Continual improvement of the pre-analytical process in a public health laboratory with quality indicators-based risk management.

Background Quality indicators (QIs) and risk management are important tools for a quality management system designed to reduce errors in a laboratory. This study aimed to show the effectiveness of QI-based risk management for the continual improvement of pre-analytical processes in the Kayseri Public Health Laboratory (KPHL) which serves family physicians and collects samples from peripheral sampling units. Methods QIs of pre-analytical process were used for risk assessment with the failure modes and effects analysis (FMEA) method. Percentages and risk priority numbers (RPNs) of QIs were quantified. QI percentages were compared to the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) performance specifications and RPNs were compared to risk level scale, and corrective actions planned if needed. The effectiveness of risk treatment actions was re-evaluated with the new percentages and with RPNs of predefined QIs. Results RPNs related to four QIs required corrective action according to the risk evaluation scale. After risk treatment, the continual improvement was achieved for performance and risk level of "transcription errors", for risk levels of "misidentified samples" and "not properly stored samples" and for the performance of "hemolyzed samples". "Not properly stored samples" had the highest risk score because of sample storage and centrifugation problems of peripheral sampling units which are not under the responsibility of the KPHL. Conclusions Public health laboratories may have different risk priorities for pre-analytical process. Risk management based on predefined QIs can decrease the risk levels and increase QI performance as evidence-based examples for continual improvement of the pre-analytical process.

Total Analytical Error and Measurement Uncertainty for Analytical Performance Evaluation and Determination of Gray Zones of Glucose Critical Value Limits.

OBJECTIVE: Total analytical error (TAE) and measurement uncertainty (MU) are important approaches to evaluating and improving the quality of measurement procedures. This study evaluates glucose analytical performance (AP) according to TAE and MU and calculates gray zones of glucose critical value limits. METHODS: Using TAE and MU values, AP was evaluated according to 5 different analytical performance specifications (APS) and the gray zones of critical value limits were calculated. The number of patients in these zones was compared. RESULTS: TAE was higher than MU at all 3 levels. The AP for the low glucose level was poor. The number of patients in the gray zones was statistically higher in the TAE groups than in the MU groups (P < .05). CONCLUSION: TAE and MU values can be used to evaluate the AP of glucose measurement as well as to evaluate the compliance of patient results with decision limits by creating gray zones.