Abstract
Background: A performance improvement model was developed that focuses on the analysis and interpretation of performance indicator (PI) data using statistical process control and benchmarking. PIs are suitable for comparison with benchmarks only if the data fall within the statistically accepted limit-that is, show only random variation. Specifically, if there is no significant special-cause variation over a period of time, then the data are ready to be benchmarked.
Methods: The proposed Define, Measure, Control, Internal Threshold, and Benchmark model is adapted from the Define, Measure, Analyze, Improve, Control (DMAIC) model. The model consists of the following five steps: Step 1. Define the process; Step 2. Monitor and measure the variation over the period of time; Step 3. Check the variation of the process; if stable (no significant variation), go to Step 4; otherwise, control variation with the help of an action plan; Step 4. Develop an internal threshold and compare the process with it; Step 5.1. Compare the process with an internal benchmark; and Step 5.2. Compare the process with an external benchmark.
Results: The steps are illustrated through the use of health care-associated infection (HAI) data collected for 2013 and 2014 from the Infection Control Unit, King Fahd Hospital, University of Dammam, Saudi Arabia.
Conclusion: Monitoring variation is an important strategy in understanding and learning about a process. In the example, HAI was monitored for variation in 2013, and the need to have a more predictable process prompted the need to control variation by an action plan. The action plan was successful, as noted by the shift in the 2014 data, compared to the historical average, and, in addition, the variation was reduced. The model is subject to limitations: For example, it cannot be used without benchmarks, which need to be calculated the same way with similar patient populations, and it focuses only on the "Analyze" part of the DMAIC model.