Key Performance Indicators to Measure Improvement After Implementation of Total Laboratory Automation Abbott Accelerator a3600


The aim of the study was to estimate improvement of work efficiency in the laboratory after implementation of total laboratory automation (TLA) by Abbott Accelerator a3600 in the laboratory with measuring different key performance indicators (KPIs) before and after TLA implementation. The objective was also to recommend steps for defining KPIs in other laboratories. For evaluation of improvement 10 organizational and/or technical KPIs were defined for all phases of laboratory work and measured before (November 2013) and after (from 2015 to 2017) TLA implementation. Out of 10 defined KPIs, 9 were successfully measured and significantly improved. Waiting time for registration of samples in the LIS was significantly reduced from 16 (9–28) to 9 (6–16) minutes after TLA (P < 0.001). After TLA all tests were performed at core biochemistry analyzers which significantly reduced walking distance for sample management (for more than 800 m per worker) and number of tube touches (for almost 50%). Analyzers downtime and engagement time for analyzers maintenance was reduced for 50 h and 28 h per month, respectively. TLA eliminated manual dilution of samples with extreme results with sigma values increment from 3.4 to >6 after TLA. Although median turnaround time TAT for potassium and troponin was higher (for approximately 20 min), number of outliers with TAT >60 min expressed as sigma values were satisfying (>3). Implementation of the TLA improved the most of the processes in our laboratory with 9 out of 10 properly defined and measured KPIs. With proper planning and defining of KPIs, every laboratory could measure changes in daily workflow.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Hawker, C.D., Laboratory automation: total and subtotal. Clin. Lab. Med. 27(4):749–770, 2007.

    Article  PubMed  Google Scholar 

  2. 2.

    Zaninotto, M., and Plebani, M., The “hospital central laboratory”: automation, integration and clinical usefulness. Clin. Chem. Lab. Med. 48(7):911–917, 2010.

    CAS  PubMed  Google Scholar 

  3. 3.

    Cameron, P.A., Schull, M.J., and Cooke, M.W., A framework for measuring quality in the emergency department. Emerg. Med. J. 28:735–740, 2011.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Harvey, H.B., Hassanzadeh, E., Aran, S., Rosenthal, D.I., Thrall, J.H., and Abujudeh, H.H., Key performance indicators in radiology: you can’t manage what you can’t measure. Curr. Probl. Diagn. Radiol. 45(2):115–121, 2016.

    Article  PubMed  Google Scholar 

  5. 5.

    Bovend’Eerdt, T.J.H., Writing SMART rehabilitation goals and achieving goal attainment scaling: a practical guide. Clin. Rehabil. 23:4352–4361, 2009.

    Google Scholar 

  6. 6.

    Armbruster, D.A., Overcash, D.R., and Reyes, J., Clinical Chemistry Laboratory Automation in the 21st Century - Amat Victoria curam (Victory loves careful preparation). Clin. Biochem. Rev. 35(3):143–153, 2014.

    PubMed  PubMed Central  Google Scholar 

  7. 7. Accessed 8 Apr 2017.

  8. 8. Accessed 8 Apr 2017.

  9. 9.

    Rizk, M.M., Zaki, A., Hossam, N., and Aboul-Ela, Y., Evaluating laboratory key performance using quality indicators in Alexandria University Hospital Clinical Chemistry Laboratories. J. Egypt Publ. Health Assoc. 89(3):105–113, 2014.

    Article  Google Scholar 

  10. 10.

    Salinas, M., López-Garrigós, M., Gutiérrez, M., Lugo, J., Sirvent, J.V., and Uris, J., Achieving continuous improvement in laboratory organization through performance measurements: a seven-year experience. Clin. Chem. Lab. Med. 48(1):57–61, 2010.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Gannon, B., Jones, C., McCabe, A., O’Sullivan, R., and Wakai, A., An economic cost analysis of emergency department key performance indicators in Ireland. Eur J Emerg Med. 24(3):196–201, 2017.

    Article  PubMed  Google Scholar 

  12. 12.

    Santos, M.A., Moraes, R.M., and Passos, S.R., Performance indicators and decision making for outsourcing public health laboratory services. Rev. Saude Publica. 46(3):456–465, 2012.

    Article  PubMed  Google Scholar 

  13. 13.

    Markin, R.S., and Whalen, S.A., Laboratory automation: trajectory, technology, and tactics. Clin. Chem. 46(5):764–771, 2000.

    CAS  PubMed  Google Scholar 

  14. 14.

    Plebani, M., Errors in clinical laboratories or errors in laboratory medicine? Clin. Chem. Lab. Med. 44:750–759, 2006.

    CAS  PubMed  Google Scholar 

  15. 15.

    Simundic, A.M., and Lippi, G., Preanalytical phase – a continuous challenge for laboratory professionals. Biochem. Med. (Zagreb). 22(2):145–149, 2012.

    Article  Google Scholar 

  16. 16.

    Kopcinovic, L.M., Trifunović, J., Pavosevic, T., and Nikolac, N., Croatian survey on critical results reporting. Biochem. Med. (Zagreb). 25(2):193–202, 2015.

    Article  Google Scholar 

  17. 17.

    Favaloro, E.J., Lippi, G., and Adcock, D.M., Preanalytical and postanalytical variables: the leading causes of diagnostic error in hemostasis? Semin. Thromb. Hemost. 34(7):612–634, 2008.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Katz, C., McNicholas, K., Bounds, R., Figurelle, T., Jones, C., Farley, H., Witkin, G., McLane, M.A., and Johnson, S.R., Improving patient safety through enhanced communication between emergency department clinicians and medical laboratory staff. J. Clin. Outcomes Manag. 20(10):455–462, 2013.

    Google Scholar 

  19. 19.

    Flegar-Meštrić, Z., Perkov, S., and Radeljak, A., Standardization in laboratory medicine: Adoption of common reference intervals to the Croatian population. World J. Method. 6(1):93–100, 2016.

    Article  Google Scholar 

  20. 20.

    Cuhadar, S., Atay, A., Koseoglu, M., Dirican, A., and Hur, A., Stability studies of common biochemical analytes in serum separator tubes with or without gel barrier subjected to various storage conditions. Biochem. Med. (Zagreb). 22(2):202–214, 2012.

    CAS  Article  Google Scholar 

  21. 21.

    Lam, C.W., and Jacob, E., Implementing a laboratory automation system: experience of a large clinical laboratory. J. Lab. Autom. 17(1):16–23, 2012.

    Article  PubMed  Google Scholar 

  22. 22.

    Martin, H., Metcalfe, S., and Whichello, R., Specimen labeling errors: a retrospective study. Online J. Nurs. Inform. 19(2), 2015. Available at Accessed 22 May 2017.

  23. 23.

    Ialongo, C., Pieri, M., and Bernardini, S., Artificial neural network for total laboratory automation to improve the management of sample dilution. LAS Technol. 22(1):44–49, 2017.

    Google Scholar 

  24. 24.

    Dukic, K., Zoric, M., Starcic, J., Culjak, M., Saracevic, A., and Miler, M., How compliant are technicians with universal safety measures in medical laboratories in Croatia? Biochem. Med. 25(3):386–392, 2015.

    Article  Google Scholar 

  25. 25.

    Miler, M., and Nikolac, N., Patient safety is not compromised by excluding microscopic examination of negative urine dipstick. Ann. Clin. Biochem., 2017.

    PubMed  Google Scholar 

  26. 26.

    Sarkozi, L., Simson, E., and Ramanathan, L., The effects of total laboratory automation on the management of a clinical chemistry laboratory. Retrospective analysis of 36 years. Clin. Chim. Acta. 329(1–2):89–94, 2003.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Goswani, B., Singh, B., Chawla, R., Gupta, V.K., and Mallika, V., Turn around Time (TAT) as a Benchmark of Laboratory Performance. Indian J. Clin. Biochem. 25:376–379, 2010.

    Article  Google Scholar 

  28. 28.

    Angeletti, S., De Cesaris, M., Hart, J.G., Urbano, M., Vitali, M.A., Fragliasso, F., and Dicuonzo, G., Laboratory automation and intra-laboratory turnaround time: experience at the University Hospital Campus Bio-Medico of Rome. J. Lab. Autom. 20(6):652–658, 2015.

    Article  PubMed  Google Scholar 

  29. 29.

    Lou, A.H., Elnenaei, M.O., Sadek, I., Thompson, S., Crocker, B.D., and Nassar, B., Evaluation of the impact of a total automation system in a large core laboratory on turnaround time. Clin. Biochem. 49(16–17):1254–1258, 2016.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Fei, Y., Zeng, R., Wang, W., He, F., Zhong, K., and Wang, Z., National survey on intra-laboratory turnaround time for some most common routine and stat laboratory analyses in 479 laboratories in China. Biochem. Med. (Zagreb). 25(2):213–221, 2015.

    Article  Google Scholar 

  31. 31.

    Pati, H.P., and Singh, G., Turnaround Time (TAT): difference in concept for laboratory and clinician. Indian J. Hematol. Blood. Transfus. 30(2):81–84, 2014.

    Article  PubMed  Google Scholar 

Download references


This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information



Corresponding author

Correspondence to Marijana Miler.

Ethics declarations

Conflict of Interest

Author Marijana Miler declares that she has no conflict of interest. Author Nora Nikolac declares that she has no conflict of interest. Author Lora Dukic declares that she has no conflict of interest. Author Ana-Maria Simundic declares that she has no conflict of interest.

Ethical Approval

Not applicable.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Miler, M., Nikolac Gabaj, N., Dukic, L. et al. Key Performance Indicators to Measure Improvement After Implementation of Total Laboratory Automation Abbott Accelerator a3600. J Med Syst 42, 28 (2018).

Download citation


  • Key performance indicators (KPI)
  • Productivity
  • Total laboratory automation (TLA)
  • Quality