A-203 A Disconnect Between Detectable Patient Error Rates and QC Understanding

Abstract

Abstract Background ISO 14971 defines “risk evaluation as the process of comparing the estimated risk against given risk criteria to determine the acceptability of the risk. Effective QC processes would detect if a stable acceptable error rate became unacceptable. Methods A survey was circulated through LinkedIn and Labvine. Forty respondents who included their credentials were included in the final analysis. Questions included: 1. What is the best choice for acceptable error rate when an analytical process is stable and in control, 2. If an analytical process “fails,” how many errors are you willing to report before the lab should stop reporting, and 3. “If an analytical process has a 5% stable error, how high would the error rate after failure need to become for a 1-2s rule to detect failure with one QC sample in a single QC run?” This interactive poster will allow viewers to cast their vote for Question 4 and then reveal an LJ chart depicting a stable error rate of 5% with a shift to show the number of 1-2s failures with their chosen acceptable post-failure error rate. Results During stable operation, only 8% wanted zero errors while 38% voted that they would allow up to 5% errors; After failure, 66% wanted to report zero errors and only 10% would allow 5% failure. 69% believed they would see a 1-2s failure in one run if the error rate rose to less than 50%; 19% believed it required a 100% error rate. Conclusions There is a strange disconnect where most believed it was acceptable to report more errors during stable operation than if the method failed. There is a widespread misunderstanding of the logic that connects management of acceptable patient risk with the capabilities of statistical QC.