On Using Statistical Process Control Charts to Analyze the Impact of Quality Improvement Interventions

Author(s):  
Karen Homa
2020 ◽  
Vol 16 (8) ◽  
pp. e807-e813 ◽  
Author(s):  
Collin L. Plourde ◽  
William T. Varnado ◽  
Barbara J. Gleaton ◽  
Devika G. Das

PURPOSE: Long wait times are a common occurrence for chemotherapy infusion patients and are a source of decreased patient satisfaction. Our facility sought to decrease outpatient infusion clinic wait times by 20% using the Model for Improvement, quality improvement tools, and Plan-Do-Study-Act cycles. METHODS: A multidisciplinary team was formed to address clinic wait times. Patient interviews, time studies, process mapping, brainstorming sessions, affinity diagrams, fishbone diagrams, and surveys were used to define the problem and to develop an intervention. Wait times from check-in until medication administration were analyzed using statistical process control charts. Our Plan-Do-Study-Act cycle led to the addition of a “fast-track” clinic title for patients not waiting for laboratory results on the day of treatment and changes in clinic communication. The fast-track clinic signaled for those patients to have priority for vital sign collection and earlier notification to pharmacy to begin preparing medications. RESULTS: Baseline wait times for patients not requiring laboratories on the day of treatment averaged 1 hour and 33 minutes. After intervention, using statistical process control charts, a shift was observed with a new average wait time of 1 hour and 12 minutes (a 23% decrease). Wait times for patients requiring laboratories on the day of treatment did not change significantly. CONCLUSION: Implementation of a fast-track clinic title and improving communication resulted in a significant reduction in wait times for patients not requiring laboratories on the day of treatment. Future efforts will focus on sustainment and improving wait times for all patients.


Author(s):  
Mifta Priyanto

This paper presents the application of Total Quality Management Method using Pareto diagrams and Statistical Process Control charts (SPC). These tools can be applied to both the manufacturing and construction sectors. A Pareto diagram can figure out some of the dominant problems of the projects, and SPC can determine whether the data variation is within control limits. SPC can measure the quality of performance in learning curve using the upper-range limit and lower-range limit of the control analysis. A case study was conducted on a precast beams installation at a rental multi-story residential project in Jakarta, Indonesia. Based on the measurement, some data are outside of the control limit due to the problems identified in the Pareto diagram. Further analysis by measuring the Process Capability Ratio (Cp) produces a value <1, indicating that project management needs to be careful about process variation.


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