Quality error rates in point-of-care testing.

BACKGROUND: Although a theoretical consideration suggests that point-of-care testing (POCT) might be uniquely vulnerable to error, little information is available on the quality error rate associated with POCT. Such information would help inform risk/benefit analyses when one considers the introduction of POCT. METHODS: This study included 1 nonacute and 2 acute hospital sites. The 2 acute sites each had a 24-h central laboratory service. POCT was used for a range of tests, including blood gas/electrolytes, urine pregnancy testing, hemoglobin A(1c) (Hb A(1c)), blood glucose, blood ketones, screening for drugs of abuse, and urine dipstick testing. An established Quality Query reporting system was in place to log and investigate all quality errors associated with POCT. We reviewed reports logged over a 14-month period. RESULTS: Over the reporting period, 225 Quality Query reports were logged against a total of 407 704 POCT tests. Almost two-thirds of reports were logged by clinical users, and the remainder by laboratory staff. The quality error rate ranged from 0% for blood ketone testing to 0.65% for Hb A(1c) testing. Two-thirds of quality errors occurred in the analytical phase of the testing process. These errors were all assessed as having no or minimal adverse impact on patient outcomes; however, the potential adverse impact was graded higher. CONCLUSIONS: The quality error rate for POCT is variable and may be considerably higher than that reported previously for central laboratory testing.

Clinical outcome indicators, disease prevalence and test request variability in primary care.

AIM: To describe differences in biochemistry test request rates (adjusted for practice size) between general practices and to investigate whether differences in HbA(1c) and thyroid function test request rates are related either to the practice prevalence of hypothyroidism and diabetes or to Quality and Outcome Framework (QOF) scores. METHODS: Information on test request rates, prevalence of diabetes and hypothyroidism, and QOF data over a one-year period were obtained from 58 practices covering a population of 284,609 patients. Spearman's rank correlation tests were used to investigate relationships between adjusted test request rates. RESULTS: There was wide variability in adjusted test request rates (lowest for HbA(1c) and highest for immunoglobulins). The ranking of practices for different tests was highly correlated. There was no relationship between adjusted test request rates for HbA(1c) and thyroid function and the reported prevalence of diabetes and hypothyroidism, respectively, nor was there any relationship with QOF scores in diabetes and hypothyroidism. CONCLUSIONS: There is wide variability in test request rates in general practice that do not appear to be related to disease prevalence or crude clinical outcome measures.

The development of a system for the reporting, classification and grading of quality failures in the clinical biochemistry laboratory.

BACKGROUND: There is no agreed system for the reporting, classification and grading of the severity of quality failures in the clinical biochemistry laboratory. METHODS: A 'Quality Query' reporting system was set up to log all quality failures identified by staff and service users. Quality failures were classified into three major groups of the preanalytical, analytical and postanalytical phases with appropriate subcategories in each group. The severity of each quality failure was graded using a five-point scoring system incorporating both actual ('A') and potential ('P') score elements. The 'A' score measured the actual adverse impact of the quality failure on patient care, while the 'P' score measured the 'worst case' potential outcome that might have resulted. The system was assessed over a 19-month period. RESULTS: Three hundred and ninety-seven Quality Query reports were completed (0.085% of all requests). Breakdown by cause: pre-analytical phase--88.9%, analytical phase--9.6%, post-analytical phase--1.5%. The quality failure severity 'A' scores were skewed towards a low adverse impact on patient care: 72.7% allocated an 'A' score of 1 (least severe grade). The 'P' scores were skewed towards a high potential impact on patient care: 65.9% allocated a 'P' score of 5 (most severe grade). CONCLUSIONS: The Quality Query reporting system proved easy to integrate into routine laboratory practice. Although the great majority of quality failures had minimal adverse impact on patient care, the potential for adverse outcomes was much higher. This system generates important information on laboratory performance and helps inform risk management priorities.