Journal:Use of middleware data to dissect and optimize hematology autoverification

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Full article title Use of middleware data to dissect and optimize hematology autoverification
Journal Journal of Pathology Informatics
Author(s) Starks, Rachel D.; Merrill, Anna E.; Davis, Scott R.; Voss, Dena R.; Goldsmith, Pamela, J.; Brown, Bonnie S.; Kulhavy, Jeff; Krasowski, Matthew D.
Author affiliation(s) University of Iowa Hospitals and Clinics
Primary contact Log-in required
Year published 2021
Volume and issue 12
Page(s) 19
DOI 10.4103/jpi.jpi_89_20
ISSN 2153-3539
Distribution license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Website https://www.jpathinformatics.org/text.asp?2021/12/1/19/313145
Download https://www.jpathinformatics.org/temp/JPatholInform12119-643471_175227.pdf (PDF)
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Abstract

Background: Hematology analysis comprises some of the highest volume tests run in clinical laboratories. Autoverification of hematology results using computer-based rules reduces turnaround time for many specimens, while strategically targeting specimen review by technologist or pathologist.

Methods: Autoverification rules had been developed over a decade at an 800-bed tertiary/quarternary care academic medical central laboratory serving both adult and pediatric populations. In the process of migrating to newer hematology instruments, we analyzed the rates of the autoverification rules/flags most commonly associated with triggering manual review. We were particularly interested in rules that on their own often led to manual review in the absence of other flags. Prior to the study, autoverification rates were 87.8% (out of 16,073 orders) for complete blood count (CBC) if ordered as a panel and 85.8% (out of 1,940 orders) for CBC components ordered individually (not as the panel).

Results: Detailed analysis of rules/flags that frequently triggered indicated that the immature granulocyte (IG) flag (an instrument parameter) and rules that reflexed platelet by impedance method (PLT-I) to platelet by fluorescent method (PLT-F) represented the two biggest opportunities to increase autoverification. The IG flag threshold had previously been validated at 2%, a setting that resulted in this flag alone preventing autoverification in 6.0% of all samples. The IG flag threshold was raised to 5% after detailed chart review; this was also the instrument vendor's default recommendation for the newer hematology analyzers. Analysis also supported switching to PLT-F for all platelet analysis. Autoverification rates increased to 93.5% (out of 91,692 orders) for CBC as a panel and 89.8% (out of 11,982 orders) for individual components after changes in rules and laboratory practice.

Conclusions: Detailed analysis of autoverification of hematology testing at an academic medical center clinical laboratory that had been using a set of autoverification rules for over a decade revealed opportunities to optimize the parameters. The data analysis was challenging and time-consuming, highlighting opportunities for improvement in software tools that allow for more rapid and routine evaluation of autoverification parameters.

Keywords: algorithms, clinical laboratory information system, hematology, informatics, middleware

Introduction

In the realm of laboratory information system (LIS) and/or middleware software, autoverification refers to the use of computer-based rules to determine the appropriate release of laboratory test results. With the expansion of data management systems in the lab, autoverification is now a routine practice in core clinical laboratories[1][2][3][4], where the use of well-designed autoverification rules improves both quality and efficiency.[1][2][4] Over the years, autoverification rules have been described in detail for clinical chemistry, blood gas, and coagulation analysis, often achieving autoverification rates of >90%.[5][6][7][8][9][10][11]


References

  1. ↑ 1.0 1.1 Crolla, Lawrence J.; Westgard, James O. (1 September 2003). "Evaluation of rule-based autoverification protocols". Clinical leadership & management review: the journal of CLMA 17 (5): 268–272. ISSN 1527-3954. PMID 14531220. https://pubmed.ncbi.nlm.nih.gov/14531220. 
  2. ↑ 2.0 2.1 Jones, Jay B. (1 March 2013). "A strategic informatics approach to autoverification". Clinics in Laboratory Medicine 33 (1): 161–181. doi:10.1016/j.cll.2012.11.004. ISSN 1557-9832. PMID 23331736. https://pubmed.ncbi.nlm.nih.gov/23331736. 
  3. ↑ Pearlman, Eugene S.; Bilello, Leonard; Stauffer, Joseph; Kamarinos, Andonios; Miele, Rudolph; Wolfert, Marc S. (1 July 2002). "Implications of autoverification for the clinical laboratory". Clinical leadership & management review: the journal of CLMA 16 (4): 237–239. ISSN 1527-3954. PMID 12168427. https://pubmed.ncbi.nlm.nih.gov/12168427. 
  4. ↑ 4.0 4.1 Torke, Narayan; Boral, Leonard; Nguyen, Tracy; Perri, Angelo; Chakrin, Alan (1 December 2005). "Process improvement and operational efficiency through test result autoverification". Clinical Chemistry 51 (12): 2406–2408. doi:10.1373/clinchem.2005.054395. ISSN 0009-9147. PMID 16306113. https://pubmed.ncbi.nlm.nih.gov/16306113. 
  5. ↑ Krasowski, Matthew D.; Davis, Scott R.; Drees, Denny; Morris, Cory; Kulhavy, Jeff; Crone, Cheri; Bebber, Tami; Clark, Iwa et al. (2014). "Autoverification in a core clinical chemistry laboratory at an academic medical center". Journal of Pathology Informatics 5 (1): 13. doi:10.4103/2153-3539.129450. ISSN 2229-5089. PMC 4023033. PMID 24843824. https://pubmed.ncbi.nlm.nih.gov/24843824. 
  6. ↑ Sediq, Amany Mohy-Eldin; Abdel-Azeez, Ahmad GabAllahm Hala (1 September 2014). "Designing an autoverification system in Zagazig University Hospitals Laboratories: preliminary evaluation on thyroid function profile". Annals of Saudi Medicine 34 (5): 427–432. doi:10.5144/0256-4947.2014.427. ISSN 0975-4466. PMC 6074554. PMID 25827700. https://pubmed.ncbi.nlm.nih.gov/25827700. 
  7. ↑ Onelöv, Liselotte; Gustafsson, Elisabeth; Grönlund, Eva; Andersson, Helena; Hellberg, Gisela; Järnberg, Ingela; Schurow, Sara; Söderblom, Lisbeth et al. (1 October 2016). "Autoverification of routine coagulation assays in a multi-center laboratory". Scandinavian Journal of Clinical and Laboratory Investigation 76 (6): 500–502. doi:10.1080/00365513.2016.1200135. ISSN 1502-7686. PMID 27400327. https://pubmed.ncbi.nlm.nih.gov/27400327. 
  8. ↑ Randell, Edward W.; Short, Garry; Lee, Natasha; Beresford, Allison; Spencer, Margaret; Kennell, Marina; Moores, Zoë; Parry, David (1 June 2018). "Strategy for 90% autoverification of clinical chemistry and immunoassay test results using six sigma process improvement". Data in Brief 18: 1740–1749. doi:10.1016/j.dib.2018.04.080. ISSN 2352-3409. PMC 5998219. PMID 29904674. https://pubmed.ncbi.nlm.nih.gov/29904674. 
  9. ↑ Randell, Edward W.; Short, Garry; Lee, Natasha; Beresford, Allison; Spencer, Margaret; Kennell, Marina; Moores, Zoë; Parry, David (1 May 2018). "Autoverification process improvement by Six Sigma approach: Clinical chemistry & immunoassay". Clinical Biochemistry 55: 42–48. doi:10.1016/j.clinbiochem.2018.03.002. ISSN 1873-2933. PMID 29518383. https://pubmed.ncbi.nlm.nih.gov/29518383. 
  10. ↑ Wu, Jie; Pan, Meichen; Ouyang, Huizhen; Yang, Zhili; Zhang, Qiaoxin; Cai, Yingmu (1 December 2018). "Establishing and Evaluating Autoverification Rules with Intelligent Guidelines for Arterial Blood Gas Analysis in a Clinical Laboratory". SLAS technology 23 (6): 631–640. doi:10.1177/2472630318775311. ISSN 2472-6311. PMID 29787327. https://pubmed.ncbi.nlm.nih.gov/29787327. 
  11. ↑ Randell, Edward W.; Yenice, Sedef; Khine Wamono, Aye Aye; Orth, Matthias (1 November 2019). "Autoverification of test results in the core clinical laboratory". Clinical Biochemistry 73: 11–25. doi:10.1016/j.clinbiochem.2019.08.002. ISSN 1873-2933. PMID 31386832. https://pubmed.ncbi.nlm.nih.gov/31386832. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, spelling, and grammar. We also added PMCID and DOI when they were missing from the original reference.

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