Using outlier events to monitor test turnaround time

Steindel SJ, Novis DA. Using outlier events to monitor test turnaround time: A College of American Pathologists Q-Probes study in 495 laboratories. Arch Pathol Lab Med. 1999;123:607-14. (Reprint: abstract JAMA, 1999;282 1408m)

OBJECTIVES: To determine the causes of excessive test turnaround time (TAT) and to identify methods of improvement by studying reasons for those tests reported in excess of 70 minutes from the time the test was ordered (ie, outliers).

DESIGN: Self-directed data-gathering of stat outlier TAT events from intensive care units and emergency departments, with descriptive parameters associated with each event and additional descriptive parameters associated with the participant.

PARTICIPANTS: Laboratories enrolled in the 1996 College of American Pathologists Q-Probes program.

MAIN OUTCOME MEASURES: Components associated with outlier TAT events and outlier TAT rates. RESULTS: Four hundred ninety-six hospital laboratories returned data on 218 551 stat tests, of which 10.6% had TATs in excess of 70 minutes. Ten percent of stat emergency department tests and 14.7% of stat intensive care unit tests were outliers. Major areas in which delays occurred were test ordering, 29.9%; within-laboratory (analytic) phase, 28.2%; collection of the specimen, 27.4%; postanalytic phase, 1.9%; and undetermined, 12.5%. The type of test performed was a significant factor and was independent of location: Chemistry-Multiple Test appeared most frequently ( approximately 40%), followed closely by Hematology-Complete Blood Count (approximately 20%) and Chemistry-Single Test ( approximately 18%). Factors of outlier TAT components for intensive care unit specimens were identified using statistical modeling and included hour of day, type of health care personnel collecting specimen, performing the test in a stat laboratory, and reason for delay. Outlier rates were not associated with any identified factors. The practice parameters of laboratories with outlier rates in the lowest 10th percentile significantly differed from those with rates in the top 10th percentile in test request computerization, report methods, and ordering methods.

CONCLUSIONS: We observed that outlier analysis yields new information, such as type of test and reason for delay, concerning test delays when compared with TAT determination alone. Laboratories experiencing stat test TAT problems should use this tool as an adjunct to routine TAT monitoring for identifying unique causes of delay.