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Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

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Improving the academic programming process in a school of nursing using computational modeling and simulation.

Thomas R Clancy¹, Connie White-Delaney, Margo Mako

¹University of Minnesota School of Nursing, Minneapolis, Minnesota, USA. rcclancy@mchsi.com

Studies in Health Technology and Informatics

|July 14, 2009

Summary

Computational modeling improved faculty productivity in nursing education by optimizing class schedules and policies. This approach demonstrated significant gains in faculty percent effort (PE) and overall performance.

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Area of Science:

Nursing Education
Operations Research
Academic Administration

Background:

Academic institutions face pressure to balance high-quality education and research with affordability.
Faculty productivity is a key metric for institutional efficiency and effectiveness.
Traditional methods for assessing and improving faculty productivity can be resource-intensive.

Purpose of the Study:

To investigate the impact of computational modeling and simulation on faculty productivity.
To identify specific strategies for enhancing faculty percent effort (PE) within a school of nursing.
To evaluate the effectiveness of proposed interventions on academic programming.

Main Methods:

Utilized discrete event simulation software (Arena) to build a model of the academic programming process.
Analyzed faculty productivity data from 2004 to 2007.
Simulated the effects of reducing class distribution variation, combining small classes, adding faculty, and standardizing PE policies.

Main Results:

The model predicted significant improvements in baseline faculty percent effort (PE).
Key interventions included reducing class scheduling variation and consolidating small enrollment classes.
Standardizing PE policies and increasing faculty numbers were also identified as impactful strategies.

Conclusions:

Computational modeling and simulation offer a viable method for enhancing faculty productivity in academic settings.
Optimizing academic programming through data-driven strategies can lead to measurable improvements in faculty performance.
The study demonstrated the potential for significant faculty productivity gains through targeted operational adjustments.