ASCP Poster 2016
Laboratories have used quality control (QC) concepts and theories based on the same statistical calculations and assumptions for decades. Risk management, as stated in Clinical & Laboratory Standards Institute’s (CLSI) EP23A Guideline, adds an ‘acceptable risk criteria.’ Now there is a way to comply with EP23A and also save time, reduce risk to patients, and diminish analytical lab errors and their costs.
The Mathematically-OptimiZed Risk Evaluation™ (M.O.R.E.) method enhances existing QC concepts, while risk metrics unveil a wealth of new understanding – just ‘beyond sigma.’ M.O.R.E. is an Excel-based software that can consistently evaluate QC results and propel the QC process to meet locally-defined quality standards. The M.O.R.E. method begins with basic QC values: target and current mean, the QC chart mean, target and current SD, frequency of QC runs, and any QC rules applied. Then, the medical director and/or clinicians sets medical goals and acceptable risk levels for quantitative analytes, while the administrative director sets costs/test and the average cost of harm to the patient if a medically-unreliable result (MUR) is released from the laboratory for those analytes. Medical goals are similar to allowable error limits; however, clinicians set the goals with their patients in mind. The acceptable risk level drives the number of patients a laboratory is willing to expose to an MUR.
Currently, SQC (Statistical QC) reports a numerical indicator of the level of quality which is subject to variations in calculation and interpretation. The new M.O.R.E. method answers the question, “Is risk acceptable?” with a clear “Yes” or “No.”
The M.O.R.E. method increases the effectiveness of the QC process and its ability to reduce the number of MURs, and also alerts the laboratorian immediately when the analytical process changes enough to allow more than the acceptable number of MURs to be released.
Kim A. Przekop MBA MLS(ASCP)CM, Zoe C. Brooks ART