Technical efficiency of nursing homes: do five-star quality ratings matter?

Dulal, Rajendra

doi:10.1007/s10729-017-9392-8

Cited by 6 publications

(8 citation statements)

References 29 publications

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“…About 20% of all the studies found a mean efficiency score higher than 0.9. Studies with highest mean efficiency score were from the US [2,20], Switzerland [21,22,23], and Taiwan [24]. The prominent characteristics of the nursing homes with higher efficiency scores were: for-profit (operational incentive), high occupancy rate (operational factor), and competitive market (environmental factor).…”

Section: Resultsmentioning

confidence: 99%

“…The prominent characteristics of the nursing homes with higher efficiency scores were: for-profit (operational incentive), high occupancy rate (operational factor), and competitive market (environmental factor). Of all the studies, 18 included at least one of these characteristics, and 14 adjusted for quality [2,24,25,26,27,28,29,30,31,32,33,34,35,36]. These 14 studies each included a relatively large number of variables, ranging from 7 to 25 (mean = 15, SD = 5), and the majority of them used data envelopment analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The majority of studies incorporated quality measures as control variables (19/29) or as a measure of output (13/29). Similar quality measures were used as outputs in some studies—e.g., “prevalence of falls” [2,25,28,34]—but as control variables as in others [42]. One study used quality measures (% non-ambulatory, % not self-feeding) as inputs [54].…”

Section: Resultsmentioning

confidence: 99%

“…The measurement of inputs and outputs, as well as facility characteristics and environmental factors, are relatively homogenous across studies. Most studies used number of beds [25,27,44,48,49], number of full-time nurses [33,35,40,44,54], or labour price [21,23,38,43,55] as inputs; and used number of resident days [2,32,33,40,55], number of residents [24,25,45,46,47], or case-mix index [20,28,30,31,51] as outputs. The use of inputs in nursing home efficiency measurement is similar to the input measures in hospital literature; i.e., number of beds, labour inputs such as doctors or nurses, or input prices [9,58].…”

Section: Discussionmentioning

confidence: 99%

“…For the first time in history, the majority of the global population can expect to live over 60, contributing to a strong increase in demand for aged-care services and placements in nursing homes [1]. Direct challenges for nursing homes are a steady increase in operational costs and higher expectations of service quality [2,3]. To maintain business vitality, the only way to deal with these challenges is to improve productivity and efficiency through service and process innovations [3].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Literature Review of Efficiency Measurement in Nursing Homes

Tran

Nguyen

Gray

et al. 2019

IJERPH

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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.

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