Market Capture of Inpatient Perioperative Services Using DEA

O’Neill, Liam; Dexter, Franklin

doi:10.1007/s10729-004-7535-1

Cited by 33 publications

(31 citation statements)

References 24 publications

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“…1 At multiple hospitals, annual caseloads of representative procedures predicted total specialty workloads (e.g., lung resection predicted all of general thoracic surgery), whether workloads were quantified by ASA RVG units, relative value units, OR hours, or charges. 11,12 The variable influencing tactical (once a year) decisions involving surgeons without changes in caseload over time is the variability among periods in the surgeon's workload or caseload. The coefficient of variation (standard deviation/ mean) was used as a measure of the variability of workload because it combines information about the signal (the mean) and the noise (the standard deviation).…”

Section: Analysis Of Cases Vs Workload For Individual Surgeonsmentioning

confidence: 99%

Monitoring changes in individual surgeon’s workloads using anesthesia data

Dexter

Masursky²,

Ledolter

et al. 2012

Can J Anesth/J Can Anesth

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Purpose We investigated whether changes in the number of cases performed by surgeons can be used as an appropriate surrogate for anesthesia departments' billed units. Methods We used both number of cases performed and the American Society of Anesthesiologists' Relative Value Guide TM (ASA RVG) units to assess all operating room anesthetics of an anesthesia group for two sets of 13 fourweek periods. The units correspond to Canadian basic units and time units. Results Although the number of ASA RVG units is an economically important variable that quantifies perioperative workload, the number of cases is a suitable surrogate for ASA RVG units when used to monitor individual surgeons. The pooled mean Pearson correlation coefficient between the two variables was r = 0.95, with 95% confidence interval 0.94 to 0.96. In addition, there were essentially none to very weak pairwise correlations among surgeons. Conclusions Informal hospital analyses of relative changes in a surgeon's caseload over one year using anesthesia workload data or anesthesia billing data will generally give equivalent results. The principal importance of our findings is that they can be used by anesthesiologists, specifically department heads, in their role as part of operating room committees. Such committees institute plans to revise the caseload of one or a few surgeons, and they then evaluate the results of those plans. The findings of this study are applicable to all anesthesia groups and may be especially valuable to the heads of anesthesiology departments who do not have the data to repeat our analyses. RésuméObjectif Nous avons cherche´a`savoir si les changements dans le nombre de patients ope´re´s par les chirurgiens pouvaient se refle´ter fide`lement dans les unite´s de facturation des de´partements d'anesthe´sie.

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Section: Analysis Of Cases Vs Workload For Individual Surgeonsmentioning

confidence: 99%

Monitoring changes in individual surgeon’s workloads using anesthesia data

Dexter

Masursky²,

Ledolter

et al. 2012

Can J Anesth/J Can Anesth

Self Cite

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“…12 The citation window is 2002-2009. 13 The data collections were made on Aug parametric stochastic frontier (SF) methods or nonparametric data envelopment analysis (DEA). Parametric estimation of a stochastic frontier needs a behavioural hypothesis of cost minimisation and confounds the effects of misspecification of functional form with inefficiency.…”

Section: Bias-corrected Cost Efficiency Analysismentioning

confidence: 99%

“…Hence, in efficiency and productivity analyses, costs for teaching and research activities are most often included in the hospital production function [1], even if the specified outputs only include patient care data. Previous research has estimated the overall impact of teaching and research on unit prices to be up to 25 per cent, which is visible in the unfavourable results for teaching and research hospitals in comparative efficiency and productivity analyses [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. To create comparable benchmarking results at the university hospital level, efficiency and productivity analysis based on a sample of university hospitals that includes specified outputs for teaching and research is desirable [17].…”

Section: Introductionmentioning

confidence: 99%

Cost efficiency of university hospitals in the Nordic countries: a cross-country analysis

et al. 2010

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This paper estimates cost efficiency scores using the bootstrap bias-corrected procedure, including variables for teaching and research, for the performance of university hospitals in the Nordic countries. Previous research has shown that hospital provision of research and education interferes with patient care routines and inflates the costs of health care services, turning university hospitals into outliers in comparative productivity and efficiency analyses. The organisation of patient care, medical education and clinical research as well as available data at the university hospital level are highly similar in the Nordic countries, creating a data set of comparable decision-making units suitable for a cross-country cost efficiency analysis. The results demonstrate significant differences in university hospital cost efficiency when variables for teaching and research are entered into the analysis, both between and within the Nordic countries. The results of a second-stage analysis show that the most important explanatory variables are geographical location of the hospital and the share of discharges with a high case weight. However, a substantial amount of the variation in cost efficiency at the university hospital level remains unexplained.

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“…In this problem, n is the number of DMUs; s is the number of outputs and m is the number of inputs; Y r0 is the amount of output r generated by unit 0 and X i0 is the amount of input i used by unit 0; j λ is the intensity variable for DMU j. O'Neill and Dexter [22]; and Langabeer and Ozcan [23]). Hollingsworth et al…”

Section: Data Envelopment Analysis Developed By Charnes Cooper and mentioning

confidence: 99%

Assessing the efficiency of mother-to-child HIV prevention in low- and middle-income countries using data envelopment analysis

Santos

Amado

Santos

2012

Health Care Manag Sci

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AIDS is one of the most significant health care problems worldwide. Due to the difficulty and costs involved in treating HIV, preventing infection is of paramount importance in controlling the AIDS epidemic. The main purpose of this paper is to explore the potential of using Data Envelopment Analysis (DEA) to establish international comparisons on the efficiency of implementation of HIV prevention programmes. To do this we use data from 52 low-and middleincome countries regarding the prevention of mother-to-child transmission of HIV. Our results indicate that there is a remarkable variation in the efficiency of prevention services across nations, suggesting that a better use of resources could lead to more and improved services, and ultimately, prevent the infection of thousands of children. These results also demonstrate the potential strategic role of DEA for the efficient and effective planning of scarce resources to fight the epidemic.

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Market Capture of Inpatient Perioperative Services Using DEA

Cited by 33 publications

References 24 publications

Monitoring changes in individual surgeon’s workloads using anesthesia data

Monitoring changes in individual surgeon’s workloads using anesthesia data

Cost efficiency of university hospitals in the Nordic countries: a cross-country analysis

Assessing the efficiency of mother-to-child HIV prevention in low- and middle-income countries using data envelopment analysis

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