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An integrated assessment system for the accreditation of medical laboratories

  • Neven Saleh ORCID logo EMAIL logo and Ahmed Abo Agyla
Published/Copyright: June 29, 2020

Abstract

Medical laboratory accreditation becomes a trend to be trustable for diagnosis of diseases. It is always performed at regular intervals to assure competence of quality management systems (QMS) based on pre-defined standards. However, few attempts were carried out to assess the quality level of medical laboratory services. Moreover, there is no realistic study that classifies and makes analyses of laboratory performance based on a computational model. The purpose of this study was to develop an integrated system for medical laboratory accreditation that assesses QMS against ISO 15189. In addition, a deep analysis of factors that sustain accreditation was presented. The system started with establishing a core matrix that maps QMS elements with ISO 15189 clauses. Through this map, a questionnaire was developed to measure the performance. Therefore, score indices were calculated for the QMS. A fuzzy logic model was designed based on the calculated scores to classify medical laboratories according to their tendency for accreditation. Further, in case of failure of accreditation, cause-and-effect root analysis was done to realize the causes. Finally, cloud computing principles were employed to launch a web application in order to facilitate user interface with the proposed system. In verification, the system has been tested using a dataset of 12 medical laboratories in Egypt. Results have proved system robustness and consistency. Thus, the system is considered as a self-assessment tool that demonstrates points of weakness and strength.


Corresponding author: Neven Saleh, Systems and Biomedical Engineering Department, Higher Institute of Engineering, Shorouk Academy, 2 El-horea Road, Cairo 11837, Egypt, E-mail:

  1. Research funding: No funding

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

  3. Conflict of interest: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

References

1. Silva, P. Guidelines on establishment of accreditation of health laboratories. Geneva: World Health Organization; 2007.Search in Google Scholar

2. Plebani, M, Sciacovelli, L. ISO 15189 accreditation: navigation between quality management and patient safety. J Med Biochem 2017;36:225–30. https://doi.org/10.1515/jomb-2017-0038.10.1515/jomb-2017-0038Search in Google Scholar PubMed PubMed Central

3. ISO 15189:2012 (E). International Organization for Standardization. Medical laboratories-requirements for quality and competence, 3rd ed. Geneva: ISO; 2012.Search in Google Scholar

4. Kusum, M, Silva, P. Quality standards in health laboratories: implementation in Thailand: a novel approach. Geneva: World Health Organization; 2005.Search in Google Scholar

5. Njoroge, AG. Assessment of the quality of medical laboratory service provision in Kenya [Ph.D. thesis]. Niarobi: School of Medicine, Kenya University; 2014.Search in Google Scholar

6. Abdel wahed, M, El kady, KH, Saleh, N. Automated management system for accreditation of clinical engineering department in hospitals. J Clin Eng 2019;44:47–52. https://doi.org/10.1097/jce.0000000000000319.10.1097/JCE.0000000000000319Search in Google Scholar

7. Zima, T, Accreditation of medical laboratories-system, process, benefits for labs. J Med Biochem 2017;36:231–7. https://doi.org/10.1515/jomb-2017-0025.10.1515/jomb-2017-0025Search in Google Scholar PubMed PubMed Central

8. Rosella, B, Nikiforova, ND. Measurement error models for interlaboratory comparison measurement data. Quality Reliabil Eng Int 2016;32:2005–15. https://doi.org/10.1002/qre.2034.10.1002/qre.2034Search in Google Scholar

9. Engelbrech, AP. Computational intelligence: an introduction, 2nd ed. New York: Wiley; 2007.10.1002/9780470512517Search in Google Scholar

10. Saleh, N, Balestra, G. Comprehensive framework for preventive maintenance priority of medical equipment. In: Proceedings of the 37th IEEE Engineering in Medicine and Biology Society. IEEE, Milan, Italy; 2015, 1227–30.10.1109/EMBC.2015.7318588Search in Google Scholar PubMed

11. Saleh, N, Sharawi, A, Abdel Wahed, M, Balestra, G. A conceptual priority index for purchasing medical equipment in hospitals. J Clin Eng 2015;40:E1–E6. https://doi.org/10.1097/jce.0000000000000104.10.1097/JCE.0000000000000104Search in Google Scholar

12. Kalantri, R, Chandrawat, S. Root cause assessment for a manufacturing industry: a case study. J Eng Sci Technol Rev 2013;6:62–67. https://doi.org/10.25103/jestr.061.12.10.25103/jestr.061.12Search in Google Scholar

13. Dobrusskin, C. On the identification of contradictions using cause effect chain analysis. Procedia CIRP 2016;39:221–4. https://doi.org/10.1016/j.procir.2016.01.192.10.1016/j.procir.2016.01.192Search in Google Scholar

14. Abdel Wahed, M, Montaser, M, Sami, SA. Root cause analysis for medical calibration laboratory nonconformities. In: Proceedings of the 4th Cairo International Biomedical Engineering Conference. IEEE, Cairo, Egypt; 2010, 206–9.10.1109/CIBEC.2010.5716096Search in Google Scholar

15. Spilakova, P, Schauer, F. Remote laboratory management system remlabnet and its booking system. In: Proceedings of the international conference on e-technologies and networks for development. Lodz, Poland; 2015:1–5.10.1109/ICeND.2015.7328544Search in Google Scholar

Received: 2019-06-03
Accepted: 2020-05-13
Published Online: 2020-06-29
Published in Print: 2021-02-23

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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