process. Health care organizations measure quality, productivity, efficacy, customer satisfaction, and cost-effectiveness. Performance quality and efficiency can be mea- sured using standards called metrics. Common metrics used in health care include (but are not limited to) the following9
:
› patient satisfaction data › defect or error rates › timeliness › cycle time (time from the beginning to the end of a process) › cost per unit › waste › environmental effects › efficiency › labor hours › success rates › safety risk › volume For each metric listed, there are different types of data you can collect. Historical
data are data that were already collected in some capacity. Historical data can be collated and used as premeasures. Historical data are not always available, but when they are, they can be very valuable, saving you substantial time and providing insights on past trends. If it is not already available in a report, you may need to work with the information technology (IT) department to generate one. IT depart- ments are generally busy places, so build realistic expectations into your projected timeline. If historical data are not available, you will need to collect data manually and
prospectively to serve as baseline data. The advantage to this is that you can collect the data you need and utilize a format you prefer. This can be time-consuming, but it is important to understand your current performance before you attempt to make improvements. One of the biggest mistakes seen in process improvement is stating that there is a problem without having data to support that claim. For example, you may perceive that there is an increase in the number of patients with weight loss in your organization, but once you actually gather and analyze the data, you may discover that the facts do not support your perception. When gathering baseline data to determine if a problem exists, a sample size
must be determined. Sampling in quality improvement work differs from research settings due to the variable nature of the population and the dynamic nature of the typical health care environment where many factors are uncontrolled. Since data collection involves staff time and resources, Perla and colleagues13
recommend
considering the following factors when determining the sample size for quality im- provement work:
› the team’s degree of belief that the change will result in improvement › the costs associated with a failed test › the readiness of those who have to make the change
272 EFFECTIVE LEADERSHIP & MANAGEMENT IN NUTRITION & DIETETICS
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