Test Cancellation: A College of American Pathologists Q-Probes Study.

CONTEXT: Requests for laboratory testing are canceled after a specimen has already been collected from the patient for many reasons. Regardless of the cause, test cancellation represents a significant resource expenditure for laboratories, and many cancellation events impact patient care by delaying the reporting of test results. OBJECTIVE: To survey a wide variety of hospitals to determine the rate, causes, and circumstances surrounding laboratory test cancellation events. DESIGN: Institutions (N = 52) prospectively monitored their test cancellation events during a 6-week period or until 75 cancellation events occurred. Information regarding the test cancellation was recorded, including the primary reason for canceling the test. The rate of test cancellation was calculated based on laboratory specimen volume. Laboratory policies relevant to test cancellation were also surveyed. RESULTS: A total of 3471 canceled tests were recorded by participating laboratories of 1,118,845 specimens they accessioned, resulting in an aggregate test cancellation rate of 3.1 per 1000 accessions. The most frequently reported reason for test cancellation occurred in the preanalytical phase, and was a duplicate test request, followed by specimen quality reasons including hemolyzed/clotted specimens and insufficient sample quantity for testing. Very few cancellations occurred during the analytical phase of testing. Lower test cancellation rates were reported by larger institutions and by laboratories that received fewer specimens from inpatients. CONCLUSIONS: Cancellation of patient tests after a specimen had been collected and received remains a significant issue for clinical laboratories. Laboratories should monitor causes of test cancellation to identify targets for process improvement efforts and to improve laboratory utilization. Cancellation events due to incomplete identification or poor specimen quality potentially delay patient care. Cancellations due to duplicate orders or excessive frequency of testing represent operational challenges for the laboratory and inefficiency in the health care system. Policies related to test cancellation should be clearly specified and communicated to users of laboratory services.

Seven Q-Tracks monitors of laboratory quality drive general performance improvement: experience from the College of American Pathologists Q-Tracks program 1999-2011.

CONTEXT: Many production systems employ standardized statistical monitors that measure defect rates and cycle times, as indices of performance quality. Clinical laboratory testing, a system that produces test results, is amenable to such monitoring. OBJECTIVE: To demonstrate patterns in clinical laboratory testing defect rates and cycle time using 7 College of American Pathologists Q-Tracks program monitors. DESIGN: Subscribers measured monthly rates of outpatient order-entry errors, identification band defects, and specimen rejections; median troponin order-to-report cycle times and rates of STAT test receipt-to-report turnaround time outliers; and critical values reporting event defects, and corrected reports. From these submissions Q-Tracks program staff produced quarterly and annual reports. These charted each subscriber's performance relative to other participating laboratories and aggregate and subgroup performance over time, dividing participants into best and median performers and performers with the most room to improve. Each monitor's patterns of change present percentile distributions of subscribers' performance in relation to monitoring durations and numbers of participating subscribers. Changes over time in defect frequencies and the cycle duration quantify effects on performance of monitor participation. RESULTS: All monitors showed significant decreases in defect rates as the 7 monitors ran variously for 6, 6, 7, 11, 12, 13, and 13 years. The most striking decreases occurred among performers who initially had the most room to improve and among subscribers who participated the longest. All 7 monitors registered significant improvement. Participation effects improved between 0.85% and 5.1% per quarter of participation. CONCLUSIONS: Using statistical quality measures, collecting data monthly, and receiving reports quarterly and yearly, subscribers to a comparative monitoring program documented significant decreases in defect rates and shortening of a cycle time for 6 to 13 years in all 7 ongoing clinical laboratory quality monitors.

Frequency monitoring of hemoglobin A1c, low-density lipoprotein, and urine protein laboratory testing: a College of American Pathologists Q-Probes study.

CONTEXT: Ensuring laboratory tests are performed at intervals consistent with established, evidence-based guidelines in diabetic patients is an important aspect of laboratory utilization. OBJECTIVES: To measure how frequently diabetes mellitus patients are tested for hemoglobin A1c (HbA1c), low-density lipoprotein, and urine protein and to determine whether the frequencies with which these analytes are tested are consistent with recognized guidelines. DESIGN: Participants prospectively identified up to 40 patients with a current HbA1c result and at least 2 previous measurements within the past 24 months. For each patient identified with at least 3 HbA1c measurements during a 24-month period, participants also determined the number of low-density lipoprotein and spot or random urine protein tests that were performed during those 24 months. RESULTS: Participants from 49 institutions submitted a total of 1915 cases that met the study criteria of at least 3 HbA1c test results within the past 24 months. Approximately 95% of patients had 8 or fewer HbA1c tests in the past 24 months; 79% of patients with at least 3 HbA1c tests had at least 2 low-density lipoprotein tests and 27% had at least 2 urine protein tests reported during the 24-month study period. CONCLUSION: Participating laboratories were generally successful in documenting appropriate utilization of HbA1c testing in diabetes mellitus monitoring, but had more difficulty documenting that diabetes mellitus patients had annual testing for low-density lipoprotein and urine protein.

Patient safety & post-analytical error.

Post-analytical laboratory processes have been considered to be less prone to error than preanalytical processes because of the widespread adoption of laboratory automation and interfaced laboratory reporting. Quality monitors and controls for the post-analytical process have focused on critical result notification, meeting established turnaround time goals, and review of changed reports. The rapid increase in the adoption of electronic health records has created a new role for laboratory professionals in the management of patient test results. Laboratory professionals must interface with the clinical side of the health care team in establishing quality control for post-analytical processes, particularly in high-risk transitions of care.

Reducing specimen identification errors.

In 2006, the University of Wisconsin Hospital and Clinics identified that the number of specimen identification errors each month was much greater than desired and represented a significant patient safety issue. A collaborative performance improvement approach between nursing and the laboratory was undertaken for the inpatient, ambulatory, and surgical services areas, with the focus on creation of a just culture. Between 2007 and 2011, interventions were successful in significantly reducing the number of errors by 85%.

Staffing benchmarks for clinical laboratories: a College of American Pathologists Q-Probes study of laboratory staffing at 98 institutions.

CONTEXT: Publicly available information concerning laboratory staffing benchmarks is scarce. One of the few publications on this topic summarized the findings of a Q-Probes study performed in 2004. This publication reports a similar survey with data collected in 2010. OBJECTIVE: To assess the relationship between staffing levels in specified laboratory sections and test volumes in these sections and quantify management span of control. DESIGN: The study defined 4 laboratory sections: anatomic pathology (including cytology), chemistry/hematology/immunology, microbiology, and transfusion medicine. It divided staff into 3 categories: management, nonmanagement (operational or bench staff), and doctoral (MD, PhD) supervisory staff. People in these categories were tabulated as full-time equivalents and exclusions specified. Tests were counted in uniform formats, specified for each laboratory section, according to Medicare rules for the bundling and unbundling of tests. RESULTS: Ninety-eight participating institutions provided data that showed significant associations between test volumes and staffing for all 4 sections. There was wide variation in productivity based on volume. There was no relationship between testing volume per laboratory section and management span of control. Higher productivity in chemistry/hematology/immunology was associated with a higher fraction of tests coming from nonacute care patients. In both the 2004 and 2010 studies, productivity was inseparably linked to test volume. CONCLUSIONS: Higher test volume was associated with higher productivity ratios in chemistry/hematology/immunology and transfusion medicine sections. The impact of various testing services on productivity is section-specific.

The truth about quality: medical usefulness and analytical reliability of laboratory tests.

BACKGROUND: In this age of evidence-based medicine, nothing is more important than the quality of laboratory tests. It is commonly thought that laboratory tests provide two-thirds to three-fourths of the information used for making medical decisions. If so, test results had better tell the truth about what is happening with our patients. METHODS: The age-old "truth standard" for the quality of evidence describes three dimensions that are important-a test should tell the truth, the whole truth, and nothing but the truth. This three-dimensional model can be used to characterize the clinical and analytical reliability of laboratory tests and guide the translation of outcome criteria, or quality goals, into practical specifications for method performance. RESULTS: Clinical reliability, or medical usefulness, should assess the correctness of patient classifications based on stated test interpretation guidelines, taking into account the precision and accuracy of the laboratory method, and allowing for the known within-subject biologic variation and the QC needed to detect method instability. Analytical reliability should assess the correctness of a test result based on a stated error limit, taking into account the precision and accuracy of the method and allowing for the QC necessary to detect method instability. These assessments challenge the reliability of current tests for cholesterol, glucose, and glycated hemoglobin in the implementation of U.S. national clinical guidelines. CONCLUSIONS: Evidence-based medicine must employ scientific methodology for translating test interpretation guidelines into practical, bench-level, operating specifications for the imprecision and inaccuracy allowable for a method and the QC necessary to detect method instability.