Optimizing the availability of 'stat' laboratory tests using Shewhart 'C' control charts.

Health care institutions often face challenges in managing the availability of urgent laboratory tests, particularly during non-business hours. Prior research has shown that 'stat' test requests can vary significantly across different clinical settings. However, it was already known that a lack of standardized protocols could lead to unnecessary or redundant testing. No prior work had resolved how to align test availability with actual clinical demand. This gap motivated the development of a system to prospectively monitor and optimize 'stat' test requests. Existing studies have focused on test accuracy or turnaround time, but not on the frequency of requests. The absence of a systematic approach to reduce unnecessary requests created a need for a new strategy. This paper's contribution lies in its use of a control chart to monitor and adjust test availability. The study aimed to bridge the gap between clinical needs and laboratory operations.

The study aimed to develop and test a method for optimizing the availability of 'stat' laboratory tests in a tertiary care hospital. The specific problem addressed was the high frequency of non-routine 'stat' requests, which can strain laboratory resources. The motivation was to reduce unnecessary testing while maintaining clinical relevance. The authors proposed using a control chart to track request patterns over time. They also sought to evaluate the impact of various interventions on request frequency. The goal was to align the test menu with actual clinical demand. The study focused on a five-year period of prospective data collection. By analyzing trends in request volume, the team aimed to identify effective strategies for reducing unnecessary 'stat' tests.

The researchers introduced a consensus menu of 'stat' tests and monitored all additional requests over five years. They used a Shewhart 'c' control chart to track the frequency of these requests. Laboratory staff triaged incoming 'stat' requests, and consultants reviewed non-routine cases. A quality system certified to ISO 9001 ensured compliance with procedures. The team tested several interventions to assess their impact on request volume. One intervention involved aligning analytical assays with sample collection times. Another involved implementing a hospital-wide laboratory information system. The control chart allowed the team to evaluate the effectiveness of each intervention in real time.

The most effective strategy combined test menu optimization with a laboratory quality system. This approach led to a sevenfold reduction in 'stat' requests. The frequency dropped from one per 2,200 specimens to fewer than one per 32,000 specimens. Matching assay timing with sample collection had no significant effect on request volume. Implementation of a hospital-wide information system also failed to reduce requests. The control chart showed stable patterns after the interventions were applied. The reduction in requests was consistent across the five-year monitoring period. The results suggest that aligning the test menu with clinical patterns is more effective than other interventions. The quality system played a key role in sustaining the improvements.

The authors concluded that optimizing the test menu in line with clinical demand is an effective way to reduce 'stat' requests. They emphasized the importance of using a control chart to monitor and validate interventions. The study showed that aligning analytical timing or implementing an information system had limited impact. They proposed that a quality system is essential for maintaining procedural consistency. The sevenfold reduction in requests was attributed to menu optimization and quality assurance. The results suggest that a data-driven approach can improve laboratory efficiency. The authors highlighted the value of prospectively tracking request patterns. They proposed that this strategy could be adapted to other institutions with similar challenges.