In recent years, performance measurement has become the focus of attention in a variety of public sector fields. Unfortunately, too little has been done to develop valid operational definitions of performance, or to identify the weaknesses and biases inherent m certain types of performance measures. Thus, the potential exists for the inappropriate use of certain indicators in performance evaluations and decisions. One field in which there has been increasing effort to deal with performance problems is that of transit. Regardless, the nebulous nature of "performance" has been all too apparent in this industry. The terms "productivity," "efficiency," and "effectiveness" have been used synonymously in some instances, while in other cases "efficiency" and "effectiveness" have been considered to be different aspects of overall "productivity." This confusion is of major significance, because the use of performance measures in operations assessment, decision making, and resource allocation is increasing. Furthermore, since it increasingly is being urged that subsidy payments be linked to the performance of a transit system, and since subsidies now constitute over half of transit revenues, the performance measurement problem is particularly important. This paper examines weaknesses and biases inherent in commonly used measures of urban mass transit performance. It is shown that measures of efficiency, such as cost per passenger, are being incorrectly used as measures of effectiveness and that various traditional measures of efficiency, such as those which incorporate mileage, can be misleading when applied in decision making. Suggestions are made for developing valid performance indicators and for developing safeguards that will avoid present shortcomings.transportation: mass transit, productivity, philosophy of modeling, cost/benefit analysis
Objectives: The structure, process, and outcomes associated with planning, developing, and offering an interprofessional course on the foundations of patient safety is described, including how organizational, structural, cultural, and attitudinal barriers were overcome. Methods: Seventeen faculty members from 7 colleges and schools and medical center participatedVfrom the fields of decision sciences and systems, dentistry, medicine, law, nursing, occupational therapy, pharmacy, physical therapy, social work, health care administration, and outcomes management in health systems. Student assessment included theme analysis of open-ended questions, descriptive analysis of multipleY response option questionnaires, and criterionbased assessment of student performance on case studies. Triangulation of student comments, final course evaluation, and student performance evaluations were performed to learn overarching themes of student experience with the course. Results: The students learned a different way of thinking, found the instructional design and active learning methods useful to learning, and felt prepared to solve problems in the future. Students believed that the content was an essential core knowledge for all health professionals (87%) and should be required for all health professions students (78%). Students achieved an application level of learning (77%) within the cognitive domain and the valuing level within the affective domain. Students agree (96%) that they can define and apply the basic principles and tenets of patient safety, including identification of tools needed to work effectively within the health system and to improve safety and strongly agree (100%) that they value patient safety as a professional practice framework. Conclusion: The universitywide implementation case may offer important lessons to others nationally in health care education.
In Data Envelopment Analysis (DEA), the two-stage method is a popular procedure for accounting for exogenous influences on efficiency. With the conventional two-stage method, a DEA is first conducted using only traditional (endogenous) inputs and outputs. Then, the first-stage DEA scores are regressed on the environmental/contextual (exogenous) inputs of interest. The regression outcomes are used to identify exogenous inputs that influence the first-stage DEA scores to a statistically significant degree, and to adjust DEA scores to account for these influences. Herein, it is demonstrated empirically that the conventional method exhibits substantial bias and low precision, with the degree of bias and precision affected by input variance and correlation. A reverse two-stage procedure that yields estimates without the bias and precision problems that compromise the validity of the conventional method's estimates is suggested.
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