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Improving regional medical laboratory center report quality through a report recall management system

  • Chuang Zhang , Xiang Ji , Jiehong Wei , Xiaowen Dou , Dayang Chen EMAIL logo and Xiuming Zhang EMAIL logo
Published/Copyright: September 7, 2023
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 2

Abstract

Objectives

Currently, most medical laboratories do not have a dedicated software for managing report recalls, and relying on traditional manual methods or laboratory information system (LIS) to record recall data is no longer sufficient to meet the quality management requirements in the large regional laboratory center. The purpose of this article was to describe the research process and preliminary evaluation results of integrating the Medical Laboratory Electronic Record System (electronic record system) laboratory report recall function into the iLab intelligent management system for quality indicators (iLab system), and to introduce the workflow and methods of laboratory report recall management in our laboratory.

Methods

This study employed cluster analysis to extract commonly used recall reasons from laboratory report recall records in the electronic record system. The identified recall reasons were validated for their applicability through a survey questionnaire and then incorporated into the LIS for selecting recall reasons during report recall. The statistical functionality of the iLab system was utilized to investigate the proportion of reports using the selected recall reasons among the total number of reports, and to perform visual analysis of the recall data. Additionally, we employed P-Chart to establish quality targets and developed a “continuous improvement process” electronic flow form.

Results

The reasons for the recall of laboratory reports recorded in the electronic recording system were analyzed. After considering the opinions of medical laboratory personnel, a total of 12 recall reasons were identified, covering 73.05 % (1854/2538) of the recalled laboratory reports. After removing data of mass spectra lab with significant anomalies, the coverage rate increased to 82.66 % (1849/2237). The iLab system can generate six types of statistical graphs based on user needs, including statistical time, specialty labs (or divisions), test items, reviewers, reasons for report recalls, and distribution of the recall frequency of 0–24 h reports. The control upper limit of the recall rate of P-Chart based on laboratory reports can provide quality targets suitable for each professional group at the current stage. Setting the five stages of continuous process improvement reasonably and rigorously can effectively achieve the goal of quality enhancement.

Conclusions

The enhanced iLab system enhances the intelligence and sustainable improvement capability of the recall management of laboratory reports, thus improving the efficiency of the recall management process and reducing the workload of laboratory personnel.

Keywords: laboratory report; recall; intelligent management; continuous improvement
Corresponding authors: Dayang Chen and Xiuming Zhang, Medical Laboratory of the Third Affiliated Hospital of Shenzhen University, 33 Zhongyuan Road, Buji St, 518000 Shenzhen, P.R. China, Phone: +86 13823985820 (X. Zhang), E-mail: (D. Chen), E-mail: (X. Zhang)

Funding source: Shenzhen Key Medical Discipline

Award Identifier / Grant number: SZXK054

Acknowledgments

We would like to thank HuiKang Technology for their digital expertise and technical support in software development and the engineers of Luohu Hospital Group for their help in system commissioning.

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Informed consent: Not applicable.

  3. Author contributions: Chuang Zhang participated in the implementation of this study, including data collection, data analysis, statistical analysis, and writing of the manuscript. Dayang Chen provided guidance for this study. Xiang Ji, Jiehong Wei, and Xiaowen Dou are involved in the monitoring and maintenance of quality indicators. Xiuming Zhang participated in the topic design, technical guidance, and funding support of this study in Clinical Chemistry and Laboratory Medicine. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: Shenzhen Key Medical Discipline (SZXK054).

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2023-0786).

Received: 2023-07-25
Accepted: 2023-08-21
Published Online: 2023-09-07
Published in Print: 2024-01-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2023-0786
Keywords for this article
laboratory report; recall; intelligent management; continuous improvement

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Rapid rule-in and rule-out protocols of acute myocardial infarction using hs-cTnI and hs-cTnT methods
  4. Reviews
  5. A guide to conducting systematic reviews of clinical laboratory tests
  6. Improving diagnosis and treatment of hypomagnesemia
  7. Opininon Paper
  8. Documenting and validating metrological traceability of serum alanine aminotransferase measurements: a priority for medical laboratory community for providing high quality service in hepatology
  9. EFLM Paper
  10. Assessing the status of European laboratories in evaluating biomarkers for chronic kidney diseases (CKD) and recommendations for improvement: insights from the 2022 EFLM Task Group on CKD survey
  11. General Clinical Chemistry and Laboratory Medicine
  12. Bland and Altman agreement method: to plot differences against means or differences against standard? An endless tale?
  13. Assessment of three equations to calculate plasma LDL cholesterol concentration in fasting and non-fasting hypertriglyceridemic patients
  14. Validation of metrological traceability of the new generation of Abbott Alinity alkaline phosphatase assay
  15. Quality assurance programs for vitamin A and E in serum: are we doing enough to assess laboratory performance?
  16. A supervised machine-learning approach for the efficient development of a multi method (LC-MS) for a large number of drugs and subsets thereof: focus on oral antitumor agents
  17. Performance evaluation of alternate ESR measurement method using BC-780 automated hematology analyzer: a comparison study with the Westergren reference method
  18. Total error in lymphocyte subpopulations by flow cytometry-based in state of the art using Spanish EQAS data
  19. Development and multi-center validation of a fully automated digital immunoassay for neurofilament light chain: toward a clinical blood test for neuronal injury
  20. CSF and plasma Aβ42/40 across Alzheimer’s disease continuum: comparison of two ultrasensitive Simoa® assays targeting distinct amyloid regions
  21. Improving regional medical laboratory center report quality through a report recall management system
  22. Cardiovascular Diseases
  23. Evaluation of the analytical and clinical performance of a new high-sensitivity cardiac troponin I assay: hs-cTnI (CLIA) assay
  24. Hemodialysis and biomarkers of myocardial infarction – a cohort study
  25. Letters to the Editor
  26. Chatbot GPT can be grossly inaccurate
  27. Blood collection in heparin vs. EDTA results in an inflammasome-independent increase in monocyte distribution width at 4 h
  28. Week-to-week within-subject and between-subject biological variation of copeptin
  29. The effect of unintended shortage in technical resources on the quality of endpoint clinical laboratory diagnosis
  30. Epigenetic signatures of cfDNA enable a topological assignment of tissue damage
  31. Six Sigma driven QC in antibody testing for infectious diseases
  32. Extensive analytical evaluation of the performances of the new DiaSys PCT assay and comparison with Elecsys B·R·A·H·M·S PCT test on Roche Cobas and B·R·A·H·M·S PCT-sensitive Kryptor
  33. Comment to: Extensive analytical evaluation of the performances of the new DiaSys PCT assay and comparison with Elecsys B·R·A·H·M·S PCT test on Roche Cobas and B·R·A·H·M·S PCT-sensitive Kryptor
  34. Evaluation of serum cortisol measurement by Ortho Vitro after stimulation by ACTH or insulin hypoglycaemia
  35. Diagnostic and prognostic value of quantitative detection of antimitochondrial antibodies subtype M2 using chemiluminescence immunoassay in primary biliary cholangitis
  36. Blood plasma biomarkers for Alzheimer’s disease: Aβ1–42/1–40 vs. AβX–42/X–40
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