Simulating the large population effects of quality management techniques in a health care clinical process.

详细信息   

• 作者：Johnson ; Susan Paul.
• 学历：Doctor
• 年：1997
• 导师：McLaughlin, Curtis P.
• 毕业院校：The University of North Carolina
• 专业：Business Administration, Management.;Engineering, Industrial.;Health Sciences, Health Care Management.;Operations Research.
• ISBN：9780591696813
• CBH：9818352
• Country：USA
• 语种：English
• FileSize：9158011
• Pages：263

文摘

The central area of interest in this research is developing methods by which we can assess the effects of quality management techniques in service operations management. Frequently, service operations management research is undertaken by adapting methods from production operations management efforts. While fruitful, we feel that this research is limited in applicability to service industries. For this reason, we choose to pursue exploratory methodologies uniquely tailored to the service industries.;This exploratory research involves simulation of a service process and investigates modeling specific quality management techniques commonly used in this process. We look at the health care industry and model the process of premature labor and delivery as a context for our investigation. Using Maryland discharge data a model of premature labor and delivery is developed and modified using Monte Carlo simulation techniques to include medical interventions and quality management techniques. Health outcomes, resource utilization and costs are measured.;The success of this methodology is in its many unique attributes. It enables researchers to explore the impact of the use of quality management techniques on a large population rather than looking at specific applications. Additionally, using real data in conjunction with well-documented effects of medical interventions and expected results from quality management is particularly helpful in linking processes with outcomes. The random selection/random replacement technique adopted from bootstrapping methods allows the models to embrace the inherent variation of the population and measure the results rather than controlling them. Finally, it provides a methodological alternative to randomized controlled trials to the health policy community.;The value of this research is not only in the development of a new research methodology but also in the managerial insights it provides. Simulation modeling can be used both to determine and implement quality improvement goals. It also shows that the goals set can be progressively more difficult to reach and that there are diminishing returns to quality improvement efforts. For the health policy community, the apparent lower bound to the desired cesarean section rate and the evidence of a relationship between respiratory distress syndrome (RDS) and cesarean section are both significant results from this study.