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A Hybrid Multi-criteria Framework for Evaluating the Performance of Clinical Labs During the Covid-19 Pandemic

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Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14029))

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Abstract

Clinical laboratories were affected by the recent Covid-19 pandemic, evidencing the low preparedness of some clinical labs when responding to seasonal diseases, epidemics/pandemics, and other disastrous events. However, various operational shortcomings become glaring in the labs also propelled by the virus’s ever-changing dynamics and rapid evolution. Therefore, this paper presents a novel hybrid intuitionistic Multi-criteria Decision-Making (MCDM) approach to evaluate the performance of clinical labs during the Covid-19 pandemic. First, we used Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) to estimate the relative weights of criteria and sub-criteria considering hesitancy and uncertainty properties. Second, we employed Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) to evaluate the interrelationships among performance criteria as often found in the healthcare context. Ultimately, the Combined Compromise Solution (CoCoSo) technique was applied to estimate the Performance Index (PI) of each clinical laboratory and pinpoint the main weaknesses hindering the effective response in presence of the Covid-19 and other disastrous events. This approach was validated in 9 clinical labs located in a Colombian region. The results evidenced that Operating capacity (global weight = 0.1985) and Occupational health and safety (global weight = 0.1924) are the most important aspects for increasing the overall response of the labs against new Covid-19 waves and future outbreaks. Besides, operating capacity (D + R = 37.486) and Equipment (D + R = 38.024) were concluded to be the main performance drivers. Also some clinical labs uncovered major shortcomings that may restrict their functioning in a future contingency.

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References

  1. Stanković, S., Ašanin, M.: Clinical laboratories in the era of the COVID-19 pandemic: An analysis of experiences in Serbia. Serbian Med. J. 3(3) (2021)

    Google Scholar 

  2. Tomo, S., Karli, S., Dharmalingam, K., Yadav, D., Sharma, P.: The clinical laboratory: A key player in diagnosis and management of COVID-19. Electron. J. Int. Feder. Clin. Chem. Lab. Med. 31(4), 326–346 (2020)

    Google Scholar 

  3. Fang, B., Meng, Q.H.: The laboratory’s role in combating COVID-19. Crit. Rev. Clin. Lab. Sci. 57(6), 400–414 (2020). https://doi.org/10.1080/10408363.2020.1776675

    Article  Google Scholar 

  4. Taherdoost, H., Madanchian, M.: Multi-Criteria decision making (MCDM) methods and concepts. Encyclopedia 3, 77–87 (2023). https://doi.org/10.3390/encyclopedia3010006

    Article  Google Scholar 

  5. Ortiz Barrios, M., Felizzola Jiménez, H., Nieto Isaza, S.: Comparative analysis between ANP and ANP- DEMATEL for six sigma project selection process in a healthcare provider. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 413–416. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13105-4_62

    Chapter  Google Scholar 

  6. Yazdani, M., Zarate, P., Zavadskas, E.K., Turskis, Z.: A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag. Decis. (2018). https://doi.org/10.1108/MD-05-2017-0458 Permanent link to this document: https://doi.org/10.1108/MD-05-2017-0458

  7. Nuñez-Argote, L., Baker, D.P., Jones, A.P.: Initial clinical laboratory response to COVID-19: A survey of medical laboratory professionals. Lab Med. 52(4), E115–E124 (2021). https://doi.org/10.1093/labmed/lmab021

    Article  Google Scholar 

  8. Torres, I., Sippy, R., Sacoto, F.: Assessing Critical Gaps in COVID-19 Testing Capacity: The Case of Delayed Results in Ecuador. BMC Public Health (2021)

    Google Scholar 

  9. Weller, S.A., et al.: Development and operation of the defence COVID-19 lab as a SARS-CoV-2 diagnostic screening capability for UK military personnel (2022)

    Google Scholar 

  10. Salermon, R.M.: Biosafety challenges for clinical labs during the COVID-19 pandemic. Clinical Laboratory News (2020)

    Google Scholar 

  11. Lippi, G., Plebani, M.: The critical role of laboratory medicine during coronavirus disease 2019 (COVID-19) and other viral outbreaks. Clin. Chem. Lab. Med. 58(7), 1063–1069 (2020)

    Article  Google Scholar 

  12. Sanyaolu, A., et al.: Comorbidity and its Impact on Patients with COVID-19. SN Comprehens. Clin. Med. 2(8), 1069–1076 (2020). https://doi.org/10.1007/s42399-020-00363-4

    Article  Google Scholar 

  13. Rimmer, A.: Covid-19: The challenges of shielding the vulnerable and the NHS workforce. BMJ 369, m1567 (2020)

    Google Scholar 

  14. Van den Berg, P., Yates, T.A., Haslam, N., et al.: The burden of COVID-19 in Eng-land’s intensive care units: A report from the ICNARC Case Mix Programme. medRxiv (2020)

    Google Scholar 

  15. Singh, A., Shaikh, N., Singh, R., Singh, A.: COVID-19: Challenges and strategic initiatives in diagnostics in Indian clinical laboratory. Indian J. Pathol. Microbiol. 63(Supplement), S138–S142 (2020)

    Google Scholar 

  16. Hanel, R., Getz, W.M.: COVID-19 and the workload of clinical laboratories: recommendations for the management of in vitro diagnostic services. medRxiv (2020)

    Google Scholar 

  17. Fang, Y., et al.: Sensitivity of chest CT for COVID-19: Comparison to RT-PCR. Radiology 296(2), E115–E117 (2020)

    Article  Google Scholar 

  18. Nardiello, S., et al.: Laboratory diagnosis of SARS-CoV-2 infection: A review of the available methods. Acta Biomed. 91(3), e2020024 (2020)

    Google Scholar 

  19. Neidich, S.D., et al.: Increased risk of hospitalization in pa-tients with SARS-CoV-2 variant of concern B.1.1.7: A retrospective cohort study. E-Clin. Med. 37, 100964 (2021)

    Google Scholar 

  20. Hogan, C.A., Sahoo, M.K., Pinsky, B.A.: Sample pooling as a strategy to detect community transmission of SARS-CoV-2. JAMA 323(19), 1967–1969 (2021)

    Article  Google Scholar 

  21. Ramaiah, A., Arora, N., Roux, L.: Drive-through testing: A unique, safe, and efficient way to test patients for COVID-19. Mayo Clin. Proc. 95(7), 1420–1421 (2020)

    Article  Google Scholar 

  22. Lin, Y.T., et al.: Laboratory outbreak of SARS-CoV-2 infection within a university hospital. J. Formos. Med. Assoc. 119(7), 1239–1246 (2020)

    Google Scholar 

  23. Singh, A.K., Singh, A., Shaikh, A., Singh, R., Misra, A., Chakraborty, R.: Assessing the preparedness of COVID-19 testing laboratories in India: A nation-wide study. Med. J. Armed Forces India 76(3), 296–302 (2020)

    Google Scholar 

  24. Cheema, S., Mahmood, A.: Efficiency assessment of clinical laboratories during COVID-19 pandemic: Data envelopment analysis approach. Clin. Epidemiol. Global Health 8(4), 1065–1069 (2020)

    Google Scholar 

  25. Berwick, D.M., Hackbarth, A.D.: Eliminating waste in US health care. JAMA 307(14), 1513–1516 (2012)

    Article  Google Scholar 

  26. Salvagno, G.L., Lippi, G., Guidi, G.C.: Laboratory performance indicators and quality of testing. J. Lab. Precis. Med. 1(1), 8–14 (2012)

    Google Scholar 

  27. Houshyar, A., Ayatollahi, H., Maleki, M.R.: Using multi-criteria decision-making (MCDM) techniques to evaluate the performance of medical laboratory centers in Iran. Med. J. Islam Repub. Iran 29, 184 (2015)

    Google Scholar 

  28. Azadeh, A., Ghaderi, S.F., Saberi, M.: Performance evaluation of medical laboratories using analytic hierarchy process (AHP) technique. Iranian J. Publ. Health 40(3), 48–55 (2011)

    Google Scholar 

  29. Liao, H.L., Yen, J.T., Ko, W.C., Huang, Y.T.: Evaluation of clinical laboratory performance during the COVID-19 pandemic using the analytic hierarchy process. BMC Med. Inform. Decis. Mak. 21(1), 1–10 (2021)

    Google Scholar 

  30. Ozcanhan, M.H., Bilen, S.G.: Evaluation of clinical laboratory performances with TOPSIS method. J. Med. Syst. 43(8), 1–8 (2019)

    Google Scholar 

  31. Khalilpourazary, S., Faraji, O., Eslamian, M.: Evaluation of clinical laboratories performance during the COVID-19 pandemic using TOPSIS. J. Med. Syst. 45(4), 1–10 (2021)

    Google Scholar 

  32. Afsar, A., Memon, N.A., Memon, Z.A.: Performance evaluation of clinical laboratories during COVID-19 pandemic: A grey relational analysis approach. Pak. J. Med. Sci. 37(4), 999–1004 (2021)

    Google Scholar 

  33. Delen, D., Demirkol, S., Gökmen, N.: Multi-criteria decision making approaches to evaluate COVID-19 testing centers. Health Care Manag. Sci. 24(2), 238–248 (2021)

    Google Scholar 

  34. Wang, Z., Li, Y., Jiang, L., Li, X., Chen, S., Liu, B.: Performance (2020)

    Google Scholar 

  35. Atanassov, K.T.: Intuitionistic Fuzzy Sets, pp. 1–137 (1999)

    Google Scholar 

  36. Xu, Z.: Intuitionistic Preference Modeling and Interactive Decision Making, vol. 280. Springer, Heidelberg (2014)

    Google Scholar 

  37. Ar, I.M., Erol, I., Peker, I., Ozdemir, A.I., Medeni, T.D., Medeni, I.T.: Evaluating the feasibility of blockchain in logistics operations: A decision framework. Expert Syst. Appl. 158, 113543 (2020). https://doi.org/10.1016/j.eswa.2020.113543

    Article  Google Scholar 

  38. Ortíz-Barrios, M.A., Garcia-Constantino, M., Nugent, C., Alfaro-Sarmiento, I.: A novel integration of IF-DEMATEL and TOPSIS for the classifier selection problem in assistive technology adoption for people with dementia. Int. J. Environ. Res. Public Health 19(3), 1133 (2022). https://doi.org/10.3390/ijerph19031133

    Article  Google Scholar 

  39. Orji, I.J., Ojadi, F., Okwara, U.K.: Assessing the pre-conditions for the pedagogical use of digital tools in the Nigerian higher education sector. Int. J. Manag. Educ. 20(2), 100626 (2022). https://doi.org/10.1016/j.ijme.2022.100626

    Article  Google Scholar 

  40. Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., Turskis, Z.: A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag. Decis. 57(9), 2501–2519 (2019). https://doi.org/10.1108/MD-05-2017-0458

  41. Stević, Ž, Pamučar, D., Puška, A., Chatterjee, P.: Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Comput. Ind. Eng. 140, 106231 (2020). https://doi.org/10.1016/j.cie.2019.106231

    Article  Google Scholar 

  42. Ortíz-Barrios, M.A., Alfaro-Saíz, J.-J.: Methodological approaches to support process improvement in emergency departments: A systematic review. Int. J. Environ. Res. Public Health 17(8), 2664 (2020). https://doi.org/10.3390/ijerph17082664

    Article  Google Scholar 

  43. Grau, C.M., et al.: Use of predictive tools in the management of COVID-19 patients: A key role of clinical laboratories. [Uso de herramientas predictivas en el manejo de pacientes COVID-19: El papel fundamental de los laboratorios clinicos] Adv. Lab. Med. 2(2), 245–252 (2021). https://doi.org/10.1515/almed-2021-0019

  44. Vega de la Cruz, L.O., Campaña, M.P., Pérez Vallejo, L.M., Tapia Claro, I.I.: Management of waiting lines through queuing theory in pharmaceutical facilities. [Gestión de las líneas de esperas a través de teoría de colas en entidades farma-céuticas] Rev. Cubana Farmacia 52(2) (2019). www.scopus.com

  45. Rivero, M.H.: An open-source application built with R programming language for clinical laboratories to innovate process of excellence and overcome the uncertain outlook during the global healthcare crisis. Paper presented at the Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, pp. 870–871 (2020). https://doi.org/10.1109/CSCI51800.2020.00163. www.scopus.com

  46. Lombardi Netto, A., Salomon, V.A.P., Ortiz Barrios, M.A.: Multi-criteria analysis of green bonds: Hybrid multi-method applications. Sustainability 13(19), 10512 (2021). https://doi.org/10.3390/su131910512

  47. Ortiz-Barrios, M., et al.: A multiple criteria decision-making approach for increasing the preparedness level of sales departments against COVID-19 and future pandemics: A real-world case. Int. J. Disaster Risk Reduct. 62, 102411 (2021). https://doi.org/10.1016/j.ijdrr.2021.102411

    Article  Google Scholar 

  48. Ortiz-Barrios, M., Nugent, C., Cleland, I., Donnelly, M., Verikas, A.: Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework. J. Multi-Criteria Decis. Anal. 27(1–2), 20–38 (2020). https://doi.org/10.1002/mcda.1678

    Article  Google Scholar 

  49. Nuñez-Perez, N., Ortíz-Barrios, M., McClean, S., Salas-Navarro, K., Jimenez-Delgado, G., Castillo-Zea, A.: Discrete-Event Simulation to Reduce Waiting Time in Accident and Emergency Departments: A Case Study in a District General Clinic, pp. 352–363 (2017)

    Google Scholar 

  50. Ortiz-Barrios, M., Lopez-Meza, P., McClean, S., Polifroni-Avendaño, G.: Discrete-Event Simulation for Performance Evaluation and Improvement of Gynecology Outpatient Departments: A Case Study in the Public Sector, pp. 101–112 (2019)

    Google Scholar 

  51. Muñoz, W.A., Fuentes, D.B., Farfán Urzúa, M.J.: Role of public laboratories in the sars-cov-2 diagnosis in the covid-19 pandemic: Experience, challenges and opportunities. March 2021. [Rol de los laboratorios públicos en el di-agnóstico SARS-CoV-2 en la pandemia de COVID-19: Experiencia, desafíos y opor-tunidades Marzo 2021] Revista Chilena De Infectologia, 38(2), 135–143 (2021). https://doi.org/10.4067/S0716-10182021000200135

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Acknowledgements

The authors would like to express their gratitude to Maria Fernanda Guzman Acosta and Jesús Soto Llanos for their valuable support during this research.

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Authors and Affiliations

  1. Department of Productivity and Innovation, Universidad de la Costa CUC, 080002, Barranquilla, Colombia

    Miguel Ortiz-Barrios & Andrea Espeleta-Aris

  2. Department of Industrial Engineering, Institución Universitaria de Barranquilla IUB, 080002, Barranquilla, Colombia

    Genett Jiménez-Delgado

  3. UNESP - Sao Paulo State University, Sao Paulo, 12242250, Brazil

    Helder Jose Celani-De Souza

  4. Sabin Medicina Diagnostica, Brasilia, 70715-900, Brazil

    Jonas Santana-de Oliveira

  5. Department of Computer Science, Nottingham Trent University, Nottigham, 4081112, UK

    Alexandros Konios

  6. Department of Research and Innovation, Soltraf Ingeniería S.A.S., 080001, Barranquilla, Colombia

    Leonardo Campis-Freyle

  7. Department of Health Sciences, Universidad Simon Bolivar, 080001, Barranquilla, Colombia

    Eduardo Navarro-Jimenez

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  1. Miguel Ortiz-Barrios
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  2. Andrea Espeleta-Aris
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Correspondence to Miguel Ortiz-Barrios .

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  1. Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

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Ortiz-Barrios, M. et al. (2023). A Hybrid Multi-criteria Framework for Evaluating the Performance of Clinical Labs During the Covid-19 Pandemic. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14029. Springer, Cham. https://doi.org/10.1007/978-3-031-35748-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-35748-0_8

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