Part 1: Simulation for Performance Improvement
As you have examined this week, simulation as an analytic tool can assist healthcare administration leaders execute important improvement initiatives. Simulations can be used to determine the impact of hospital outbreaks, shortages in staff, potential disaster events, or even financial challenges that might impact healthcare delivery for a health services organization. As a current or future healthcare administration leader, the ability to use simulation as an analytic technique will help you execute sound decision making to tackle healthcare administration challenges.
For this Discussion, review the resources , and consider those issues that might most affect healthcare administration practice. Consider how those issues might be addressed through the process of using simulation as an analytic technique, and reflect on how you might apply the process of simulation to address these issues.
Explain in 2 or 3 paragraphs of how simulation might be used to improve performance in your health services organization or one with which you are familiar. Be specific, and provide examples.
Part 2: Assignment: Simulation in Health Care
Sometimes, challenges or issues in healthcare delivery are not prescriptive in nature and cannot be solved using pure optimization. Simulation provides healthcare administration leaders and decision makers a method to evaluate problems using probabilistic components. For example, a primary care queueing problem might logically follow a Poisson process and be modeled using simulation techniques.
For this Assignment, review the resources, and examine the different simulation techniques highlighted. Reflect on how these simulation techniques might apply to specific challenges or issues for healthcare delivery. Then, complete the assigned problems for the Assignment.
The Assignment: (3–5 pages)
- Complete Problem 40 on page 876 of your course text.
Note: You will be using Excel and @Risk for this Assignment.
Albright, S. C., & Winston, W. L. (2015). Business analytics: Data analysis and decision making (5th ed.). Stamford, CT: Cengage Learning.
- Chapter 15, “Introduction to Simulation Modeling” (pp. 812–879)X
Palisade [PalisadeCorp]. (2014a, January 29). Introduction to Monte Carlo simulation and risk analysis using @RISK and RISKOptimzer [Video file]. Retrieved from https://www.youtube.com/watch?v=KgAHLRxjBsA&feature=youtu.be
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