Preanalytical errors in a satellite stat laboratory: A Six Sigma analysis of seven years' data

BACKGROUND

There have been few reports regarding the frequency and types of preanalytical errors in stat laboratories, in particular those occurring in the satellite laboratory setting. The impact of this error type on laboratory performance in this environment is largely unknown. We assessed the performance of a stat laboratory serving a population of predominantly elderly patients with suspected or established diagnoses of cancer using Six Sigma methodology and compared the results to previous work on this subject.

METHODS

We performed an observational retrospective study using data from the period 2013-2020. The clinical setting was a satellite laboratory supporting an outpatient medical clinic. The type and frequency of each type of preanalytical error were compiled and were used to derive the quarterly error rate. Overall and quarterly performance were calculated using Six Sigma methodology.

RESULTS

During the study period 1314 preanalytical errors were identified from 247,271 laboratory tests (0.5% of total test volume). There was a steady decrease in the error rate over the course of the study period, ranging from 1.4% in 2013 to 0.14% in 2020, despite a 290% increase in quarterly test volume during this period. The most common error types encountered were order error, hemolysis, collection error, and lab accident.

CONCLUSION

1) The overall performance of a satellite laboratory with a stat testing menu is comparable to hospital-based laboratory stat testing. 2) The most frequent error types encountered in satellite laboratory stat testing differ from those found in hospital-based laboratories. 3) There was an overall improvement in laboratory performance based on Six Sigma methodology.

Greater Efficiency Observed 12 Months Post-Implementation of an Automatic Tube Sorting and Registration System in a Core Laboratory

BACKGROUND

Sample classification and registration have been recognized as important and time-consuming processes in laboratories. There is increasing pressure on laboratories to automate processes due to intense workload and reduce manual procedures and errors. The aim of the present study was to evaluate the positive effects of an automatic tube registration and sorting system on specimen processing.

METHODS

An automatic tube registration and sorting system (HCTS2000 MK2, m-u-t AG, Wedel, Germany) was evaluated. Turnaround time (TAT), rate of sample rejection and unrealized tests were examined 12 months pre- and post-implementation of the automatic tube sorting and registration system.

RESULTS

The mean TAT of routine chemistry immunoassay, complete blood cell count (CBC) and coagulation samples were significantly improved (P<0.001). The number of rejected samples and unrealized tests was insignificantly decreased post-implementation of the system (0.4% to 0.2% and 4.5% to 1.4%, respectively) (P>0.05).

CONCLUSIONS

By reducing delays and errors in the preanalytical processing and sorting of samples, significant improvements in specimen processing were observed after implementation of the system. These results suggest that an automatic tube registration and sorting system may also be used to improve specimen processing in a higher-volume core laboratory.

Mapping turnaround times (TAT) to a generic timeline: a systematic review of TAT definitions in clinical domains

BACKGROUND

Assessing turnaround times can help to analyse workflows in hospital information systems. This paper presents a systematic review of literature concerning different turnaround time definitions. Our objectives were to collect relevant literature with respect to this kind of process times in hospitals and their respective domains. We then analysed the existing definitions and summarised them in an appropriate format.

METHODS

Our search strategy was based on Pubmed queries and manual reviews of the bibliographies of retrieved articles. Studies were included if precise definitions of turnaround times were available. A generic timeline was designed through a consensus process to provide an overview of these definitions.

RESULTS

More than 1000 articles were analysed and resulted in 122 papers. Of those, 162 turnaround time definitions in different clinical domains were identified. Starting and end points vary between these domains. To illustrate those turnaround time definitions, a generic timeline was constructed using preferred terms derived from the identified definitions. The consensus process resulted in the following 15 terms: admission, order, biopsy/examination, receipt of specimen in laboratory, procedure completion, interpretation, dictation, transcription, verification, report available, delivery, physician views report, treatment, discharge and discharge letter sent. Based on this analysis, several standard terms for turnaround time definitions are proposed.

CONCLUSION

Using turnaround times to benchmark clinical workflows is still difficult, because even within the same clinical domain many different definitions exist. Mapping of turnaround time definitions to a generic timeline is feasible.