tool to assess the risk of bias and quality. Results were synthesized in a narrative description due to methodological heterogeneity. A total of 24 articles from seven middle-income countries were included, the majority of which are from Asia. In most cases, hospital payment reforms included shifts from passive (fee-for-service) to active payment models-the most common being diagnosis-related group payments, capitation and global budget. In general, hospital payment reforms were associated with decreases in hospital expenditures, out-of-pocket payments, length of hospital stay and readmission rates. The majority of the articles scored low on quality due to weak study design. A shift from passive to active hospital payment methods in LMICs has been associated with lower hospital and patient costs as well as increased efficiency without any apparent compromise on quality. However, there is an important need for high-quality studies in this area.

Evaluation of resource utilization efficiency in obstetrics and gynecology units in China: A three-stage data envelopment analysis of the Shanxi province

In China, health care resources for expectant mothers and children are still not utilized to full efficiency, with health requirements still not being met. The purpose of this study is to critically examine the efficiency of gynecology and obstetrics hospital (OB/GYN) units in Shanxi province of China, with the overarching objective of exploring methods for improving their efficiency. We employ the three-stage data envelopment analysis (DEA) model to measure the efficiency of 134 OB/GYN units in Shanxi. The results show that the technical efficiency and scale efficiency scores of the sample units were low (0.48 and 0.54, respectively). The efficiency of the OB/GYN units varies by region, city, and county and by type of unit. We conclude that the main reason for the low efficiency of OB/GYN units in Shanxi province lies in the unreasonable scale. The government should, therefore, allocate health resources more reasonably, improving the efficiency of different regions, cities, and counties, as well as different types of OB/GYN units.

Integrated Analysis of Healthcare Efficiency: A Systematic Review

Data Envelopment Analysis (DEA) has been used as a performance measurement tool in efficiency assessment of healthcare systems. However, over the years, researchers and health practitioners presented the theoretical and methodological limitations of DEA that limits the full view of healthcare efficiency. To address these limitations, a commonly used strategy is to integrate other statistical methods and techniques with DEA to provide better efficiency evaluation. This paper reviews 57 studies with DEA applications in the healthcare industry to illustrate the integrated analysis of healthcare efficiency. With DEA as the central method, regression models in conjunction with statistical tests are commonly used. Input-oriented radial DEA models using predominantly capacity-related inputs and activity-related outputs and following either constant return to scale or variable return to scale assumptions are mostly applied to measure healthcare efficiency.

Balancing the health workforce: breaking down overall technical change into factor technical change for labour-an empirical application to the Dutch hospital industry

BACKGROUND

Well-trained, well-distributed and productive health workers are crucial for access to high-quality, cost-effective healthcare. Because neither a shortage nor a surplus of health workers is wanted, policymakers use workforce planning models to get information on future labour markets and adjust policies accordingly. A neglected topic of workforce planning models is productivity growth, which has an effect on future demand for labour. However, calculating productivity growth for specific types of input is not as straightforward as it seems. This study shows how to calculate factor technical change (FTC) for specific types of input.

METHODS

The paper first theoretically derives FTCs from technical change in a consistent manner. FTC differs from a ratio of output and input, in that it deals with the multi-input, multi-output character of the production process in the health sector. Furthermore, it takes into account substitution effects between different inputs. An application of the calculation of FTCs is given for the Dutch hospital industry for the period 2003-2011. A translog cost function is estimated and used to calculate technical change and FTC for individual inputs, especially specific labour inputs.

RESULTS

The results show that technical change increased by 2.8% per year in Dutch hospitals during 2003-2011. FTC differs amongst the various inputs. The FTC of nursing personnel increased by 3.2% per year, implying that fewer nurses were needed to let demand meet supply on the labour market. Sensitivity analyses show consistent results for the FTC of nurses.

CONCLUSIONS

Productivity growth, especially of individual outputs, is a neglected topic in workforce planning models. FTC is a productivity measure that is consistent with technical change and accounts for substitution effects. An application to the Dutch hospital industry shows that the FTC of nursing personnel outpaced technical change during 2003-2011. The optimal input mix changed, resulting in fewer nurses being needed to let demand meet supply on the labour market. Policymakers should consider using more detailed and specific data on the nature of technical change when forecasting the future demand for health workers.

Managing for efficiency in health care: the case of Greek public hospitals

This paper evaluates the efficiency of public hospitals with two alternative conceptual models. One model targets resource usage directly to assess production efficiency, while the other model incorporates financial results to assess economic efficiency. Performance analysis of these models was conducted in two stages. In stage one, we utilized data envelopment analysis to obtain the efficiency score of each hospital, while in stage two we took into account the influence of the operational environment on efficiency by regressing those scores on explanatory variables that concern the performance of hospital services. We applied these methods to evaluate 96 general hospitals in the Greek national health system. The results indicate that, although the average efficiency scores in both models have remained relatively stable compared to past assessments, internal changes in hospital performances do exist. This study provides a clear framework for policy implications to increase the overall efficiency of general hospitals.

How efficient are Greek hospitals? A case study using a double bootstrap DEA approach

The purpose of this study was to measure Greek hospital performance using different input-output combinations, and to identify the factors that influence their efficiency thus providing policy makers with valuable input for the decision-making process. Using a unique dataset, we estimated the productive efficiency of each hospital through a bootstrapped data envelopment analysis (DEA) approach. In a second stage, we explored, using a bootstrapped truncated regression, the impact of environmental factors on hospitals' technical and scale efficiency. Our results reveal that over 80% of the examined hospitals appear to have a technical efficiency lower than 0.8, while the majority appear to be scale efficient. Moreover, efficiency performance differed with inclusion of medical examinations as an additional variable. On the other hand, bed occupancy ratio appeared to affect both technical and scale efficiency in a rather interesting way, while the adoption of advanced medical equipment and the type of hospital improves scale and technical efficiency, correspondingly. The findings of this study on Greek hospitals' performance are not encouraging. Furthermore, our results raise questions regarding the number of hospitals that should operate, and which type of hospital is more efficient. Finally, the results indicate the role of medical equipment in performance, confirming its misallocation in healthcare expenditure.

Data Envelopment Analysis in the Presence of Measurement Error: Case Study from the National Database of Nursing Quality Indicators® (NDNQI®)

Data Envelopment Analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency (Hollingsworth, 2008), but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized (Gajewski, Lee, Bott, Piamjariyakul and Taunton, 2009; Ruggiero, 2004). We propose to address measurement error systematically using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data from the National Database of Nursing Quality Indicators® (NDNQI®) to estimate nursing units' efficiency. Several external reliability studies inform the posterior distribution of the measurement error on the DEA variables. We will discuss the case of generalizing the approach to situations where an external reliability study is not feasible.

Governance and performance: the performance of Dutch hospitals explained by governance characteristics

This paper describes the efficiency of Dutch hospitals using the Data Envelopment Analysis (DEA) method with bootstrapping. In particular, the analysis focuses on accounting for cost inefficiency measures on the part of hospital corporate governance. We use bootstrap techniques, as introduced by Simar and Wilson (J. Econom. 136(1):31–64, 2007), in order to obtain more efficient estimates of the effects of governance on the efficiency. The results show that part of the cost efficiency can be explained with governance. In particular we find that a higher remuneration of the board as well as a higher remuneration of the supervisory board does not implicate better performance.

Profile of hospital charges for chronic conditions by health status and severity level: a case study of 4 provinces in Thailand

The objective of this study is to identify charges for common chronic patients, by health status and severity of illness. Patients having 4 common chronic diseases-diabetics, hypertension, chronic lower respiratory diseases, and chronic renal failure-from 4 provinces were included (between 2002 and 2004). Patients were classified into clinically defined and health plan categories; charges were analyzed according to core health status and severity level of the chronic disease groups. Patients classified as single chronic condition (69.8%) had mean annual charges between 4089 and 7461 baht. Patients with multiple chronic conditions (30.2%) had mean annual charges varying, by health status and severity, from 611 to 16 794 baht, accounting for 40% of the total charges. Distribution of charges varied across health status groups. 1 USD = 35.1 baht The percentages of chronic health expenditures vary according to health status and severity of illness. This analysis can be used to identify patients for various purposes.