Startseite The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance

  • A. Shane Brown und Tony Badrick EMAIL logo
Veröffentlicht/Copyright: 24. Oktober 2022
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Clinical Chemistry and Laboratory Medicine (CCLM)
Aus der Zeitschrift Clinical Chemistry and Laboratory Medicine (CCLM) Band 61 Heft 1

Abstract

Laboratory automation in clinical laboratories has made enormous differences in patient outcomes, with a wide range of tests now available that are accurate and have a rapid turnaround. Total laboratory automation (TLA) has mechanised tube handling, sample preparation and storage in general chemistry, immunoassay, haematology, and microbiology and removed most of the tedious tasks involved in those processes. However, there are still many tasks that must be performed by humans who monitor the automation lines. We are seeing an increase in the complexity of the automated laboratory through further platform consolidation and expansion of the reach of molecular genetics into the core laboratory space. This will likely require rapid implementation of enhanced real time quality control measures and these solutions will generate a significantly greater number of failure flags. To capitalise on the benefits that an improved quality control process can deliver, it will be important to ensure that an automation process is implemented simultaneously with enhanced, real time quality control measures and auto-verification of patient samples in middleware. Therefore, it appears that the best solution may be to automate those critical decisions that still require human intervention and therefore include quality control as an integral part of total laboratory automation.

Keywords: human factors; laboratory efficiency; patient safety; total laboratory automation
Corresponding author: Tony Badrick, Royal College of Pathologists of Australasia Quality Assurance Programs, St Leonards, Sydney, NSW, Australia, E-mail:

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.

References

1. Felder, R. Obituary: Masahide Sasaki, MD, PhD (August 27, 1933–September 23, 2005). Clin Chem 2006;52:791–2. https://doi.org/10.1373/clinchem.2006.067686.Suche in Google Scholar

2. Hawker, CD. Nonanalytic laboratory automation: a quarter century of progress. Clin Chem 2017;63:1074–82. https://doi.org/10.1373/clinchem.2017.272047.Suche in Google Scholar PubMed

3. MarketsandMarkets. Lab automation market by product [Internet]; 2020. Available from: https://www.marketsandmarkets.com/Market-Reports/lab-automation-market-1158.html.Suche in Google Scholar

4. Plebani, M, Laposata, M, Lundberg, GD. The brain-to-brain loop concept for laboratory testing 40 years after its introduction. Am J Clin Pathol 2011;136:829–33. https://doi.org/10.1309/ajcpr28hwhssdnon.Suche in Google Scholar PubMed

5. Ghaferi, AA, Myers, CG, Sutcliffe, KM, Pronovost, P. The next wave of hospital innovation to make patients safer [Internet]; 2016. Available from: https://hbr.org/2016/08/the-next-wave-of-hospital-innovation-to-make-patients-safer.Suche in Google Scholar

6. López Yeste, ML, Pons Mas, AR, Guiñón Muñoz, L, Izquierdo Álvarez, S, García, FM, Blanco Font, A, et al.. Management of post-analytical processes in the clinical laboratory according to ISO 15189:2012. Considerations about the management of clinical samples, ensuring quality of post-analytical processes, and laboratory information management. Adv Lab Med 2021;2:373–80. https://doi.org/10.1515/almed-2021-0044.Suche in Google Scholar

7. Krasowski, M, Kulhavy, J, Morris, C, Nelson, D, Teul, S, Voss, D, et al.. Autoverification in a core clinical chemistry laboratory at an academic medical center. J Pathol Inf 2014;5:13. https://doi.org/10.4103/2153-3539.129450.Suche in Google Scholar PubMed PubMed Central

8. Vasikaran, S, Sikaris, K, Kilpatrick, E, French, J, Badrick, T, Osypiw, J, et al.. Assuring the quality of interpretative comments in clinical chemistry. Clin Chem Lab Med 2016;54:963–70. https://doi.org/10.1515/cclm-2016-0709.Suche in Google Scholar PubMed

9. Harron, K, Dibben, C, Boyd, J, Hjern, A, Azimaee, M, Barreto, ML, et al.. Challenges in administrative data linkage for research. Big Data Soc 2017;4:1–12. https://doi.org/10.1177/2053951717745678.Suche in Google Scholar PubMed PubMed Central

10. Wiggins, N, Albion, T, Stokes, B, Jose, M. Preparing pathology data for linkage. Int J Popul Data Sci 2020;5. https://doi.org/10.23889/ijpds.v5i5.1480.Suche in Google Scholar

11. Hallworth, MJ. The “70% claim”: what is the evidence base? Ann Clin Biochem 2011;48:487–8. https://doi.org/10.1258/acb.2011.011177.Suche in Google Scholar PubMed

12. Hallworth, MJ. That ‘70%’ claim again …. Ann Clin Biochem 2018;55:517–8. https://doi.org/10.1177/0004563218773778.Suche in Google Scholar PubMed

13. Wilson, S, Steele, S, Adeli, K. Innovative technological advancements in laboratory medicine: predicting the lab of the future. Biotechnol Biotechnol Equip 2022;36:S5–17. https://doi.org/10.1080/13102818.2021.2011413.Suche in Google Scholar

14. Cembrowski, GS. Thoughts on quality-control systems: a laboratorian’s perspective. Clin Chem 1997;892:886–92. https://doi.org/10.1093/clinchem/43.5.886.Suche in Google Scholar

15. Westgard, J, Barry, P, Hunt, M. A multi-rule Shewart Chart for quality control in clinical chemistry. Clin Chem 1981;27:493–501. https://doi.org/10.1093/clinchem/27.3.493.Suche in Google Scholar

16. Steindel, SJ, Tetrault, G. Quality control practices for calcium, cholesterol, digoxin, and hemoglobin. Arch Pathol Lab Med 1998;122:401–8.Suche in Google Scholar

17. Howanitz, PJ, Tetrault, GA, Steindel, SJ. Clinical laboratory quality control: a costly process now out of control. Clin Chim Acta 1997;260:163–74. https://doi.org/10.1016/s0009-8981(96)06494-7.Suche in Google Scholar PubMed

18. Loh, TP, Cervinski, MA, Katayev, A, Bietenbeck, A, van Rossum, H, Badrick, T. Recommendations for laboratory informatics specifications needed for the application of patient-based real time quality control. Clin Chim Acta 2019;495:625–9. https://doi.org/10.1016/j.cca.2019.06.009.Suche in Google Scholar PubMed

19. van Rossum, HH. Moving average quality control: principles, practical application and future perspectives. Clin Chem Lab Med 2019;57:773–82. https://doi.org/10.1515/cclm-2018-0795.Suche in Google Scholar PubMed

20. van Rossum, HH. When internal quality control is insufficient or inefficient: consider patient-based real-time quality control!. Ann Clin Biochem 2020;57:198–201. https://doi.org/10.1177/0004563220912273.Suche in Google Scholar PubMed

21. Badrick, T, Bietenbeck, A, Cervinski, MA, Katayev, A, van Rossum, HH. Patient-based real-time quality control: review and recommendations. Clin Biochem 2019;65:962–71.10.1373/clinchem.2019.305482Suche in Google Scholar PubMed

22. Badrick, T, Cervinski, M, Loh, TP. A primer on patient-based quality control techniques. Clin Biochem 2019;64:1–5. https://doi.org/10.1016/j.clinbiochem.2018.12.004.Suche in Google Scholar PubMed

23. Badrick, T, Bietenbeck, A, Katayev, A, van Rossum, HH, Loh, TP, Cervinski, MA. Implementation of patient-based real-time quality control. Crit Rev Clin Lab Sci 2020;57:532–47. https://doi.org/10.1080/10408363.2020.1765731.Suche in Google Scholar PubMed

24. Fleming, J, Katayev, A. Changing the paradigm of laboratory quality control through implementation of real-time test results monitoring. Clin Biochem 2015;48:508–13. https://doi.org/10.1016/j.clinbiochem.2014.12.016.Suche in Google Scholar PubMed

25. van Rossum, HH, Bietenbeck, A, Cervinski, MA, Katayev, A, Loh, TP, Badrick, TC. Benefits, limitations and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice. Clin Chem Lab Med 2021;59:1213–20. https://doi.org/10.1515/cclm-2021-0072.Suche in Google Scholar PubMed

26. Katayev, A, Fleming, JK. Past, present, and future of laboratory quality control: patient- based real-time quality control or when getting more quality at less cost is not wishful thinking. J Lab Precis Med 2020;5:28. https://doi.org/10.21037/jlpm-2019-qc-03.Suche in Google Scholar

27. van Rossum, H, Kemperman, H. Moving average for continuous quality control: time to move to implementation in daily practice? Clin Chem 2017;63:1040–1. https://doi.org/10.1373/clinchem.2016.269258.Suche in Google Scholar PubMed

28. He, Y, Gu, D, Kong, X, Feng, Z, Lin, W, Cai, Y. A study of the moving rate of positive results for use in a patient-based real-time quality control program on a procalcitonin point-of-care testing analyzer. J Clin Lab Anal 2022;36:e24320.10.1002/jcla.24320Suche in Google Scholar PubMed PubMed Central

29. Lukić, V, Ignjatović, S. Optimizing moving average control procedures for small-volume laboratories: can it be done? Biochem Med 2019;29:1–13. https://doi.org/10.11613/bm.2019.030710.Suche in Google Scholar PubMed PubMed Central

30. Koerbin, G, Liu, J, Eigenstetter, A, Tan, C, Badrick, T, Loh, TP. Missed detection of significant positive and negative shifts in gentamicin assay: Implications for routine laboratory quality practices. Biochem Med 2018;28. https://doi.org/10.11613/bm.2018.010705.Suche in Google Scholar PubMed PubMed Central

31. Fleming, JK, Katayev, A. Changing the paradigm of laboratory quality control through implementation of real-time test results monitoring: for patients by patients. Clin Biochem [Internet] 2015;48:508–13. https://doi.org/10.1016/j.clinbiochem.2014.12.016.Suche in Google Scholar

32. Duan, X, Wang, B, Zhu, J, Shao, W, Wang, H, Shen, J, et al.. Assessment of patient-based real-time quality control algorithm performance on different types of analytical error. Clinica Chimica Acta [Internet] 2020;511:329–35. https://doi.org/10.1016/j.cca.2020.10.006.Suche in Google Scholar PubMed

33. van Rossum, HH. Optimization and validation of limit check error-detection performance using a laboratory-specific data-simulation approach: a prerequisite for an evidence-based practice. J Appl Lab Med 2022;7:467–79. https://doi.org/10.1093/jalm/jfab144.Suche in Google Scholar PubMed

34. Lim, CY, Badrick, T, Loh, TP. Patient-based quality control for glucometers: using the moving sum of positive patient results and moving average. Biochem Med 2020;30:1–11.10.11613/BM.2020.020709Suche in Google Scholar

35. Loh, TP, Badrick, T. Using next generation electronic medical records for laboratory quality monitoring. J Lab Precis Med 2017;2:61. https://doi.org/10.21037/jlpm.2017.08.06.Suche in Google Scholar

36. Jones, G. Average of delta: a new quality control tool for clinical laboratories. Ann Clin Biochem 2016;53:133–40. https://doi.org/10.1177/0004563215581400.Suche in Google Scholar PubMed

37. Rosenbaum, MW, Flood, JG, Melanson, SEF, Baumann, NA, Marzinke, MA, Rai, AJ, et al.. Quality control practices for chemistry and immunochemistry in a cohort of 21 large academic medical centers. Am J Clin Pathol 2018;150:96–104. https://doi.org/10.1093/ajcp/aqy033.Suche in Google Scholar PubMed

38. Westgard, JO, Westgard, SA. The quality of laboratory testing today. Am J Clin Pathol 2006;125:343–54. https://doi.org/10.1309/v50h4frvvwx12c79.Suche in Google Scholar

39. Kohn, L, Corrigan, J, Donaldson, M. To err is human: building a safer health system. Washington: National Academies Press; 2000.Suche in Google Scholar

40. DeMott, DL. Human reliability and the cost of doing business [Internet]; 2014. Available from: https://ntrs.nasa.gov/api/citations/20140008715/downloads/20140008715.pdf [Accessed 7 Jun 2022].Suche in Google Scholar

41. Desiere, F, Kowalik, K, Fassbind, C, Assaad, RS, Füzéry, AK, Gruson, D, et al.. Digital diagnostics and mobile health in laboratory medicine: an International Federation of Clinical Chemistry and Laboratory Medicine Survey on current practice and future perspectives. J Appl Lab Med 2021;6:969–79. https://doi.org/10.1093/jalm/jfab026.Suche in Google Scholar PubMed

42. Plebani, M. Clinical laboratory: bigger is not always better. Diagnosis 2018;5:41–6. https://doi.org/10.1515/dx-2018-0019.Suche in Google Scholar PubMed

43. Sciacovelli, L, Panteghini, M, Lippi, G, Sumarac, Z, Cadamuro, J, Galoro, CADO, et al.. Defining a roadmap for harmonizing quality indicators in Laboratory Medicine: a consensus statement on behalf of the IFCC Working Group “laboratory Error and Patient Safety” and EFLM Task and Finish Group “performance specifications for the extra-analytical phases”. Clin Chem Lab Med 2017;55:1478–88. https://doi.org/10.1515/cclm-2017-0412.Suche in Google Scholar PubMed

Received: 2022-04-26
Accepted: 2022-09-26
Published Online: 2022-10-24
Published in Print: 2023-01-27

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. A new milestone on the road to global standardization of apolipoprotein measurements
  4. Review
  5. Saliva – a new opportunity for fluid biopsy
  6. Opinion Papers
  7. Emerging technology: a definition for laboratory medicine
  8. The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance
  9. EFLM Paper
  10. The new, race-free, Chronic Kidney Disease Epidemiology Consortium (CKD-EPI) equation to estimate glomerular filtration rate: is it applicable in Europe? A position statement by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)
  11. Guidelines and Recommendations
  12. Overcoming challenges regarding reference materials and regulations that influence global standardization of medical laboratory testing results
  13. General Clinical Chemistry and Laboratory Medicine
  14. Quantitative protein mass-spectrometry requires a standardized pre-analytical phase
  15. Report from the HarmoSter study: inter-laboratory comparison of LC-MS/MS measurements of corticosterone, 11-deoxycortisol and cortisone
  16. The influence of proteoforms: assessing the accuracy of total vitamin D-binding protein quantification by proteolysis and LC-MS/MS
  17. Total serum vitamin B12 (cobalamin) LC-MS/MS assay as an arbiter of clinically discordant immunoassay results
  18. The preanalytical process in the emergency department, a European survey
  19. Proenkephalin A as a marker for glomerular filtration rate in critically ill children: validation against gold standard iohexol GFR measurements
  20. Umbilical cord blood gases: probability of arterial or venous source in acidemia
  21. Reference Values and Biological Variations
  22. Pediatric reference interval verification for 17 specialized immunoassays and cancer markers on the Abbott Alinity i system in the CALIPER cohort of healthy children and adolescents
  23. Hematology and Coagulation
  24. Performance of digital morphology analyzer CellaVision DC-1
  25. Cancer Diagnostics
  26. Managing the impact of inter-method bias of prostate specific antigen assays on biopsy referral: the key to move towards precision health in prostate cancer management
  27. Cardiovascular Diseases
  28. Quantitation of cardiac troponin I in cancer patients treated with immune checkpoint inhibitors: a case-control study
  29. Infectious Diseases
  30. A novel scoring system combining Modified Early Warning Score with biomarkers of monocyte distribution width, white blood cell counts, and neutrophil-to-lymphocyte ratio to improve early sepsis prediction in older adults
  31. Technical and health governance aspects of the External Quality Assessment Scheme for the SARS-CoV-2 molecular tests: institutional experience performed in all clinical laboratories of a Regional Health Service
  32. Acknowledgment
  33. Acknowledgment
  34. Letters to the Editor
  35. About the estimation of albuminuria based on proteinuria results
  36. Response to “About the estimation of albuminuria based on proteinuria results”
  37. Reply to Abildgaard et al.: lot variation and inter-device differences contribute to poor analytical performance of the DCA vantage™ HbA1c POCT instrument in a true clinical setting
  38. Reply to letter from Mayfield et al. regarding “Lot variation and inter-device differences contribute to poor analytical performance of the DCA Vantage™ HbA1c POCT instrument in a true clinical setting”
  39. High-sensitive cardiac troponin T: are turbulences coming?
  40. Analytical performance evaluation of bioactive adrenomedullin on point-of-care platform
  41. Increased PD-L1 surface expression on peripheral blood granulocytes and monocytes after vaccination with SARS-CoV2 mRNA or vector vaccine
  42. Neopterin level can be measured by intraocular liquid biopsy
  43. The stability of pleural fluid pH under slushed ice and room temperature conditions
https://doi.org/10.1515/cclm-2022-0409
Schlagwörter für diesen Artikel
human factors; laboratory efficiency; patient safety; total laboratory automation

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. A new milestone on the road to global standardization of apolipoprotein measurements
  4. Review
  5. Saliva – a new opportunity for fluid biopsy
  6. Opinion Papers
  7. Emerging technology: a definition for laboratory medicine
  8. The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance
  9. EFLM Paper
  10. The new, race-free, Chronic Kidney Disease Epidemiology Consortium (CKD-EPI) equation to estimate glomerular filtration rate: is it applicable in Europe? A position statement by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)
  11. Guidelines and Recommendations
  12. Overcoming challenges regarding reference materials and regulations that influence global standardization of medical laboratory testing results
  13. General Clinical Chemistry and Laboratory Medicine
  14. Quantitative protein mass-spectrometry requires a standardized pre-analytical phase
  15. Report from the HarmoSter study: inter-laboratory comparison of LC-MS/MS measurements of corticosterone, 11-deoxycortisol and cortisone
  16. The influence of proteoforms: assessing the accuracy of total vitamin D-binding protein quantification by proteolysis and LC-MS/MS
  17. Total serum vitamin B12 (cobalamin) LC-MS/MS assay as an arbiter of clinically discordant immunoassay results
  18. The preanalytical process in the emergency department, a European survey
  19. Proenkephalin A as a marker for glomerular filtration rate in critically ill children: validation against gold standard iohexol GFR measurements
  20. Umbilical cord blood gases: probability of arterial or venous source in acidemia
  21. Reference Values and Biological Variations
  22. Pediatric reference interval verification for 17 specialized immunoassays and cancer markers on the Abbott Alinity i system in the CALIPER cohort of healthy children and adolescents
  23. Hematology and Coagulation
  24. Performance of digital morphology analyzer CellaVision DC-1
  25. Cancer Diagnostics
  26. Managing the impact of inter-method bias of prostate specific antigen assays on biopsy referral: the key to move towards precision health in prostate cancer management
  27. Cardiovascular Diseases
  28. Quantitation of cardiac troponin I in cancer patients treated with immune checkpoint inhibitors: a case-control study
  29. Infectious Diseases
  30. A novel scoring system combining Modified Early Warning Score with biomarkers of monocyte distribution width, white blood cell counts, and neutrophil-to-lymphocyte ratio to improve early sepsis prediction in older adults
  31. Technical and health governance aspects of the External Quality Assessment Scheme for the SARS-CoV-2 molecular tests: institutional experience performed in all clinical laboratories of a Regional Health Service
  32. Acknowledgment
  33. Acknowledgment
  34. Letters to the Editor
  35. About the estimation of albuminuria based on proteinuria results
  36. Response to “About the estimation of albuminuria based on proteinuria results”
  37. Reply to Abildgaard et al.: lot variation and inter-device differences contribute to poor analytical performance of the DCA vantage™ HbA1c POCT instrument in a true clinical setting
  38. Reply to letter from Mayfield et al. regarding “Lot variation and inter-device differences contribute to poor analytical performance of the DCA Vantage™ HbA1c POCT instrument in a true clinical setting”
  39. High-sensitive cardiac troponin T: are turbulences coming?
  40. Analytical performance evaluation of bioactive adrenomedullin on point-of-care platform
  41. Increased PD-L1 surface expression on peripheral blood granulocytes and monocytes after vaccination with SARS-CoV2 mRNA or vector vaccine
  42. Neopterin level can be measured by intraocular liquid biopsy
  43. The stability of pleural fluid pH under slushed ice and room temperature conditions
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 1.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cclm-2022-0409/html
Button zum nach oben scrollen