Home Comprehensive assessment of medical laboratory performance: a 4D model of quality, economics, velocity, and productivity indicators
Article
Licensed
Unlicensed Requires Authentication

Comprehensive assessment of medical laboratory performance: a 4D model of quality, economics, velocity, and productivity indicators

  • Pavel S. Zubanov EMAIL logo , Pavel P. Tregub , Arkady S. Goldberg , Mikhail A. Godkov and Vasily G. Akimkin
Published/Copyright: May 5, 2025
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 63 Issue 10

Abstract

Laboratory diagnostics play a crucial role in modern medicine and healthcare economics. The effective management of a medical laboratory is based on reliable assessment of indicators characterizing quality of testing, productivity, velocity (speed) and cost-effectiveness. The usual concepts of laboratory management focus on one or two groups of these indicators and exclude a comprehensive assessment of the effectiveness of a medical laboratory. Various guidelines and concepts (ISO, Lean, Six Sigma, etc.) often provide similar approaches but use different terms. This review discusses common options for performance indicators in medical laboratories, as well as practical experience in using these indicators to assess the overall effectiveness of the laboratory and improve medical care for patients. All indicators were divided into four broad groups: quality, economy, velocity, and productivity. Based on these four groups, we describe the new” four-dimensional model” for assessment of medical laboratory performance based on different combinations of indicator groups for different types of laboratories.

Keywords: IVD management; medical laboratories; efficiency indicators; quality indicators; healthcare
Corresponding author: Pavel S. Zubanov, Central Research Institute of Epidemiology, Moscow, Russia, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

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

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

Appendix 1

Examples of medical laboratory efficiency indicators

Main groups of indicators Examples of indicators for each group
Velocity of testing

References: [19], 34], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96]

  1. Full TAT – the average time between ordering a test and reporting the results;

  1. DAT (diagnostic analysis time) – the average time between sample collection and reporting the results;

  1. Intra-laboratory TAT – the average time between sample receipt by the laboratory and reporting the results;

  1. Number of reports delivered outside the specified TAT (percentage relative to the total number reports).
Quality and accuracy of tests

References: [7], 8], [21], [22], [23], [24], [25, [30], [31], [32], [33], [34, 36]

  1. Preanalytical phase – misidentification errors (percentage relative to the total number of samples);

  1. Preanalytical phase – inappropriate test requests and test transcription errors (percentages relative to the total number of requests);

  1. Preanalytical phase – incorrect sample type (percentage relative to the total number of samples);

  1. Preanalytical phase – unsuitable samples for transportation and storage problems (percentages relative to the total number of samples);

  1. Preanalytical phase – samples hemolyzed and clotted (percentages relative to the total number of samples);

  1. Preanalytical phase – inappropriate sample collection time (percentage relative to the total number of samples);

  1. Intra-analytical phase – tests with inappropriate ICQ and EQA-PT performances (percentage relative to the total number of tests on the menu);

  1. Intra-analytical phase – tests uncovered by an EQA-PT control (percentage relative to the total number of tests on the menu);

  1. Intra-analytical phase – data transcription errors (percentage relative to the total number of results);

  1. Postanalytical phase – incorrect laboratory reports (percentage relative to the total number of reports);

  1. Percentage of the total points awarded in the customer global satisfaction survey (patients and clinicians);

  1. Number of laboratory information system downtime episodes per year;

  1. Number of inaccurate results released (percentage relative to the total number of results released).
Number of performed tests and productivity potential

References: [69], 70], 79], 80], 115]

  1. Number of tests performed per average workday;

  1. Number of tests performed per year;

  1. Number of samples received per average workday;

  1. Number of samples received per year;

  1. Percentage of the number of tests performed per year relative to the maximum number of tests the lab can process per year;

  1. Percentage of the number of samples received per year relative to the maximum number of samples the lab can process per year.

  1. Annual number of tests performed per square meter of workspace;
Economic efficiency

References: [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83]

  1. EBITDA – earnings before interest, taxes, depreciation. And amortization (for cases when financial accounting for the laboratory can be carried out separately);

  1. OIBDA – operating income before depreciation and amortization (for cases when financial accounting for laboratory can be carried out separately);

  1. IRR – internal rate of return (for cases when financial accounting for laboratory can be carried out separately);

  1. Annual number of tests performed per thousand square meters of area;

  1. Annual number of tests performed per one employee;

  1. Average cost of reagents per one test performed.

  1. The Table shows examples of the most common indicators. However, it is possible to find more indicators for each group and to make more detailed descriptions for some indicators. Some indicators can be calculated in various ways: for the entire test flow, for a specific laboratory department (e.g., hematology or microbiology), or for a specific test or for a specific source of samples (e.g., inpatients or outpatients).

References

1. Plebani, M. The CCLM contribution to improvements in quality and patient safety. Clin Chem Lab Med 2013;51:39–46. https://doi.org/10.1515/cclm-2012-0094.Search in Google Scholar PubMed

2. Yu, S, Jeon, BR, Liu, C, Kim, D, Park, HI, Park, HD, et al.. Laboratory preparation for digital medicine in healthcare 4.0: an investigation into the awareness and applications of big data and artificial intelligence. Ann Lab Med 2024;44:562–71. https://doi.org/10.3343/alm.2024.0111.Search in Google Scholar PubMed PubMed Central

3. Klatt, EC. Cognitive factors impacting patient understanding of laboratory test information. J Pathol Inf 2023;15:100349. https://doi.org/10.1016/j.jpi.2023.100349.Search in Google Scholar PubMed PubMed Central

4. Leonard, A, Boran, G, Kane, A, Cornes, M. Monitoring and capturing patient identification errors in laboratory medicine. Ann Clin Biochem 2020;57:266–70. https://doi.org/10.1177/0004563220903076.Search in Google Scholar PubMed

5. Siham, K, Ikhlas, M, Raihane, B, Zineb, ASA, Hicham, Y, Mustapha, AA, et al.. Pre-analytical phase in hemostasis: the main anomalies and means to correct them. Am J Lab Med 2019;4:105–10. https://doi.org/10.11648/j.ajlm.20190406.14.Search in Google Scholar

6. Kang, F, Li, W, Xia, X, Shan, Z. Three years’ experience of quality monitoring program on pre-analytical errors in China. J Clin Lab Anal 2021;35:23699. https://doi.org/10.1002/jcla.23699.Search in Google Scholar PubMed PubMed Central

7. Plebani, M, Astion, ML, Barth, JH, Chen, W, de Oliveira Galoro, CA, Escuer, MI, et al.. Harmonization of quality indicators in laboratory medicine. A preliminary consensus. Clin Chem Lab Med 2014;52:951–8. https://doi.org/10.1515/cclm-2014-0142.Search in Google Scholar PubMed

8. Zaninotto, M, Graziani, MS, Plebani, M. The harmonization issue in laboratory medicine: the commitment of CCLM. Clin Chem Lab Med 2022;61:721–31. https://doi.org/10.1515/cclm-2022-1111.Search in Google Scholar PubMed

9. Plebani, M. Quality in laboratory medicine and the Journal: walking together. Clin Chem Lab Med 2022;61:713–20. https://doi.org/10.1515/cclm-2022-0755.Search in Google Scholar PubMed

10. Nichols, JH. Verification of method performance for clinical laboratories. Adv Clin Chem 2009;47:121–37. https://doi.org/10.1016/s0065-2423(09)47005-7.Search in Google Scholar PubMed

11. Killeen, AA, Long, T, Souers, R, Styer, P, Ventura, CB, Klee, GG. Verifying performance characteristics of quantitative analytical systems: calibration verification, linearity, and analytical measurement range. Arch Pathol Lab Med 2014;138:1173–81. https://doi.org/10.5858/arpa.2013-0051-cp.Search in Google Scholar PubMed

12. Abdel, GMT, El-Masry, MI. Verification of quantitative analytical methods in medical laboratories. J Med Biochem 2021;40:225–36. https://doi.org/10.5937/jomb0-24764.Search in Google Scholar PubMed PubMed Central

13. Clinical Laboratory Services Market Size. Share & industry analysis, by test type (Clinical Chemistry, Hematology, Genetic Testing, Microbiology & Cytology, and Others). In: By age (Pediatric and Adult), by source of payment (Public, Private, and Out of Pocket), by service provider (Hospital-based Laboratories, Stand-alone Laboratories, Clinic-based Laboratories, and Others), and regional forecast; 2024:2024–32 pp. Available from: https://www.fortunebusinessinsights.com/industry-reports/clinical-laboratory-services-market-100725.Search in Google Scholar

14. Ardon, O, Asa, SL, Lloyd, MC, Lujan, G, Parwani, A, Santa-Rosario, JC, et al.. Understanding the financial aspects of digital pathology: a dynamic customizable return on investment calculator for informed decision-making. J Pathol Inf 2024;15:100376. https://doi.org/10.1016/j.jpi.2024.100376.Search in Google Scholar PubMed PubMed Central

15. Devis, L, Catry, E, Honore, PM, Mansour, A, Lippi, G, Mullier, F, et al.. Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review. Ann Intensive Care 2024;14:9. https://doi.org/10.1186/s13613-024-01244-y.Search in Google Scholar PubMed PubMed Central

16. White, BA, Baron, JM, Dighe, AS, Camargo, CAJr, Brown, DF. Applying Lean methodologies reduces ED laboratory turnaround times. Am J Emerg Med 2015;33:1572–6. https://doi.org/10.1016/j.ajem.2015.06.013.Search in Google Scholar PubMed PubMed Central

17. Letelier, P, Guzmán, N, Medina, G, Calcumil, L, Huencho, P, Mora, J, et al.. Workflow optimization in a clinical laboratory using Lean management principles in the pre-analytical phase. J Med Biochem 2021;40:26–32. https://doi.org/10.5937/jomb0-26055.Search in Google Scholar PubMed PubMed Central

18. Zhao, X, Qin, X, Miao, Y. Reducing turnaround time for routine outpatient biochemical tests through Lean Six Sigma: a case study in China. J Eval Clin Pract 2024;31. https://doi.org/10.1111/jep.14116.Search in Google Scholar PubMed

19. Gebreyes, M, Sisay, A, Tegen, D, Asnake, A, Wolde, M. Evaluation of laboratory performance, associated factors and staff awareness towards achieving turnaround time in tertiary hospitals, Ethiopia. Ethiop J Health Sci 2020;30:767–76. https://doi.org/10.4314/ejhs.v30i5.17.Search in Google Scholar PubMed PubMed Central

20. Lubin, IM, Astles, JR, Shahangian, S, Madison, B, Parry, R, Schmidt, RL, et al.. Bringing the clinical laboratory into the strategy to advance diagnostic excellence. Diagnosis (Berl) 2021;8:281–94. https://doi.org/10.1515/dx-2020-0119.Search in Google Scholar PubMed PubMed Central

21. Plebani, M. The detection and prevention of errors in laboratory medicine. Ann Clin Biochem 2010;47:101–10. https://doi.org/10.1258/acb.2009.009222.Search in Google Scholar PubMed

22. Mainz, J. Quality indicators: essential for quality improvement. Int J Qual Health Care 2004;16:S1–2. https://doi.org/10.1093/intqhc/mzh036.Search in Google Scholar

23. Shahangian, S, Snyder, SR. Laboratory medicine quality indicators: a review of the literature. Am J Clin Pathol 2009;131:418–31. https://doi.org/10.1309/ajcpjf8ji4zldque.Search in Google Scholar

24. Mrazek, C, Lippi, G, Keppel, MH, Felder, TK, Oberkofler, H, Haschke-Becher, E, et al.. Errors within the total laboratory testing process, from test selection to medical decision-making – a review of causes, consequences, surveillance and solutions. Biochem Med (Zagreb) 2020;30:020502. https://doi.org/10.11613/BM.2020.020502.Search in Google Scholar PubMed PubMed Central

25. Plebani, M, Chiozza, ML, Sciacovelli, L. Towards harmonization of quality indicators in laboratory medicine. Clin Chem Lab Med 2013;51:187–95. https://doi.org/10.1515/cclm-2012-0582.Search in Google Scholar PubMed

26. Sciacovelli, L, Panteghini, M, Lippi, G, Sumarac, Z, Cadamuro, J, Galoro, CAO, 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.Search in Google Scholar PubMed

27. Sciacovelli, L, Lippi, G, Sumarac, Z, West, J, Ivanov, A, Plebani, M, Working Group “Laboratory Errors and Patient Safety” of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), et al.. Quality indicators in laboratory medicine: the status of the progress of IFCC working group “laboratory errors and patient safety” project. Clin Chem Lab Med 2017;55:348–57. https://doi.org/10.1515/cclm-2016-0929.Search in Google Scholar PubMed

28. Cobbaert, C, Smit, N, Gillery, P. Metrological traceability and harmonization of medical tests: a quantum leap forward is needed to keep pace with globalization and stringent IVD-regulations in the 21st century. Clin Chem Lab Med 2018;56:1598–602. https://doi.org/10.1515/cclm-2018-0343.Search in Google Scholar PubMed

29. Thelen, MHM, Huisman, W. Harmonization of accreditation to ISO15189. Clin Chem Lab Med 2018;56:1637–43. https://doi.org/10.1515/cclm-2017-0820.Search in Google Scholar PubMed

30. Ricós, C, García-Victoria, M, de la Fuente, B. Quality indicators and specifications for the extra-analytical phases in clinical laboratory management. Clin Chem Lab Med 2004;42:578–82. https://doi.org/10.1515/cclm.2004.100.Search in Google Scholar PubMed

31. Aita, A, Sciacovelli, L, Plebani, M. Extra-analytical quality indicators - where to now? Clin Chem Lab Med 2018;57:127–33. https://doi.org/10.1515/cclm-2017-0964.Search in Google Scholar PubMed

32. 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.Search in Google Scholar PubMed

33. Sciacovelli, L, Plebani, M. The IFCC Working Group on laboratory errors and patient safety. Clin Chim Acta 2009;404:79–85. https://doi.org/10.1016/j.cca.2009.03.025.Search in Google Scholar PubMed

34. Plebani, M, Sciacovelli, L, Aita, A. Quality indicators for the total testing process. Clin Lab Med 2017;37:187–205. https://doi.org/10.1016/j.cll.2016.09.015.Search in Google Scholar PubMed

35. Braga, F, Panteghini, M. The utility of measurement uncertainty in medical laboratories. Clin Chem Lab Med 2020;58:1407–13. https://doi.org/10.1515/cclm-2019-1336.Search in Google Scholar PubMed

36. Sciacovelli, L, Padoan, A, Aita, A, Basso, D, Plebani, M. Quality indicators in laboratory medicine: state-of-the-art, quality specifications and future strategies. Clin Chem Lab Med 2023;61:688–95. https://doi.org/10.1515/cclm-2022-1143.Search in Google Scholar PubMed

37. Westgard, S, Bayat, H, Westgard, JO. Analytical Sigma metrics: a review of Six Sigma implementation tools for medical laboratories. Biochem Med (Zagreb) 2018;28:020502. https://doi.org/10.11613/bm.2018.020502.Search in Google Scholar PubMed PubMed Central

38. Zhou, B, Wu1, Y, He, H, Li, C, Tan, L, Cao, Y. Practical application of Six Sigma management in analytical biochemistry processes in clinical settings. J Clin Lab Anal 2020;34:23126. https://doi.org/10.1002/jcla.23126.Search in Google Scholar PubMed PubMed Central

39. Liu, Q, Bian, G, Chen, X, Han, J, Chen, J, Wang, M, et al.. Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk-based statistical quality control strategy for these assays: a multicenter study. J Clin Lab Anal 2021;35:21u24059. https://doi.org/10.1002/jcla.24059.Search in Google Scholar PubMed PubMed Central

40. Plebani, M, Padoan, A, Sciacovelli, L. Measurement uncertainty: light in the shadows. Clin Chem Lab Med 2020;58:1381–3. https://doi.org/10.1515/cclm-2020-0134.Search in Google Scholar PubMed

41. Don-Wauchope, AC, Santaguida, PL. Grading evidence for laboratory test studies beyond diagnostic accuracy: application to prognostic testing. EJIFCC 2015;26:168–82.Search in Google Scholar

42. Roslan, NA, Abu, MN, Ismail, FR. Diagnostic accuracy and agreement between inter- and intra-observers in the absence of clinical history for liquid-based preparation of gynecology specimens. J Cytol 2022;39:110–15. https://doi.org/10.4103/joc.joc_68_22.Search in Google Scholar PubMed PubMed Central

43. Thakur, V, Akerele, OA, Randell, E. Lean and Six Sigma as continuous quality improvement frameworks in the clinical diagnostic laboratory. Crit Rev Clin Lab Sci 2023;60:63–81. https://doi.org/10.1080/10408363.2022.2106544.Search in Google Scholar PubMed

44. Villa, D. Automation, lean, six sigma: synergies for improving laboratory efficiency. J Med Biochem 2010;29:339–48. https://doi.org/10.2478/v10011-010-0038-3.Search in Google Scholar

45. Chen, A, Anderson, J, Frater, JL. Preanalytical errors in a satellite stat laboratory: a Six Sigma analysis of seven years’ data. Clin Chim Acta 2021;523:26–30. https://doi.org/10.1016/j.cca.2021.08.031.Search in Google Scholar PubMed

46. Coskun, A, Unsal, I, Serteser, M, Inal, T. Six sigma as a quality management tool: evaluation of performance in laboratory medicine. Rijeka: Sciyo; 2010.10.5772/9928Search in Google Scholar

47. Mao, X, Shao, J, Zhang, B, Wang, Y. Evaluating analytical quality in clinical biochemistry laboratory using Six Sigma. Biochem Med (Zagreb) 2018;28:020904. https://doi.org/10.11613/bm.2018.020904.Search in Google Scholar

48. Scherrer, F, Bouilloux, JP, Calendini, O, Chamard, D, Cornu, F. Interest and limits of the six sigma methodology in medical laboratory. Ann Biol Clin 2017;75:107–13. https://doi.org/10.1684/abc.2016.1216.Search in Google Scholar PubMed

49. Agarwal, R, Chhillar, N, Tripathi, CB. Study of variables affecting critical value notification in a laboratory catering to tertiary care hospital. Indian J Clin Biochem 2015;30:89–93. https://doi.org/10.1007/s12291-013-0409-x.Search in Google Scholar PubMed PubMed Central

50. Xia, Y, Wang, X, Yan, C, Wu, J, Xue, H, Li, M, et al.. Risk assessment of the total testing process based on quality indicators with the Sigma metrics. Clin Chem Lab Med 2020;58:1223–31. https://doi.org/10.1515/cclm-2019-1190.Search in Google Scholar PubMed

51. Jha, PK, Agarwal, R. Quality tools and strategy for critical alerts process improvements to ensure patient safety. J Lab Physicians 2022;14:471–8. https://doi.org/10.1055/s-0042-1747677.Search in Google Scholar PubMed PubMed Central

52. Alharbi, AB, Berrou, I, Umaru, N, Al Hamid, A, Shebl, NA. Factors influencing the implementation of medicine risk communications by healthcare professionals in clinical practice: a systematic review. Res Soc Adm Pharm 2023;19:28–56. https://doi.org/10.1016/j.sapharm.2022.07.003.Search in Google Scholar PubMed

53. Hijjeh, M, Al Shaikh, L, Alinier, G, Selwood, D, Malmstrom, F, Hassan, IF. Critical care network in the state of Qatar. Qatar Med J 2019;2.10.5339/qmj.2019.qccc.2Search in Google Scholar PubMed PubMed Central

54. Brun, M, Füzéry, AK, Henschke, B, Rozak, K, Venner, AA. Identifying sources of error and selecting quality indicators for point of care testing. Pract Lab Med 2021;25:00216. https://doi.org/10.1016/j.plabm.2021.e00216.Search in Google Scholar PubMed PubMed Central

55. Chokkalla, AK, Recio, BD, Devaraj, S. Best practices for effective management of point of care testing. EJIFCC 2023;34:245–9.Search in Google Scholar

56. Schmidt, RL, Ashwood, ER. Laboratory medicine and value-based health care. Am J Clin Pathol 2015;144:357–8. https://doi.org/10.1309/ajcpwtdajgmyln51.Search in Google Scholar

57. Goldberg, AS, Aleksandrova, OY, Zudin, AB, Shegai, MM. The system of criteria and indicators of accessibility of laboratory diagnostic in the subject of The Russian Federation. Probl Sotsialnoi Gig Zdravookhranenniiai Istor Med 2024;32:11–15. https://doi.org/10.32687/0869-866x-2024-32-1-11-15.Search in Google Scholar

58. Lippi, G, Mattiuzzi, C, Bovo, C. Are we getting better at the preanalytical phase or just better at measuring it? J. Lab Precis Med 2018;3. https://doi.org/10.21037/jlpm.2018.01.03.Search in Google Scholar

59. Hammerling, JA. A review of medical errors in laboratory diagnostics and where we are today. Lab Med 2012;43:41–4. https://doi.org/10.1309/lm6er9wjr1ihqauy.Search in Google Scholar

60. Lippi, G, Cadamuro, J. Novel opportunities for improving the quality of preanalytical phase. A glimpse to the future? J Med Biochem 2017;36:293–300. https://doi.org/10.1515/jomb-2017-0029.Search in Google Scholar PubMed PubMed Central

61. Eren, F, Tuncay, ME, Oguz, EF, Neselioglu, S, Erel, O. The response of total testing process in clinical laboratory medicine to COVID-19 pandemic. Biochem Med (Zagreb) 2021;31:020713. https://doi.org/10.11613/bm.2021.020713.Search in Google Scholar PubMed PubMed Central

62. Ahmed, S, Jahan, F, Naeem Effendi, MU, Ghani, F. Impact of COVID-19 on the pre and post analytical clinical laboratory testing processes – a performance evaluation study using six sigma. Ann Med Surg (Lond) 2021;70:102842. https://doi.org/10.1016/j.amsu.2021.102842.Search in Google Scholar PubMed PubMed Central

63. Schuers, M, Joulakian, MB, Griffon, N, Pachéco, J, Périgard, C, Lepage, E, et al.. Quality indicators from laboratory and radiology information systems. Stud Health Technol Inf 2015;216:212–6.Search in Google Scholar

64. 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;2:373–89. https://doi.org/10.1515/almed-2021-0044.Search in Google Scholar PubMed PubMed Central

65. Tsai, ER, Tintu, AN, Boucherie, RJ, de Rijke, YB, Schotman, HHM, Demirtas, D. Characterization of laboratory flow and performance for process improvements via application of process mining. Appl Clin Inf 2023;14:144–52. https://doi.org/10.1055/a-1996-8479.Search in Google Scholar PubMed PubMed Central

66. Bunch, DR, Durant, TJ, Rudolf, JW. Artificial intelligence applications in clinical chemistry. Clin Lab Med 2023;43:47–69. https://doi.org/10.1016/j.cll.2022.09.005.Search in Google Scholar PubMed

67. Undru, TR, Uday, U, Lakshmi, JT, Kaliappan, A, Mallamgunta, S, Nikhat, SS, et al.. Integrating artificial intelligence for clinical and laboratory diagnosis – a review. Maedica (Bucur). 2022;17:420–6. https://doi.org/10.26574/maedica.2022.17.2.420.Search in Google Scholar PubMed PubMed Central

68. Herman, DS, Rhoads, DD, Schulz, WL, Durant, TJS. Artificial intelligence and mapping a new direction in laboratory medicine: a review. Clin Chem 2021;67:1466–82. https://doi.org/10.1093/clinchem/hvab165.Search in Google Scholar PubMed

69. Sveshchinsky, ML, Zheleznyakova, IA, Kukushkin, VI, Semakova, EV. Analysis of actual resource utilization by laboratory services of hospital institutions in the Russian Federation. Health Care Manag 2017;30-39.Search in Google Scholar

70. Fantus, JE. Marketing your lab’s services more effectively. MLO Med Lab Obs 1990;22:36–7.Search in Google Scholar

71. Al Naam, YA, Elsafi, S, Al Jahdali, MH, Al Shaman, RS, Al-Qurouni, BH, Al Zahrani, EM. The impact of total automaton on the clinical laboratory workforce: a case study. J Healthc Leader 2022;14:55–62. https://doi.org/10.2147/jhl.s362614.Search in Google Scholar PubMed PubMed Central

72. Thurow, K. Strategies for automating analytical and bioanalytical laboratories. Anal Bioanal Chem 2023;415:5057–66. https://doi.org/10.1007/s00216-023-04727-2.Search in Google Scholar PubMed PubMed Central

73. Rupp, N, Peschke, K, Köppl, M, Drissner, D, Zuchner, T. Establishment of low-cost laboratory automation processes using AutoIt and 4-axis robots. SLAS Technol 2022;27:312–18. https://doi.org/10.1016/j.slast.2022.07.001.Search in Google Scholar PubMed

74. Wolf, Á, Wolton, D, Trapl, J, Janda, J, Romeder-Finger, S, Gatternig, T, et al.. Towards robotic laboratory automation Plug & Play: the “LAPP” framework. SLAS Technol 2022;27:18–25. https://doi.org/10.1016/j.slast.2021.11.003.Search in Google Scholar PubMed

75. Badiou, S, Vuillot, O, Bargnoux, AS, Kuster, N, Lefebvre, S, Sebbane, M, et al.. Improved quality of samples and laboratory turnaround time using 3.5 mL low vacuum BD Vacutainer® Barricor tubes in the emergency department. Pract Lab Med 2019;16:00128. https://doi.org/10.1016/j.plabm.2019.e00128.Search in Google Scholar PubMed PubMed Central

76. Gawria, G, Tillmar, L, Landberg, E. A comparison of stability of chemical analytes in plasma from the BD Vacutainer Barricor tube with mechanical separator versus tubes containing gel separator. J Clin Lab Anal 2020;34:23060. https://doi.org/10.1002/jcla.23060.Search in Google Scholar PubMed PubMed Central

77. Kim, K, Lee, SG, Kim, TH, Lee, SG. Economic evaluation of total laboratory automation in the clinical laboratory of a tertiary care hospital. Ann Lab Med 2022;42:89–95. https://doi.org/10.3343/alm.2022.42.1.89.Search in Google Scholar PubMed PubMed Central

78. William, OU. The customer-oriented laboratory, 2nd ed. Chicago: American Society of Clinical Pathologists; 1996:294 p.Search in Google Scholar

79. Kishkun, AA. Centralization of clinical laboratory research. Moscow: GEOTAR-Medicine; 2015:368 p.Search in Google Scholar

80. Shcolnik, W, de Oliveira, CA, de São José, AS, de Oliveira Galoro, CA, Plebani, M, Burnett, D. Brazilian laboratory indicators program. Clin Chem Lab Med 2012;50:1923–34. https://doi.org/10.1515/cclm-2012-0357.Search in Google Scholar PubMed

81. Gontard, P, Stankevich, L, Gerasimova, E, Khlamov, V. TCO – innovative, profit and efficiency oriented tool for evidence based laboratory management. Citilab’s theoretical model and subsequent month-long, full flow production validation on 2 competing technological platforms. ORGHODRAV: news. Opinions. Education. WSHOUZ Bulletin 2015;2:96–102.Search in Google Scholar

82. Tsai, WW, Nash, DB, Seamonds, B, Weir, GJ. Point-of-care versus central laboratory testing: an economic analysis in an academic medical center. Clin Ther 1994;16:898–910.Search in Google Scholar

83. Nataatmadja, M, Fung, AWS, Jacobson, B, Ferera, J, Bernstein, E, Komenda, P, et al.. Performance of StatSensor point-of-care device for measuring creatinine in patients with chronic kidney disease and postkidney transplantation. Can J Kidney Health Dis 2020;7:2054358120970716. https://doi.org/10.1177/2054358120970716.Search in Google Scholar PubMed PubMed Central

84. Mansoor, I, Dar, FJ. Utilizing data analytics and business intelligence tools in laboratory workflow. EJIFCC 2024;35:34–43.Search in Google Scholar

85. Hawkins, RC. Laboratory turnaround time. Clin Biochem Rev 2007;28:179–94.Search in Google Scholar

86. Rudat, KS, Henry, J, Mosley, J. Improving laboratory results turnaround time. Best Pract Benchmarking Healthc 1996;1:301–6.Search in Google Scholar

87. Khalifa, M, Khalid, P. Improving laboratory results turnaround time by reducing pre analytical phase. Integrating Stud Health Technol Inform 2014;202:71–4.10.3233/978-1-61499-423-7-71Search in Google Scholar

88. UNI EN ISO 15189. Medical laboratory: requirements for quality and competence. Geneva (Switzerland): International Organization for Standardization; 2013.Search in Google Scholar

89. Ialongo, C, Bernardini, S. Timeliness “at a glance”: assessing the turnaround time through the six sigma metrics. Biochem Med 2016;26:98. https://doi.org/10.11613/bm.2016.010.Search in Google Scholar PubMed PubMed Central

90. Cakirca, G. The evaluation of error types and turnaround time of pre analytical phase in biochemistry and hematology laboratory. Iranian Journal of Pathology 2018;13:173. https://doi.org/10.30699/ijp.13.2.173.Search in Google Scholar

91. Holland, LL, Smith, LL, Blick, KE. Total laboratory automation can help eliminate the laboratory as a factor in emergency department length of stay. Am J Clin Pathol 2006;125:765. https://doi.org/10.1309/3j5p-9vjr-up4u-5ru5.Search in Google Scholar

92. Eshetu, LH. Evaluation of hospital laboratory workflow design in Ethiopia: blood specimen collection and chemistry laboratory. Ergonomics Int J 2018;2:2577–953.10.23880/EOIJ-16000179Search in Google Scholar

93. Eyob, AM, Binyam, T, Getachew, B, Aytenew, A, Veronica, G. Factors affecting quality of laboratory services in public and private health facilities. EJIFCC 2017;28:205–23.Search in Google Scholar

94. Hadian, SA, Rezayatmand, R, Shaarbafchizadeh, N, Ketabi, S, Pourghaderi, AR. Hospital performance evaluation indicators: a scoping review. BMC Health Serv Res 2024;24:561. https://doi.org/10.1186/s12913-024-10940-1.Search in Google Scholar PubMed PubMed Central

95. Gnanaraj, J, Kulkarni, RG, Sahoo, D, Abhishekh, B. Assessment of the key performance indicator proposed by NABH in the blood centre of a tertiary health care hospital in Southern India. Indian J Hematol Blood Transfus 2023;39:308–16. https://doi.org/10.1007/s12288-022-01563-9.Search in Google Scholar PubMed PubMed Central

96. Karnchanaphiboonwong, A, Sringam, P, Niwattakul, K, Krommuang, T, Gammie, A. Innovation, automation and informatics improves quality in lerdsin hospital, Thailand. Br J Biomed Sci 2023;80:11532. https://doi.org/10.3389/bjbs.2023.11532.Search in Google Scholar PubMed PubMed Central

97. Antonios, K, Croxatto, A, Culbreath, K. Current state of laboratory automation in clinical microbiology laboratory. Clin Chem 2021;68:99–114. https://doi.org/10.1093/clinchem/hvab242.Search in Google Scholar PubMed

98. Lou, AH, Elnenaei, MO, Sadek, I, Thompson, S, Crocker, BD, Nassar, BA. Multiple pre- and post-analytical lean approaches to the improvement of the laboratory turnaround time in a large core laboratory. Clin Biochem 2017;50:864–9. https://doi.org/10.1016/j.clinbiochem.2017.04.019.Search in Google Scholar PubMed

99. Ellison, TL, Alharbi, M, Alkaf, M, Elimam, S, Alfaries, M, Nounou, RA, et al.. Implementation of total laboratory automation at a tertiary care hospital in Saudi Arabia: effect on turnaround time and cost efficiency. Ann Saudi Med 2018;38:352–7. https://doi.org/10.5144/0256-4947.2018.352.Search in Google Scholar PubMed PubMed Central

100. Lou, AH, Elnenaei, MO, Sadek, I, Thompson, S, Crocker, BD, Nassar, B. Evaluation of the impact of a total automation system in a large core laboratory on turnaround time. Clin Biochem 2016;49:1254–8. https://doi.org/10.1016/j.clinbiochem.2016.08.018.Search in Google Scholar PubMed

101. Angeletti, S, De Cesaris, M, Hart, JG, Urbano, M, Vitali, MA, Fragliasso, F, et al.. Laboratory automation and intra-laboratory turnaround time: experience at the university hospital campus bio-medico of Rome. J Lab Autom 2015;20:652–8. https://doi.org/10.1177/2211068214566458.Search in Google Scholar PubMed

102. Chung, HJ, Song, YK, Hwang, SH, Lee, DH, Sugiura, T. Experimental fusion of different versions of the total laboratory automation system and improvement of laboratory turnaround time. J Clin Lab Anal 2018;32:22400. https://doi.org/10.1002/jcla.22400.Search in Google Scholar PubMed PubMed Central

103. Yun, SG, Shin, JW, Park, ES, Bang, HI, Kang, JG. Efficiency of an automated reception and turnaround time management system for the phlebotomy room. Ann Lab Med 2016;36:49–54. https://doi.org/10.3343/alm.2016.36.1.49.Search in Google Scholar PubMed PubMed Central

104. Chang, J, Kim, S, Um, TH, Cho, CR, Shin, DW, Yoo, SJ. Real-time display of laboratory testing status improves satisfaction levels in an emergency department: a pilot study. J Clin Lab Anal 2020;34:23290. https://doi.org/10.1002/jcla.23290.Search in Google Scholar PubMed PubMed Central

105. Starks, RD, Merrill, AE, Davis, SR, Voss, DR, Goldsmith, PJ, Brown, BS, et al.. Use of middleware data to dissect and optimize hematology autoverification. J Pathol Inf 2021;12:19. https://doi.org/10.4103/jpi.jpi_89_20.Search in Google Scholar PubMed PubMed Central

106. Zhang, L, Liu, ZH, Lv, YJ, Fu, S, Luo, ZM, Guo, ML. Comprehensive improvements in the emergency laboratory test process based on information technology. BMC Med Inf Decis Making 2023;23:292. https://doi.org/10.1186/s12911-023-02387-x.Search in Google Scholar PubMed PubMed Central

107. Yodchai, K, Khaorapapong, T, Karnjanadecha, M, Songsriboonsit, B. Development of a notification delivery specimen system for perioperative Thai nurses via the LINE application. Digit Health 2023;9:20552076231211242. https://doi.org/10.1177/20552076231211242.Search in Google Scholar PubMed PubMed Central

108. Inal, TC, Ozturk, GO, Kibar, F, Cetiner, S, Matyar, S, Daglioglu, G, et al.. Lean six sigma methodologies improve clinical laboratory efficiency and reduce turnaround times. J Clin Lab Anal 2018;32:22180. https://doi.org/10.1002/jcla.22180.Search in Google Scholar PubMed PubMed Central

109. Audibert, PF, Heim, JA, Vetch, A. Guiding the lean design of a hospital’s new laboratory: a case study. HERD 2023;16:379–94. https://doi.org/10.1177/19375867231164779.Search in Google Scholar PubMed

110. Randell, EW, Short, G, Lee, N, Beresford, A, Spencer, M, Kennell, M, et al.. Autoverification process improvement by Six Sigma approach: clinical chemistry & immunoassay. Clin Biochem 2018;55:42–8. https://doi.org/10.1016/j.clinbiochem.2018.03.002.Search in Google Scholar PubMed

111. Durur, F, Akbulut, Y. Lean methodology for pathology laboratories: a case study from a public hospital. Turk Patoloji Derg 2019;35:228–36. https://doi.org/10.5146/tjpath.2019.01462.Search in Google Scholar PubMed

112. Apostu, SA, Vasile, V, Veres, C. Externalities of lean implementation in medical laboratories. Process optimization vs. Adaptation and flexibility for the future. Int J Environ Res Publ Health 2021;18:12309. https://doi.org/10.3390/ijerph182312309.Search in Google Scholar PubMed PubMed Central

113. Trigueiro, G, Oliveira, C, Rodrigues, A, Seabra, S, Pinto, R, Bala, Y, et al.. Conversion of a classical microbiology laboratory to a total automation laboratory enhanced by the application of lean principles. Microbiol Spectr 2024;12:0215323. https://doi.org/10.1128/spectrum.02153-23.Search in Google Scholar PubMed PubMed Central

114. Tarieh, RRA, Zayyat, R, Naoufal, RN, Samaha, HR. A case study exploring the impact of JCI standards implementation on staff productivity and motivation at the laboratory and blood bank. Health Sci Rep 2022;5:497. https://doi.org/10.1002/hsr2.497.Search in Google Scholar PubMed PubMed Central

115. Jakovljevic, M, Timofeyev, Y, Zhuravleva, T. The impact of pandemic-driven care redesign on hospital efficiency. Risk Manag Healthc Policy 2024;17:1477–91. https://doi.org/10.2147/rmhp.s465167.Search in Google Scholar

116. Shouval, R, Fein, JA, Savani, B, Mohty, M, Nagler, A. Machine learning and artificial intelligence in haematology. Br J Haematol 2021;192:239–50. https://doi.org/10.1111/bjh.16915.Search in Google Scholar PubMed

117. Iqbal, J, Cortés Jaimes, DC, Makineni, P, Subramani, S, Hemaida, S, Thugu, TR, et al.. Reimagining healthcare: unleashing the power of artificial intelligence in medicine. Cureus 2023;15:e44658. https://doi.org/10.7759/cureus.44658.Search in Google Scholar PubMed PubMed Central

118. Zhou, S, Chen, B, Fu, ES, Yan, H. Computer vision meets microfluidics: a label-free method for high-throughput cell analysis. Microsyst Nanoeng 2023;9:116. https://doi.org/10.1038/s41378-023-00562-8.Search in Google Scholar PubMed PubMed Central

119. Syed, TA, Siddiqui, MS, Abdullah, HB, Jan, S, Namoun, A, Alzahrani, A, et al.. In-depth review of augmented reality: tracking technologies, development tools, AR displays, collaborative AR, and security concerns. Sensors (Basel) 2022;23:146. https://doi.org/10.3390/s23010146.Search in Google Scholar PubMed PubMed Central

120. Navab, N, Martin-Gomez, A, Seibold, M, Sommersperger, M, Song, T, Winkler, A, et al.. Medical augmented reality: definition, principle components, domain modeling, and design-development-validation process. J Imaging 2022;9:4. https://doi.org/10.3390/jimaging9010004.Search in Google Scholar PubMed PubMed Central

121. Rupp, N, Peschke, K, Köppl, M, Drissner, D, Zuchner, T. Establishment of low-cost laboratory automation processes using AutoIt and 4-axis robots. SLAS Technol 2022;27:312–18. https://doi.org/10.1016/j.slast.2022.07.001.Search in Google Scholar PubMed

122. Manickam, P, Mariappan, SA, Murugesan, SM, Hansda, S, Kaushik, A, Shinde, R, et al.. Artificial intelligence (AI) and Internet of medical Things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors 2022;12:562. https://doi.org/10.3390/bios12080562.Search in Google Scholar PubMed PubMed Central

123. Austerjost, J, Porr, M, Riedel, N, Geier, D, Becker, T, Scheper, T, et al.. Introducing a virtual assistant to the lab: a voice user interface for the intuitive control of laboratory instruments. SLAS Technol 2018;23:476–82. https://doi.org/10.1177/2472630318788040.Search in Google Scholar PubMed

124. Avila Vazquez, MF, Rupp, N, Ballardt, L, Opara, J, Zuchner, T. An expandable voice user interface as lab assistant based on an improved version of Google’s speech recognition. Sci Rep 2023;13:19451. https://doi.org/10.1038/s41598-023-46185-x.Search in Google Scholar PubMed PubMed Central

125. Tsai, ER, Tintu, AN, Demirtas, D, Boucherie, RJ, de Jonge, R, de Rijke, YB. A critical review of laboratory performance indicators. Crit Rev Clin Lab Sci 2019;56:458–71. https://doi.org/10.1080/10408363.2019.1641789.Search in Google Scholar PubMed

Received: 2025-03-16
Accepted: 2025-04-23
Published Online: 2025-05-05
Published in Print: 2025-09-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Quality indicators: an evolving target for laboratory medicine
  4. Reviews
  5. Regulating the future of laboratory medicine: European regulatory landscape of AI-driven medical device software in laboratory medicine
  6. The spectrum of nuclear patterns with stained metaphase chromosome plate: morphology nuances, immunological associations, and clinical relevance
  7. Opinion Papers
  8. Comprehensive assessment of medical laboratory performance: a 4D model of quality, economics, velocity, and productivity indicators
  9. Detecting cardiac injury: the next generation of high-sensitivity cardiac troponins improving diagnostic outcomes
  10. Perspectives
  11. Can Theranos resurrect from its ashes?
  12. Guidelines and Recommendations
  13. Australasian guideline for the performance of sweat chloride testing 3rd edition: to support cystic fibrosis screening, diagnosis and monitoring
  14. General Clinical Chemistry and Laboratory Medicine
  15. Recommendations for the integration of standardized quality indicators for glucose point-of-care testing
  16. A cost-effective assessment for the combination of indirect immunofluorescence and solid-phase assay in ANA-screening
  17. Assessment of measurement uncertainty of immunoassays and LC-MS/MS methods for serum 25-hydroxyvitamin D
  18. A novel immunoprecipitation-based targeted liquid chromatography-tandem mass spectrometry analysis for accurate determination for copeptin in human serum
  19. Histamine metabolite to basal serum tryptase ratios in systemic mastocytosis and hereditary alpha tryptasemia using a validated LC-MS/MS approach
  20. Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid
  21. Impact of analytical bias on machine learning models for sepsis prediction using laboratory data
  22. Immunochemical measurement of urinary free light chains and Bence Jones proteinuria
  23. Serum biomarkers as early indicators of outcomes in spontaneous subarachnoid hemorrhage
  24. High myoglobin plasma samples risk being reported as falsely low due to antigen excess – follow up after a 2-year period of using a mitigating procedure
  25. Candidate Reference Measurement Procedures and Materials
  26. Commutability evaluation of glycated albumin candidate EQA materials
  27. Reference Values and Biological Variations
  28. Health-related reference intervals for heavy metals in non-exposed young adults
  29. Hematology and Coagulation
  30. Practical handling of hemolytic, icteric and lipemic samples for coagulation testing in European laboratories. A collaborative survey from the European Organisation for External Quality Assurance Providers in Laboratory Medicine (EQALM)
  31. Cancer Diagnostics
  32. Assessment of atypical cells in detecting bladder cancer in female patients
  33. Cardiovascular Diseases
  34. False-positive cardiac troponin I values due to macrotroponin in healthy athletes after COVID-19
  35. Diabetes
  36. A comparison of current methods to measure antibodies in type 1 diabetes
  37. Letters to the Editor
  38. The neglected issue of pyridoxal- 5′ phosphate
  39. Error in prostate-specific antigen levels after prostate cancer treatment with radical prostatectomy
  40. Arivale is dead ‒ Hooke is alive
  41. A single dose of 20-mg of ostarine is detectable in hair
  42. Growing importance of vocabularies in medical laboratories
  43. Congress Abstracts
  44. 62nd National Congress of the Hungarian Society of Laboratory Medicine Szeged, Hungary, August 28–30, 2025
https://doi.org/10.1515/cclm-2025-0323
Keywords for this article
IVD management; medical laboratories; efficiency indicators; quality indicators; healthcare

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Quality indicators: an evolving target for laboratory medicine
  4. Reviews
  5. Regulating the future of laboratory medicine: European regulatory landscape of AI-driven medical device software in laboratory medicine
  6. The spectrum of nuclear patterns with stained metaphase chromosome plate: morphology nuances, immunological associations, and clinical relevance
  7. Opinion Papers
  8. Comprehensive assessment of medical laboratory performance: a 4D model of quality, economics, velocity, and productivity indicators
  9. Detecting cardiac injury: the next generation of high-sensitivity cardiac troponins improving diagnostic outcomes
  10. Perspectives
  11. Can Theranos resurrect from its ashes?
  12. Guidelines and Recommendations
  13. Australasian guideline for the performance of sweat chloride testing 3rd edition: to support cystic fibrosis screening, diagnosis and monitoring
  14. General Clinical Chemistry and Laboratory Medicine
  15. Recommendations for the integration of standardized quality indicators for glucose point-of-care testing
  16. A cost-effective assessment for the combination of indirect immunofluorescence and solid-phase assay in ANA-screening
  17. Assessment of measurement uncertainty of immunoassays and LC-MS/MS methods for serum 25-hydroxyvitamin D
  18. A novel immunoprecipitation-based targeted liquid chromatography-tandem mass spectrometry analysis for accurate determination for copeptin in human serum
  19. Histamine metabolite to basal serum tryptase ratios in systemic mastocytosis and hereditary alpha tryptasemia using a validated LC-MS/MS approach
  20. Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid
  21. Impact of analytical bias on machine learning models for sepsis prediction using laboratory data
  22. Immunochemical measurement of urinary free light chains and Bence Jones proteinuria
  23. Serum biomarkers as early indicators of outcomes in spontaneous subarachnoid hemorrhage
  24. High myoglobin plasma samples risk being reported as falsely low due to antigen excess – follow up after a 2-year period of using a mitigating procedure
  25. Candidate Reference Measurement Procedures and Materials
  26. Commutability evaluation of glycated albumin candidate EQA materials
  27. Reference Values and Biological Variations
  28. Health-related reference intervals for heavy metals in non-exposed young adults
  29. Hematology and Coagulation
  30. Practical handling of hemolytic, icteric and lipemic samples for coagulation testing in European laboratories. A collaborative survey from the European Organisation for External Quality Assurance Providers in Laboratory Medicine (EQALM)
  31. Cancer Diagnostics
  32. Assessment of atypical cells in detecting bladder cancer in female patients
  33. Cardiovascular Diseases
  34. False-positive cardiac troponin I values due to macrotroponin in healthy athletes after COVID-19
  35. Diabetes
  36. A comparison of current methods to measure antibodies in type 1 diabetes
  37. Letters to the Editor
  38. The neglected issue of pyridoxal- 5′ phosphate
  39. Error in prostate-specific antigen levels after prostate cancer treatment with radical prostatectomy
  40. Arivale is dead ‒ Hooke is alive
  41. A single dose of 20-mg of ostarine is detectable in hair
  42. Growing importance of vocabularies in medical laboratories
  43. Congress Abstracts
  44. 62nd National Congress of the Hungarian Society of Laboratory Medicine Szeged, Hungary, August 28–30, 2025
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 19.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2025-0323/html
Scroll to top button