Non-parametric and parametric applications measuring efficiency in health care

Performance evaluation of ambulatory surgery centres: an efficiency approach

This paper evaluates the performance of 198 ambulatory surgery centres (ASCs) operating in the State of Pennsylvania during the fiscal year 2006. Performance is assessed from technical efficiency view using data envelopment analysis (DEA). Multi-input/output model included two inputs: number of operating rooms and labour, and patient surgical visits differentiated by age groups: 0–17, 18–64, 65+ as three outputs. Input oriented models were employed to assess various DEA efficiency models. Results show that about 48 (24%) of ASCs are efficient with a mean efficiency score of 0.60. The results also indicate that appropriate utilization of operating rooms and labour inputs are the main determinants of ASC efficiency.

A conceptual framework for selecting the most appropriate variables for measuring hospital efficiency with a focus on Iranian public hospitals

Over the past few decades, there has been an increasing interest in the measurement of hospital efficiency in developing countries and in Iran. While the choice of measurement methods in hospital efficiency assessment has been widely argued in the literature, few authors have offered a framework to specify variables that reflect different hospital functions, the quality of the process of care and the effectiveness of hospital services. However, without the knowledge of hospital objectives and all relevant functions, efficiency studies run the risk of making biased comparisons, particularly against hospitals that provide higher quality services requiring the use of more resources. Undertaking an in-depth investigation regarding the multi-product nature of hospitals, various hospital functions and the values of various stakeholders (patient, staff and community) with a focus on the Iranian public hospitals, this study has proposed a conceptual framework to select the most appropriate variables for measuring hospital efficiency using frontier-based techniques. This paper contributes to hospital efficiency studies by proposing a conceptual framework and incorporating a broader set of variables in Iran. This can enhance the validity of hospital efficiency studies using frontier-based methods in developing countries.

Do hospitals practice cream skimming?

‘Cream skimming’ refers to choosing patients for some characteristic(s) other than their need for care, which enhances the profitability or reputation of the provider. Under capitation or other fixed payment schemes, this often means choosing less ill patients. We present a new methodology to measure cream skimming by hospitals. Our approach also provides a measure of a hospital's gain in productive efficiency by caring for patients with lower illness severity. Using a panel of Washington state hospitals, we find evidence that hospitals do practice cream skimming. However, we find little evidence to suggest that cream skimming varies by hospital size, profit status or time.

A systematic review of health care efficiency measures

OBJECTIVE

To review and characterize existing health care efficiency measures in order to facilitate a common understanding about the adequacy of these methods.

DATA SOURCES

Review of the MedLine and EconLit databases for articles published from 1990 to 2008, as well as search of the "gray" literature for additional measures developed by private organizations.

STUDY DESIGN

We performed a systematic review for existing efficiency measures. We classified the efficiency measures by perspective, outputs, inputs, methods used, and reporting of scientific soundness.

PRINCIPAL FINDINGS

We identified 265 measures in the peer-reviewed literature and eight measures in the gray literature, with little overlap between the two sets of measures. Almost all of the measures did not explicitly consider the quality of care. Thus, if quality varies substantially across groups, which is likely in some cases, the measures reflect only the costs of care, not efficiency. Evidence on the measures' scientific soundness was mostly lacking: evidence on reliability or validity was reported for six measures (2.3 percent) and sensitivity analyses were reported for 67 measures (25.3 percent).

CONCLUSIONS

Efficiency measures have been subjected to few rigorous evaluations of reliability and validity, and methods of accounting for quality of care in efficiency measurement are not well developed at this time. Use of these measures without greater understanding of these issues is likely to engender resistance from providers and could lead to unintended consequences.

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

We investigate the impact of implementing capitated-based Universal Health Coverage (UC) in Thailand on technical efficiency in larger public hospitals during the policy transition period. We measure efficiency before and during the transition period of UC using a two-stage analysis with Data Envelopment Analysis, bootstrap DEA, and truncated regressions. Our analysis indicates that during the transition period efficiency in larger public hospitals across the country increased. The findings differed by region, and hospitals in provinces with more wealth not only started with greater efficiency, but improved their relative position during the transitional phases of the UC system.

The measurement of efficiency and productivity of health care delivery

The measurement of efficiency and productivity of health service delivery has become a small industry. This is a review of 317 published papers on frontier efficiency measurement. The techniques used are mainly based on non-parametric data envelopment analysis, but there is increasing use of parametric techniques, such as stochastic frontier analysis. Applications to hospitals and other health care organizations and areas are reviewed and summarised, and some meta-type analysis undertaken. Cautious conclusions are that public provision may be potentially more efficient than private, in certain settings. The paper also considers conceptualizations of efficiency, and points to dangers and opportunities in generating such information. Finally, some criteria for assessing the use and usefulness of efficiency studies are established, with a view to helping both researchers and those assessing whether or not to act upon published results.

Hospital quality, efficiency, and input slack differentials

OBJECTIVE

To use an advance in data envelopment analysis (DEA) called congestion analysis to assess the trade-offs between quality and efficiency in U.S. hospitals.

STUDY SETTING

Urban U.S. hospitals in 34 states operating in 2004.

STUDY DESIGN AND DATA COLLECTION

Input and output data from 1,377 urban hospitals were taken from the American Hospital Association Annual Survey and the Medicare Cost Reports. Nurse-sensitive measures of quality came from the application of the Patient Safety Indicator (PSI) module of the Agency for Healthcare Research and Quality (AHRQ) Quality Indicator software to State Inpatient Databases (SID) provided by the Healthcare Cost and Utilization Project (HCUP).

DATA ANALYSIS

In the first step of the study, hospitals' relative output-based efficiency was determined in order to obtain a measure of congestion (i.e., the productivity loss due to the occurrence of patient safety events). The outputs were adjusted to account for this productivity loss, and a second DEA was performed to obtain input slack values. Differences in slack values between unadjusted and adjusted outputs were used to measure either relative inefficiency or a need for quality improvement.

PRINCIPAL FINDINGS

Overall, the hospitals in our sample could increase the total amount of outputs produced by an average of 26 percent by eliminating inefficiency. About 3 percent of this inefficiency can be attributed to congestion. Analysis of subsamples showed that teaching hospitals experienced no congestion loss. We found that quality of care could be improved by increasing the number of labor inputs in low-quality hospitals, whereas high-quality hospitals tended to have slack on personnel.

CONCLUSIONS

Results suggest that reallocation of resources could increase the relative quality among hospitals in our sample. Further, higher quality in some dimensions of care need not be achieved as a result of higher costs or through reduced access to health care.

Factors affecting productive efficiency in primary care clinics

This study examines factors affecting the productive efficiency of primary care clinics. The empirical analysis uses a single-stage stochastic frontier regression model, in which factors affecting productive efficiency are specified as part of the inefficiency error component and estimated simultaneously with the production function. The study population includes primary care clinics in the US Military Health System from 1999 through 2003; the analytical data set is an unbalanced panel of 442 observations. The study's main results were that primary care clinics not associated with medical centres had significantly higher levels of productive efficiency than those associated with medical centres and that having proportionately more civilian staff (and thus less turnover) had a positive impact on productive efficiency. Due to their nature, these findings would be expected to also be applicable to the production of primary care in other settings. A key implication of the results is that improvements in productive efficiency should be a top priority, given the possibility for providing more primary care visits without increases in cost.

Stochastic frontier analysis of hospital inefficiency: a review of empirical issues and an assessment of robustness

Twenty stochastic frontier analysis (SFA) studies of hospital inefficiency in the United States were analyzed. Results from best-practice methods were compared against previously used methods in hospital studies to ascertain the robustness of SFA in estimating cost inefficiency. To compare past studies and analyze new data, SFA methods were varied by (a) the assumptions of the structure of costs and distribution of the error term, (b) inclusion of quality and product descriptor measures, and (c) use of simultaneous and two-stage estimation techniques. SFA results were relatively insensitive to several model variations.

The effect of environmental factors on technical and scale efficiency of primary health care providers in Greece

BACKGROUND

The purpose of this study was to compare technical and scale efficiency of primary care centers from the two largest Greek providers, the National Health System (NHS) and the Social Security Foundation (IKA) and to determine if, and how, efficiency is affected by various exogenous factors such as catchment population and location.

METHODS

The sample comprised of 194 units (103 NHS and 91 IKA). Efficiency was measured with Data Envelopment Analysis (DEA) using three inputs, -medical staff, nursing/paramedical staff, administrative/other staff- and two outputs, which were the aggregated numbers of scheduled/emergency patient visits and imaging/laboratory diagnostic tests. Facilities were categorized as small, medium and large (<15,000, 15,000-30,000 and >30,000 respectively) to reflect catchment population and as urban/semi-urban or remote/island to reflect location. In a second stage analysis, technical and scale efficiency scores were regressed against facility type (NHS or IKA), size and location using multivariate Tobit regression.

RESULTS

Regarding technical efficiency, IKA performed better than the NHS (84.9% vs. 70.1%, Mann-Whitney P < 0.001), smaller units better than medium-sized and larger ones (84.2% vs. 72.4% vs. 74.3%, Kruskal-Wallis P < 0.01) and remote/island units better than urban centers (81.1% vs. 75.7%, Mann-Whitney P = 0.103). As for scale efficiency, IKA again outperformed the NHS (89.7% vs. 85.9%, Mann-Whitney P = 0.080), but results were reversed in respect to facility size and location. Specifically, larger units performed better (96.3% vs. 90.9% vs. 75.9%, Kruskal-Wallis P < 0.001), and urban units showed higher scale efficiency than remote ones (91.9% vs. 75.3%, Mann-Whitney P < 0.001). Interestingly 75% of facilities appeared to be functioning under increasing returns to scale. Within-group comparisons revealed significant efficiency differences between the two primary care providers. Tobit regression models showed that facility type, size and location were significant explanatory variables of technical and scale efficiency.

CONCLUSION

Variations appeared to exist in the productive performance of the NHS and IKA as the two main primary care providers in Greece. These variations reflect differences in primary care organization, economical incentives, financial constraints, sociodemographic and local peculiarities. In all technical efficiency comparisons, IKA facilities appeared to outperform NHS ones irrespective of facility size or location. In respect to scale efficiency, the results were to some extent inconclusive and observed differences were mostly insignificant, although again IKA appeared to perform better.

Accounting for quality in the measurement of hospital performance: evidence from Costa Rica

This paper provides insights into how Costa Rican public hospitals responded to the pressure for increased efficiency and quality introduced by the reforms carried out over the period 1997-2001. To that purpose we compute a generalized output distance function by means of non-parametric mathematical programming to construct a productivity index, which accounts for productivity changes while controlling for quality of care. Our results show an improvement in hospital performance mainly driven by quality increases. The adoption of management contracts seems to have contributed to such enhancement, more notably for small hospitals. Further, productivity growth is primarily due to technical and scale efficiency change rather than technological change. A number of policy implications are drawn from these results.

East-west: does it make a difference to hospital efficiencies in Ukraine?

Ukraine's history has given it a split personality (e.g. divergent cultural influences on economic and managerial behavior), as was observed in the recent political developments both prior to and following the December 2004 elections. Eastern regions were heavily influenced by Russo-Soviet rule, while western regions have more of a European outlook. This study, which is largely exploratory, compares recent trends in hospital efficiency in Ukraine to see if this split personality manifests itself in differential rates of improvement. Given the inflexibility of Soviet-style planned economies, it is hypothesized that western regions will show greater improvement in economic efficiency that can be attributed to higher levels of managerial and medical entrepreneurship. Data for this study comes from three oblasts (i.e. geopolitical regions), one in the west and two in the east, spanning from 1997 to 2001. Data envelopment analysis (DEA) was used to estimate technical efficiency for the hospitals. After correcting for bias, a second-stage Tobit regression was estimated. Results indicate that hospitals in the west improved efficiencies, while those in the east stayed constant. These western areas of the nation, being more amenable to western management and medical 'business' practice, may be quicker to pick up on new techniques to increase healthcare delivery efficiencies. This may stem from the more limited effects of a shorter history of incorporation into a Soviet-style planned and controlled economy in which individual decision-making and entrepreneurship was suppressed in favor of central decision-making by the state.

A 12-year Analysis of Malmquist Total Factor Productivity in Dialysis Facilities

This study examined total factor productivity of dialysis facilities in Greece over a 12-year period, using nationally representative panel data. Data Envelopment Analysis (DEA) was used to compute Malmquist productivity indices, which were decomposed into technical efficiency change and technological change. The sample consisted of 73 dialysis facilities operating throughout the entire study period (1993-2004), corresponding to 97.3% and 58.9% of all facilities in the first and last study years respectively. Production variables were nursing staff and dialysis machines (inputs) and dialysis sessions (output). The DEA model was input-oriented allowing for constant returns to scale (CRS). Technical efficiency change was decomposed into scale efficiency change and variable returns to scale (VRS) "pure" technical efficiency change. Mean overall efficiency, throughout the study years, ranged from 39.6 to 63.1% with an all-time average of 56.7%, and only 2-4% of the facilities were fully efficient in each study year. Productivity indices indicated year-by-year progress or regress up to 5%, but the efficiency and technological components differed, in some cases, by as much as 30%. Although interesting subperiod effects were observed, conclusions could not be generalized for the entire study period due to alternating trends. We suggest that preliminary insight to productivity in this sector has been obtained, but particular subperiods must be isolated and further investigated.

Health care in the CIS countries : the case of hospitals in Ukraine

The study analyses the technical efficiency of community hospitals in Ukraine during 1997-2001. Hospital cost amount to two-thirds of Ukrainian spending on health care. Data are available on the number of beds, physicians and nurses employed, surgical procedures performed, and admissions and patient days. We employ data envelopment analysis to calculate the efficiency of hospitals and to assess productivity changes over time. The scores calculated with an output-oriented model assuming constant returns to scale range from 150% to 110%. Average relative inefficiency of the hospitals is initially above 30% and later drops to 15% or below. The average productivity change is positive but below 1%; a Malmquist index decomposition reveals that negative technological progress is overcompensated by positive catching-up.

Analysis of uncompensated hospital care using a DEA model of output congestion

Uncompensated care can create financial difficulties for hospitals. The problem is likely to worsen as the number of individuals lacking health insurance continues to grow. The objective of this study is to measure how uncompensated care affects hospitals' ability to provide the services for which they do receive compensation. Applying output-based data envelopment analysis (DEA) under various assumptions on the disposability of outputs to a sample of Pennsylvania hospitals, we find that, on average, hospitals could have produced 7% more output if they had all operated on the best-practice frontier and that uncompensated care reduced the production of other hospital outputs by 2%. Thus, even if hospitals were to operate efficiently, they might still face financial distress as a result of providing uncompensated care. The findings in our study suggest that policy makers should continue looking at ways to increase funding to hospitals providing uncompensated care while not distorting economic incentives to reduce excessive costs.

Balancing efficiency of health services and equity of access in remote areas in Greece

Data envelopment analysis (DEA) was used to investigate the efficiency of a set of small-scaled Greek hospitals known as hospital-health centers (HHCs). These facilities naturally provide primary and secondary care but are also expected to function as health centers addressing mostly preventive medicine, hygiene and other public health issues. They are located in remote rural areas and serve the relatively small local populations. This study aimed to obtain insight on their productive efficiency in light of their particular role. The sample consisted of 17 from the 18 units existing in the Greek NHS. Variables chosen to characterize production were numbers of doctors, nurses and beds as inputs, and admissions, outpatient visits and preventive medical services as outputs. The DEA model was input oriented, allowed for constant returns to scale and units were ranked according to a benchmarking approach. Analyses were performed with and without the preventive medicine variable and the results demonstrated technical inefficiencies 26.77 and 25.13%, respectively. Location appeared to affect performance, with remote units, e.g. on small islands, more inefficient. This raises the question if correcting reduced efficiency compromises equity of service access for highly dependent populations. Moreover, we observed superior performance of units additionally offering preventive medical services. This generates another question as to the role these facilities should play in our currently changing health care system.

A note on health care productivity with uncertain data

The present paper suggests extending the FDH-method, known from the efficiency measurement literature, to cover interval production data. Interval data represent a direct way to handle data uncertainty and problems with data comparability which seem particularly prevalent concerning health care applications. We illustrate the method using a simple 1-input-2-output model on health care data from the OECD health statistics.

Estimating technical efficiency in the hospital sector with panel data: a comparison of parametric and non-parametric techniques

BACKGROUND

Policy makers are increasingly interested in developing performance indicators that measure hospital efficiency. These indicators may give the purchasers of health services an additional regulatory tool to contain health expenditure.

OBJECTIVE

Using panel data, this study compares different parametric (econometric) and non-parametric (linear programming) techniques for the measurement of a hospital's technical efficiency.

METHOD

This comparison was made using a sample of 17 Italian hospitals in the years 1996-9.

RESULTS

Highest correlations are found in the efficiency scores between the non-parametric data envelopment analysis under the constant returns to scale assumption (DEA-CRS) and several parametric models. Correlation reduces markedly when using more flexible non-parametric specifications such as data envelopment analysis under the variable returns to scale assumption (DEA-VRS) and the free disposal hull (FDH) model. Correlation also generally reduces when moving from one output to two-output specifications.

CONCLUSIONS

This analysis suggests that there is scope for developing performance indicators at hospital level using panel data, but it is important that extensive sensitivity analysis is carried out if purchasers wish to make use of these indicators in practice.

The Improving Efficiency Frontier of Religious Not-for-Profit Hospitals

By using data-envelopment analysis (DEA), this study evaluates the efficiency of religious not-for-profit hospitals. Hospital executives, healthcare policy makers, taxpayers, and other stakeholders benefit from studies that improve hospital efficiency. Results indicate that overall efficiency in religious hospitals improved from 72% in 1998 to 74% in 2001. What is more important is that the number of religious hospitals operating on the efficiency frontier increased from 40 in 1998 to 47 in 2001. This clearly documents that religious hospitals are becoming more efficient in the management of resources. From a policy perspective, this study highlights the economic importance of encouraging increased efficiency throughout the healthcare industry.