Analytics, simply defined, is the discovery and communication of meaningful patterns in data.1 These identified trends or patterns can then be used to help in the development of process improvement initiatives. This is more complex than the capture, storage, and retrieval of data, and is one of the benefits frequently overlooked in sterile processing when considering moving from a manual to an automated method of managing the instrument reprocessing loop.
To illustrate the difference, here are a few examples of data capture, storage and retrieval versus data analytics:
It is easy to see the value of data analytic statistics over basic data capture, storage, and recall. If you’re considering an automated instrument management system be sure to include in your budget justification the use of data analytics to support continuous process improvement efforts. Yes, reducing paper record storage and quick data retrieval are key advantages of instrument management automation. However, in today’s environment, it is also important to choose a system that can help you identify potential successes or opportunities for improvement by incorporating algorithms and computing software into the reporting package.
1Wikipedia. “Analytics definition”, available at https://en.wikipedia.org/wiki/Analytics. Accessed March 21, 2016.