Harvey Wolfe

Estimating Need and Demand for Prehospital Care

Models estimating demand and need for emergency transportation services are developed. These models can provide reliable estimates which can be used for planning purposes, by complementing and/or substituting for historical data. The model estimating demand requires only four independent variables: population in the area, employment in the area, and two indicators of socioeconomic status which can be obtained from census data. The model can be used to estimate demand according to 4 operational categories and 11 clinical categories. The parameters of the model are calibrated with 1979 data from 82 ambulance services covering over 200 minor civil divisions in Southwestern Pennsylvania. This model was tested with data from another 55 minor civil divisions, also in Southwestern Pennsylvania, and it provided good estimates to total demand. The model to estimate need evolves from the demand model. It enables planners to estimate unmet need occurring in the region. The effect of emergency transportation service (ETS) provider characteristics on demand was also investigated. Statistical tests show that, for purposes of forecasting demand, when the sociodemographic factors are taken into account, provider characteristics are not significant.

First term probation: models for identifying high risk students

EC 2000 has heightened awareness among engineering faculty about the importance of student retention, especially the retention of first-year students. Previous research found that students placed on academic probation after their first term have a high probability of leaving engineering prior to graduation. Using five years of data, we examine the influence of the students initial preparedness, attitude toward his/her chosen career, and self-assessed confidence in areas such as study habits and communication skills, on first term probation and retention. Logistic regression approaches were used to develop models that have enabled us to determine the factors that most influence first term probation and to better identify students who require early interventions if they are going to successfully complete the engineering curriculum.

The Role of Operations Research in Regional Health Planning

Operations-research workers have not met with much success in being accepted as integral members of regional-health-planning teams, owing in part to a lack of understanding by health planners of the skills the operations researcher has to offer and in part the analysts inability to demonstrate that he can close the gap between theoretical modeling and the implementation of his results. This paper explores the growth of regional health planning in the United States and highlights its important problem areas. The literature of operations-research applications to health planning is reviewed critically with respect to the feasibility of models and the appropriateness of assumptions. Specific problems with the types of studies currently in the literature are identified and recommendations are made for improved coordination between operations-research workers and health planners.

Improving student learning through the use of multisource assessment and feedback

The paper examines the use of multisource assessment and feedback processes in the classroom and the potential impact on student learning in engineering. Grounded in control and goal setting theories, this assessment process provides a means for students to take a proactive role in their learning. Research and practice issues are addressed.

Using technology to enhance outcome assessment in engineering education

This paper describes on-going research at several major universities on the design, development, and application of outcome assessment methodologies enhanced by information technologies. Several applications are described as well as advantages and disadvantages. Future research objectives are discussed.

An Interdisciplinary Certificate in Product Realization: meeting the challenges of industry and the engineering criteria

A steady stream of technological advances and business needs have significantly altered the landscape of engineering education and training in the US. Although development of fundamental analytical skills is still an essential component of instruction, the industrial marketplace demands that graduating engineers learn a diverse range of new and emerging technologies. A crucial area where the knowledge of emerging technology is essential is the design, development, and creation of new products. Traditional engineering curricula are not designed to increase student proficiency in state-of-the-art product creation techniques. Specifically, engineering education has not adequately addressed the essential areas of collaborative design, entrepreneurship, marketing, and state-of-the-art product creation techniques. The University of Pittsburgh has addressed this critical issue with the introduction of an Interdisciplinary Certificate in Product Realization. This paper describes the Certificate and how it focuses on meeting many of the engineering criteria.

A Heuristic Method For Obtaining An Integer Solution To A Facility Design Optimization Problem

AbstractA linear programming model is formulated for optimizing the design of a tire plant. The determination of process capacities, the amounts of the various types of equipment, and the time-phasing of their acquisition constitutes facility design in this study. The large-scale linear program is decomposed into several subproblems to induce computational feasibility. Application of a linear programming code to these subproblems may yield a noninteger, optimum solution. Available integer programming codes can handle only very small problems. A heuristic method that exploits the special structure of the model is presented in this paper. The methodology is applied to the design of a new tire plant.

Comparing with Relative Accuracy Two Independent Ordinal Samples

Abstract Misuse of statistical techniques for analyzing ordinal data means that accuracy of resultant inferences are suspect. In an effort to examine the consequences of this issue, we investigate the relative accuracy of three hypothesis testing methods — two-sample t test with equal variances, the rank-sum test and ordered logit estimation — when applied to ordinal samples of five categories. Our most conclusive results are the following: Where ordinal samples are small and not bimodal, one should compare them with traditional nonparametric methods (rank-sum). Where observations have been made from bimodal populations on ordinal scales, and where samples are not so small, one should take care to compare them with theoretically appropriate techniques (ordered logit). Finally we present the proposed approach as a basis for comparing independent ordinal samples from the ABC Customer Satisfaction Survey dataset.