Before You Begin Moving Averages

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THINGS TO CONSIDER: Before you Begin Moving Averages CHALLENGE: Are your instruments reporting quality results at all times? Moving Averages allows you to monitor analytical performance in your laboratory. This instills confidence that you are releasing accurate results between QC runs. When monitoring moving averages, a lab should consider: 1 Roche Middleware maintains workflow Labs use two main methods to establish sample population, mean and standard deviation of the mean a. By studying historical data: Some labs have historical data that they use for long term studies and reporting. This data may be valuable in establishing a sample population, its mean, and the standard deviation of the mean. This is the best method for determining the inputs for a protocol, as you can experiment with the data before ever entering settings into Moving Averages. This significantly reduces the need to refine the protocol in the future. b. Middleware Calculations: The Moving Average system has the ability to gather data over a set period of time and calculate a mean and standard deviation using that data. This is a very useful tool, but it does require experimentation. PP-US-05039 2 Exclusionary Filtering Exclusionary filters allow abnormal patients to be removed from monitoring, keeping moving averages within range. Without filtering, abnormal patient results will be a part of the moving averages and can skew the data which may cause fluctuations in moving average trend lines and trigger errors. • Location • Ordering Physician • Patient Age • Outliers 3 Determining the N Value It is important to determine the number of results used to plot a moving average point to make sure there are not false alerts or a delay before error detection. The best suggestions to determine the N value are: • Begin with an N value of 50 and adjust as needed • Too few results per plot point= excessive alerts • Too many results per plot point= long lag before detection Learn More: Assessment of “Average of Normals” Quality Control procedures and Guidelines for Implementation, Cembrowski, Chandler, and Westgard, The American Journal of Clinical Pathology 81:4 pp. 492-497 (1984) 4 Final Thoughts Moving averages is a quality control (QC) protocol that uses patient results to monitor the performance of an instrument system. Monitoring with moving averages is a passive process at first, and can be done without the users on the system even knowing that it is running. Labs should use exclusionary filtering to verify that all results filtering into the moving averages pool are similar. Correctly calculating the mean, standard deviation, and N values will lead to more accurate results and trigger less false alerts. Patient result median monitoring for clinical laboratory quality control; Wilson, Roberts, Pavlov, Fontenot, Jackson (ARUP Laboratories, Clinica Chimica Acta 412 (2011) 1441–1446 © 2015 Roche. 461-61552-0715 PP-US-05039.

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