Using analytics to manage QA and reduce laboratory errors

By Vanessa Hawrylak, MS, MT(ASCP), Thomas Joseph, MBA, MT(ASCP), Tim Bickley, MT(ASCP), MBA, CPHIMS, and Kristina Ziaugra


oday, many laboratories are still measuring their data manually, a time-consuming process subject to human error. Laboratory managers often struggle to obtain timely metrics, as labora- tory information systems (LIS) provide only limited management reports, and often the metrics received are a month old and thus of limited value in improving quality and reducing errors. As a result, laboratories are increasingly turning to laboratory analytics/business intelligence as a solution to these challenges for their data mining needs. A laboratory analytics system, however, processes a wealth of laboratory data in seconds, not only ensuring that laboratory management has more time to focus on other tasks, but also providing the means for managers and supervisors to monitor and maintain higher standards of quality. Laboratory analytics are proving to be a beneficial tool in ensuring that large amounts of data can be analyzed and presented in meaningful reports that easily identify opportuni- ties to catch and correct laboratory errors such as specimen defects, shifts in analyzer results, inappro- priate utilization of laboratory tests, and can also be used as evidence of compliance (EOC) to accrediting laboratory compliance agencies.

Specimen defects and reference range changes Laboratory analytics/business intelligence tools can assist in identifying specimen defects, which is necessary to determine areas of improvement. Important specimen information can be captured, such as how many specimens are ranked Quantity Not Sufficient (QNS), or the number of hemolyzed specimens, with detailed information such as who collected the specimen and where the specimen was collected. A hemolyzed or QNS specimen requires re-sticking a patient and can create a delay in result reporting. With an effective laboratory analytics system in place, managers can easily view all specimens for hemolysis and QNS rates and take necessary correc- tive action. The laboratory business intelligence/ analytics system provides insight into where a hemolyzed specimen came from, what clinic, which ward, and even the nurse or phlebotomist who drew the specimen. By performing hemolysis and QNS audits to identify patient locations and col- lection, staff members with the highest number and proportion of occurrences can be identified. Laboratory management can then act to identify which staff members require retraining to improve quality. While it may not be possible to retrain everyone for all quality problems, it is possible to identify where most quality assurance (QA) issues


originate, providing management with the insight to focus retraining for the greatest effect. Defective test results can pose significant finan- cial implications to the health system when lab tests are misinterpreted and misused. An analytics system can generate a comparison of test results that allows laboratory management to see defini- tive analytic results to quickly answer questions about instrument performance over time. With data available daily, management can ensure that a per- formance problem never goes undetected and that quality managers and lab directors are kept aware of the source of laboratory problems.

Inappropriate test utilization The consequences of unnecessary testing for patient care can include iatrogenic anemia, time spent on insignificant abnormal results, incorrect diagno- ses, and longer length of stay. Various strategies can be employed to reduce overutilization of test- ing, including requisition redesign, hard and soft stops in the computerized physician order entry (CPOE), test formularies, education, and audits. No one strategy is sufficient, however, and an auditing capability plays a critical role. With the data from a real-time analytics system, laboratory managers will know the most important areas of unnecessary testing so rules can be developed for the electronic health record (EHR), providing soft stop guidance to physicians. A real-time analytics system also identifies common categories of unnec- essary testing. These common categories range from screening/reflexing/normalcy, such as ordering an FT4 when the TSH is normal, to redundant testing such as troponin and CKMBs ordered together, to excessive frequency of repeat testing (e.g., HbA1c should not be ordered more than once every 21 days). From this, laboratory management can iden- tify benchmarks based on tests per inpatient admis- sion, length of stay, and length of stay vs. tests per admission. Using this data and benchmarks, labora- tories can develop strategies, and measures can be taken to limit obsolete tests, limit esoteric tests, and minimize bundles of tests.

Shifts and trends in analyzer results A laboratory analytics system can assist in reducing lab errors by providing a means to monitor qual- ity control (QC) and assist in identifying shifts and trends in analyzer results. For example, analysis using either coefficient of variation ratio (CVR) and standard deviation index (SDI) or a standard analytical null hypothesis theory provides two different approaches to quickly determine if any instruments are reporting

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