TURKEY HEALTH SYSTEM

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Overview of Health efficiency methods PPT

Overview of research on health care efficiency Paul G. Barnett, PhD February 23, 2011 Presentation has some valuable info about SFA, DEA differences, limitations and two other health care efficiency measurement methods. Population and Episode Groupers and Small Area Variation Analysis. Two academic and two commercial way of efficiency in health. LINK

Scope of this talk

  1. Definition of health care efficiency
  2. Efficiency concepts
  3. Methods of measuring efficiency
  4. Ways to achieve health care efficiency
  5. Ethics and new applications

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Efficiency software

Estimation of technical efficiency: a review of some of the stochastic frontier and DEA software.

http://www.economicsnetwork.ac.uk/cheer/ch15_1/dea.htm

SAV(Small Area Variation)

Fam Med. 1995 Apr;27(4):272-6.
Small area variation analysis: a tool for primary care research.

Small area variation analysis is a research tool used by health services researchers to describe how rates of health care use and events vary over well-defined geographic areas. Significant variation has been shown to exist in the rates of hospitalization for chronic obstructive lung disease, pneumonia, hypertension, and in surgical procedures, such as hysterectomy, cholecystectomy, and tonsillectomy. Potential sources of variation include differences in underlying morbidity, access to care, physician judgment, quality of care delivered, patient demand for services, and random variation. Small area variation studies have been used to determine if significant variation exists across geographic areas and to describe relationships between the observed variation and potential causal factors. Methodologic concerns include the definition of small areas, defining the at-risk population within each small area, sample size, case mix adjustments, and stability of rates over time. The use of small area analysis in primary care will require definition of appropriate small areas for ambulatory care, description of the variation in ambulatory events across small areas, development of appropriate measures for ambulatory case mix, and development of appropriate tools to measure the outcomes of ambulatory care.

Technical efficiency techs and misspesification in stochastic

Essentially there are two main methodologies for measuring technical efficiency: the econometric (or parametric) approach, and the mathematical (or non-parametric) approach. The two techniques use different methods to envelop data, and in doing so they make different accommodation for random noise and for flexibility in the structure of production technology. Hence they differ in many ways, but the advantages of one approach over the other boil down to two characteristics:

• the econometric approach is stochastic and attempts to distinguish between the effects of noise and the effects of inefficiency, while the linear programming approach is deterministic and under the voice inefficiency melt noise and real inefficiency;

• the econometric approach is parametric and as a result suffers from functional form misspecification, while the programming approach is non-parametric and so it is immune to any form of functional misspecification.

Specification (regression)

In regression analysis and related fields such as econometrics, specification is the process of converting a theory into a regression model. This process consists of selecting an appropriate functional form for the model and choosing which variables to include. Model specification is one of the first steps in regression analysis. If an estimated model is misspecified, it will be biased and inconsistent.[1]

Specification error and bias

Specification error occurs when an independent variable is correlated with the error term. There are several different causes of specification error:

  • incorrect functional form
  • a variable omitted from the model may have a relationship with both the dependent variable and one or more of the independent variables (omitted-variable bias);[2]
  • an irrelevant variable may be included in the model
  • the dependent variable may be part of a system of simultaneous equations (simultaneity bias)

measurement errors may affect the independent variables

Survey on technical efficiency

http://www2.warwick.ac.uk/fac/soc/economics/staff/phd_students/porcelli/porcelli_dea_sfm.pdf

That survey is about technical efficiency contains a brief for measurement of efficiency, DEA and stockhastic frontier. (See: Figure 1 for performance context)

Reasons for measuring efficiency

http://pages.stern.nyu.edu/~wgreene/FrontierModeling/SurveyPapers/Lovell-Fried-Schmidt.pdf pages 10-11

Why the interest in measuring efficiency and productivity? We can think of three reasons. First, only by measuring efficiency and productivity, and by separating their effects from those of the operating environment so as to level the playing field, can we explore hypotheses concerning the sources of efficiency or productivity differentials. Identification and separation of controllable and uncontrollable sources of performance variation is essential to the institution of private practices and public policies designed to improve performance. Zeitsch et al. (1994) provide an empirical application showing how important it is to disentangle variation in the operating environment (in this case customer density) from variation in controllable sources of productivity growth in Australian electricity distribution. Read more of this post