To explore the design and implementation of automatic verification rules using a laboratory information system (LIS) to optimize blood cell analysis, thereby improving laboratory efficiency and accuracy.

A total of 25 692 laboratory reports were collected and analyzed, including blood cell counts, classifications, and cell morphology indicators. Automatic verification rules encompassing patient information validation, sample information verification, critical value identification, and instrument alarm information validation were designed and implemented using the built-in scripting language of the LIS.

The implementation of the automatic verification rules, which comprised 64 rules across five categories, processed 25 692 reports. Among these, 19 140 reports passed automatic verification, resulting in a pass rate of 74.50%. A total of 6552 reports failed automatic verification, triggering 11 986 rule interceptions. The automatic verification system reduced the median turnaround time (TAT) from 15 minutes to 3 minutes, decreased the error rate of reports from 0.082% (16/19 526) to 0.008% (2/25 692), and lowered patient dissatisfaction from 15.38% (10/65) to 1.19% (1/84).

The design and implementation of automatic verification rules effectively enhance laboratory efficiency and quality, significantly improving patient satisfaction. Future research should aim to further optimize these rules to address more complex clinical scenarios and explore advanced technologies to support the continued development of automated blood cell analysis.