ABSTRACT
BACKGROUND: Clinical laboratories have focused on quality for more than 60 years. While analytic quality is considered excellent in most laboratories, nonanalytic quality is an area for focused improvement. One of our quality metrics, lost samples, has been tracked continuously for 25 years and has demonstrated steady improvement. Nonanalytic processes have become highly automated within our organization, which, we believe, was a major factor in reducing lost samples. We have also implemented numerous behavioral controls and completed many process reengineering projects that have had a demonstrable effect on lost sample rates. Our objective in this study was to determine the overall contributions of our error-proofing methods to reducing lost samples. METHODS: Using data spanning 25 years, we plotted the correlation between lost samples and the implementation dates for 8 major phases of automation along with 16 process improvements and engineering controls. RESULTS: The lost sample rate decreased nearly 100-fold. In Six Sigma terms, the 12-month moving average for lost samples currently hovers around 5.85-sigma, with several months at or better than 6-sigma. While implementation of process improvements, engineering controls, and automation all contributed to the reduction, automation was the most significant contributor. CONCLUSIONS: The custom automation in use by our laboratory has led to improved nonanalytic quality. Although this level of automation might not be possible for all laboratories, our description of 16 behavior and engineering controls may be useful to other laboratories seeking to design high-quality nonanalytic processes.
ABSTRACT
BACKGROUND: Mislabeled samples are a serious problem in most clinical laboratories. Published error rates range from 0.39/1000 to as high as 1.12%. Standardization of bar codes and label formats has not yet achieved the needed improvement. The mislabel rate in our laboratory, although low compared with published rates, prompted us to seek a solution to achieve zero errors. METHODS: To reduce or eliminate our mislabeled samples, we invented an automated device using 4 cameras to photograph the outside of a sample tube. The system uses optical character recognition (OCR) to look for discrepancies between the patient name in our laboratory information system (LIS) vs the patient name on the customer label. All discrepancies detected by the system's software then require human inspection. The system was installed on our automated track and validated with production samples. RESULTS: We obtained 1 009 830 images during the validation period, and every image was reviewed. OCR passed approximately 75% of the samples, and no mislabeled samples were passed. The 25% failed by the system included 121 samples actually mislabeled by patient name and 148 samples with spelling discrepancies between the patient name on the customer label and the patient name in our LIS. Only 71 of the 121 mislabeled samples detected by OCR were found through our normal quality assurance process. CONCLUSIONS: We have invented an automated camera system that uses OCR technology to identify potential mislabeled samples. We have validated this system using samples transported on our automated track. Full implementation of this technology offers the possibility of zero mislabeled samples in the preanalytic stage.
