Productivity Models in Non-hospital Settings
Productivity Models in Non-hospital Settings
Productivity Models in Non-hospital Settings
Question description
Part 1 of Productivity Models in Non-hospital Settings: Using the information starting on page 408 of your text book, describe how to improve productivity in non-hospital organizations e.g. American Heart Association, Lupus Foundation, Alzheimer’s Association etc. What are the differences in applying the model to the non-hospital setting? Are there any special challenges in the non-hospital setting? Respond to at least two of your classmates’ postings.
This Discussion asks you to use the information starting on page 408 of your text book, describe how to improve productivity in non-hospital organizations e.g. American Heart Association, Lupus Foundation, Alzheimer’s Association etc. Please use the following seven steps written on page 409 of your text book to discuss.
- Management orientation;
- Overview studies;
- Productivity reporting;
- Quality control;
- In-depth studies;
- Performance/reward systems, and;
- Monitoring, review, and change
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Don’t forget to answer the following two questions:
What are the differences in applying the model to the non-hospital setting (e.g. American Heart Association, Lupus Foundation, Alzheimer’s Association)?
Are there any special challenges (regarding applying this model) in the non-hospital setting (e.g. American Heart Association, Lupus Foundation, Alzheimer’s Association)?
Are there any special challenges in the non-hospital setting?
Part 2
What are the advantages and disadvantages of using a Cause and Effect Diagram and Pareto Analysis in terms of analyzing quality issues?
In the literature on hospital performance, increasing attention has been paid to the fact that hospital production activity could be
influenced by factors other than merely outputs and inputs. Thus, hospitals’ characteristics and elements inherent to the environment where units operate in–e.g., hospital complexity, teaching status, degree of specialization, etc.—may affect their costs and performance [1]. As performance measures can be regarded as success indicators [2], accounting for the effects of these hospital cost heterogeneity factors turns out to be crucial to avoid misleading conclusions that may affect policies designed to improve hospital productive processes.
Hospital performance has been commonly approached through productivity measurements. Indeed, the need to obtain productivity measurements in a multiple-input and multiple-output context, such as that of hospitals, has given rise to the concept of Total Factor Productivity (TFP) index. This index measures the performance of a unit by the quotient of an aggregated measure of the outputs produced relative to an aggregated measure of the inputs used. Moreover, the TFP index accounts for other factors that may influence productivity, such as the scale in which units operate, i.e., economies of scale—or investments in technology and organizational adjustments, i.e., technical change. Therefore, the TFP index provides more comprehensive information on both hospitals’ cost structure and performance than just efficiency measurements [3].
In addition to efficiency, frontier methods can provide productivity measurements. Thus, TFP indexes can also be derived by obtaining a frontier of reference using either non-parametric or parametric techniques [4]. Although approaches using parametric methods and measurements and decomposition of productivity have been developed in the literature on performance measurements [5–6], the majority of applications to hospital productivity have employed non-parametric methods to obtain the frontier, and thus have addressed the construction of a productivity index based on the Malmquist Index [7–10].
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Hospital cost heterogeneity has been extensively considered in frontier analyses on hospital performance. However, although there is little evidence of measurement bias caused in hospital efficiency indexes by the omission of these factors, we are not aware of any previous study measuring the bias in hospital productivity levels. The premise is that hospitals operating in relatively more adverse conditions will be located at a greater distance from the benchmark frontier, appearing to be more inefficient. In other words, not accounting for elements of heterogeneity in costs is likely to give rise to higher inefficiency values [11] and, in turn, to lower productivity values. In addition, heterogeneity factors could also affect the use of technology and scale of operations. Thus, these elements may also influence hospital productivity via their impact on its components of technical change and scale economies.
Considering all the above, the main objective of this research is to estimate -in the context of Spanish public general hospitals- the bias that the omission of hospital cost heterogeneity may cause in measurements of hospital productivity as well as in each of its components. The Spanish public health system is a national health system (SNHS) characterised by universal coverage and tax funding. In addition, the SNHS is decentralised in such a way that all 17 Spanish regions (Autonomous Communities) have the responsibility to manage, regulate and plan the provision of health care services for their respective populations, leaving basically the coordination tasks to the central government. This decentralisation took more than 20 years: from 1981, when Cataluña took over the management of its health care system, to 2002, when the health services were devolved to the last ten Autonomous Communities depending on the central government. Based on hospital data from the SNHS, a stochastic cost frontier is estimated following parametric techniques to derive measures of hospital cost efficiency and productivity. This constitutes an adequate methodology for the purposes of this study as: (1) it enables to test hypotheses on the existence of hospital cost heterogeneity and (2) it allows the separation of effects related to random shocks from inefficiency, and by extension, from productivity measures.
The remainder of the paper is organised as follows. Section 2 provides a review of how previous literature on hospital performance has accounted for the effects of hospital cost heterogeneity. In section 3, the methodology followed, as well as the description of the variables used, are outlined. Results of the analysis are reported in section 4, whereas these are discussed in section 5 that includes the concluding remarks.