Development and Implementation of an Internal Quality Control and External Quality Assessment Information System for a Regional Medical Laboratory Center: Pilot Design and Implementation Study
Bibliographic record
Abstract
BACKGROUND: The Regional Medical Laboratory Center (RMLC) integrates laboratory departments from hospitals with different levels to optimize resource allocation, enhance testing efficiency, and promote the sharing of professional expertise. Despite these benefits, ensuring consistent quality control (QC) across the RMLC presents a significant challenge due to diverse equipment, procedures, and staff expertise levels. To address these challenges, this study provided an information tool called iLab, developed by Shenzhen Huikang Information Technology Co, Ltd, for managing internal quality control (IQC) and external quality assessment (EQA). OBJECTIVE: This study aimed to develop and implement an integrated informatics platform managing IQC and EQA across RMLC. METHODS: We integrated 4 software programs to manage IQC and EQA, laboratory information system (LIS) supplied by Shanghai Tengcheng Medical Tech-Info Co, Ltd; Quality Control Box (QCBOX) and Quality Control EQA Center (QCEC) provided by Bio-Rad Laboratories (Shanghai) Co, Ltd; and the iLab electronic recording system. The platform centralizes QC data processing, with QCBOX and QCEC handling operational workflows, while iLab oversees documentation, analytics, and approval processes. It was successfully deployed in August 2023 across a pilot network comprising 5 district hospitals, 1 central laboratory, and 4 community health stations in Shenzhen's Luohu District. RESULTS: By leveraging the LIS-QCBOX data flow, the platform streamlined the entire IQC process, which incorporated interlaboratory comparisons and iLab-facilitated postanalysis management. Concurrently, EQA was enhanced through electronic documentation, multirule analysis of acceptable data, regional proficiency testing via QCEC, and measurement uncertainty (MU) estimation. By September 2025, the platform supported 114 users, integrating 133 instruments and 859 active QC items, and generated 2331 monthly IQC analysis reports, 340 EQA multirule analyses, and 289 MU reports. Comparative analysis revealed marked improvements in key metrics from 2023 (preimplementation) to 2024 (postimplementation): the IQC implementation rate rose from 97.79% to 99.87% (P<.001), the report error rate decreased from 0.048% to 0.027% (P<.001), and the intralaboratory turnaround time compliance rate increased from 95.49% to 95.71% (P<.001). The annual EQA unacceptable rate dropped from 0.34% in 2023 to 0% in 2024. The number of ISO (International Organization for Standardization) 15189-accredited test items increased from 203 in 2022 to 206 in 2024. CONCLUSIONS: The iLab system establishes a scalable framework for standardized IQC and EQA management in RMLC, demonstrating significant potential to enhance regional laboratory quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".