A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak

The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low thr...

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Autores:
Ortíz-Barrios, Miguel
Jaramillo Rueda, Natalia
Gul, Muhammet
Jimenez Delgado, Genett Isabel
Alfaro-Saiz, Juan-Jose
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10437
Acceso en línea:
https://hdl.handle.net/11323/10437
https://repositorio.cuc.edu.co/
Palabra clave:
Emergency departments (EDs)
Intuitionistic fuzzy analytic hierarchy process (IF-AHP)
Intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL)
Combined compromise solution (CoCoSo)
Performance evaluation
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_9b358ce89ed78933cd55b7c41c17e7e4
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10437
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
title A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
spellingShingle A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
Emergency departments (EDs)
Intuitionistic fuzzy analytic hierarchy process (IF-AHP)
Intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL)
Combined compromise solution (CoCoSo)
Performance evaluation
title_short A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
title_full A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
title_fullStr A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
title_full_unstemmed A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
title_sort A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreak
dc.creator.fl_str_mv Ortíz-Barrios, Miguel
Jaramillo Rueda, Natalia
Gul, Muhammet
Jimenez Delgado, Genett Isabel
Alfaro-Saiz, Juan-Jose
dc.contributor.author.none.fl_str_mv Ortíz-Barrios, Miguel
Jaramillo Rueda, Natalia
Gul, Muhammet
Jimenez Delgado, Genett Isabel
Alfaro-Saiz, Juan-Jose
dc.subject.proposal.eng.fl_str_mv Emergency departments (EDs)
Intuitionistic fuzzy analytic hierarchy process (IF-AHP)
Intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL)
Combined compromise solution (CoCoSo)
Performance evaluation
topic Emergency departments (EDs)
Intuitionistic fuzzy analytic hierarchy process (IF-AHP)
Intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL)
Combined compromise solution (CoCoSo)
Performance evaluation
description The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-31T22:09:54Z
dc.date.available.none.fl_str_mv 2023-08-31T22:09:54Z
dc.date.issued.none.fl_str_mv 2023-03-05
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Ortíz-Barrios, M.; Jaramillo-Rueda, N.; Gul, M.; Yucesan, M.; Jiménez-Delgado, G.; Alfaro-Saíz, J.-J. A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak. Int. J. Environ. Res. Public Health 2023, 20, 4591. https://doi.org/10.3390/ ijerph20054591
dc.identifier.issn.spa.fl_str_mv 1661-7827
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10437
dc.identifier.doi.none.fl_str_mv 10.3390/ijerph20054591
dc.identifier.eissn.spa.fl_str_mv 1660-4601
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Ortíz-Barrios, M.; Jaramillo-Rueda, N.; Gul, M.; Yucesan, M.; Jiménez-Delgado, G.; Alfaro-Saíz, J.-J. A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak. Int. J. Environ. Res. Public Health 2023, 20, 4591. https://doi.org/10.3390/ ijerph20054591
1661-7827
10.3390/ijerph20054591
1660-4601
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10437
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv International Journal of Environmental Research and Public Health
dc.relation.references.spa.fl_str_mv 1. Hooker, E.A.; Mallow, P.J.; Oglesby, M.M. Characteristics and trends of emergency department visits in the United States (2010–2014). J. Emerg. Med. 2019, 56, 344–351. [CrossRef]
2. Gul, M.; Guneri, A.F. A comprehensive review of emergency department simulation applications for normal and disaster conditions. Comput. Ind. Eng. 2015, 83, 327–344. [CrossRef]
3. Paul, J.A.; Lin, L. Models for improving patient throughput and waiting at hospital emergency departments. J. Emerg. Med. 2012, 43, 1119–1126. [CrossRef] [PubMed]
4. Sohrabizadeh, S.; Yousefian, S.; Bahramzadeh, A.; Vaziri, M.H. A systematic review of health sector responses to the coincidence of disasters and COVID-19. BMC Public Health 2021, 21, 1–9. [CrossRef]
5. Lippi, G.; Favaloro, E.J.; Plebani, M. Laboratory medicine and natural disasters: Are we ready for the challenge? Clin. Chem. Lab. Med. 2010, 48, 573–575. [CrossRef] [PubMed]
6. Lioutas, E.D.; Charatsari, C. Enhancing the ability of agriculture to cope with major crises or disasters: What the experience of COVID-19 teaches us. Agric. Syst. 2021, 187, 103023. [CrossRef]
7. Quah, L.J.J.; Tan, B.K.K.; Fua, T.-P.; Wee, C.P.J.; Lim, C.S.; Nadarajan, G.; Zakaria, N.D.; Chan, S.-E.J.; Wan, P.W.; Teo, L.T.; et al. Reorganising the emergency department to manage the COVID-19 outbreak. Int. J. Emerg. Med. 2020, 13, 32. [CrossRef]
8. Park, M.; Cook, A.R.; Lim, J.T.; Sun, Y.; Dickens, B.L. A systematic review of COVID-19 epidemiology based on current evidence. J. Clin. Med. 2020, 9, 967. [CrossRef]
9. B ˛aczkiewicz, A.; Kizielewicz, B.; Shekhovtsov, A.; W ˛atróbski, J.; Sałabun, W. Methodical Aspects of MCDM Based E-Commerce Recommender System. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 2192–2229. [CrossRef]
10. Opricovic, S.; Tzeng, G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 2004, 156, 445–455. [CrossRef]
11. Fiallos, J.; Patrick, J.; Michalowski, W.; Farion, K. Using data envelopment analysis for assessing the performance of pediatric emergency department physicians. Health Care Manag. Sci. 2017, 20, 129–140. [CrossRef]
12. Torkayesh, A.E.; Pamucar, D.; Ecer, F.; Chatterjee, P. An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in eastern europe. Socio-Econ. Plan. Sci. 2021, 78, 101052. [CrossRef]
13. Yazdani, M.; Zarate, P.; Kazimieras Zavadskas, E.; Turskis, Z. A combined compromise solution (CoCoSo) method for multicriteria decision-making problems. Manag. Decis. 2019, 57, 2501–2519. [CrossRef]
14. Hulland, E.N.; Wiens, K.; Shirude, S.; Morgan, J.D.; Bertozzi-Villa, A.; Farag, T.H.; Fullman, N.; Kraemer, M.U.G.; Miller-Petrie, M.K.; Gupta, V.; et al. Travel time to health facilities in areas of outbreak potential: Maps for guiding local preparedness and response. BMC Med. 2019, 17, 232. [CrossRef] [PubMed]
15. Seyedin, H.; Moslehi, S.; Sakhaei, F.; Dowlati, M. Developing a hospital preparedness checklist to assess the ability to respond to the COVID-19 pandemic. East Mediterr. Health J. 2021, 2, 131–141. [CrossRef] [PubMed]
16. Gul, M.; Yucesan, M. Hospital preparedness assessment against COVID-19 pandemic: A case study in Turkish tertiary healthcare services. Math. Probl. Eng. 2021, 2021, 2931219. [CrossRef]
17. Garcia-Lopez, J.; Delgadillo, J.; Vilarrodona, A.; Querol, S.; Ovejo, J.; Coll, R.; Millan, A.; Madrigal, A.; Soria, G.; Vidal, F.; et al. SARS-CoV-2/COVID-19 pandemic: First wave, impact, response and lessons learnt in a fully integrated Regional Blood and Tissue Bank. A narrative report. Blood Transfus. 2021, 19, 158. [CrossRef] [PubMed]
18. Marques, L.C.; Lucca, D.C.; Alves, E.O.; Fernandes, G.C.M.; Nascimento, K.C.D. COVID-19: Nursing care for safety in the mobile pre-hospital service. Texto Contexto-Enferm. 2020, 29. [CrossRef]
19. Marshall, D.L.; Bois, F.; Jensen, S.K.; Linde, S.A.; Higby, R.; Rémy-McCort, Y.; Murray, S.; Dieckelman, B.; Sudradjat, F.; Martin, G.G. Sentinel Coronavirus environmental monitoring can contribute to detecting asymptomatic SARS-CoV-2 virus spreaders and can verify effectiveness of workplace COVID-19 controls. Microb. Risk Anal. 2020, 16, 100137. [CrossRef]
20. Jöbges, S.; Vinay, R.; Luyckx, V.A.; Biller-Andorno, N. Recommendations on COVID-19 triage: International comparison and ethical analysis. Bioethics 2020, 34, 948–959. [CrossRef]
21. Singh, P.K.; Nandi, S.; Ghafoor, K.Z.; Ghosh, U.; Rawat, D.B. Preventing COVID-19 spread using information and communication technology. IEEE Consum. Electron. Mag. 2020, 10, 18–27. [CrossRef]
22. Xiao, Y.; Torok, M.E. Taking the right measures to control COVID-19. Lancet Infect. Dis. 2020, 20, 523–524. [CrossRef]
23. Carenzo, L.; Costantini, E.; Greco, M.; Barra, F.L.; Rendiniello, V.; Mainetti, M.; Bui, R.; Zanella, A.; Grasselli, G.; Lagioia, M.; et al. Hospital surge capacity in a tertiary emergency referral centre during the COVID-19 outbreak in Italy. Anaesthesia 2020, 75, 928–934. [CrossRef]
24. Suleiman, A.; Bsisu, I.; Guzu, H.; Santarisi, A.; Alsatari, M.; Abbad, A.; Jaber, A.; Harb, T.; Abuhejleh, A.; Nadi, N.; et al. Preparedness of frontline doctors in Jordan healthcare facilities to COVID-19 outbreak. Int. J. Environ. Res. Public Health 2020, 17, 3181. [CrossRef]
25. Islam, M.S.; Rahman, K.M.; Sun, Y.; Qureshi, M.O.; Abdi, I.; Chughtai, A.A.; Seale, H. Current knowledge of COVID-19 and infection prevention and control strategies in healthcare settings: A global analysis. Infect. Control. Hosp. Epidemiol. 2020, 41, 1196–1206. [CrossRef] [PubMed]
26. Griffin, K.M.; Karas, M.G.; Ivascu, N.S.; Lief, L. Hospital preparedness for COVID-19: A practical guide from a critical care perspective. Am. J. Respir. Crit. Care Med. 2020, 201, 1337–1344. [CrossRef]
27. Boufkhed, S.; Harding, R.; Kutluk, T.; Husseini, A.; Pourghazian, N.; Shamieh, O. What is the preparedness and capacity of palliative care services in Middle-Eastern and North African countries to respond to COVID-19? A rapid survey. J. Pain Symptom Manag. 2021, 61, e13–e50. [CrossRef] [PubMed]
28. Mukhtar, S. Preparedness and proactive infection control measures of Pakistan during COVID-19 pandemic outbreak. Res. Soc. Adm. Pharm. 2021, 17, 2052. [CrossRef] [PubMed]
29. Gupta, S.; Federman, D.G. Hospital preparedness for COVID-19 pandemic: Experience from department of medicine at Veterans Affairs Connecticut Healthcare System. Postgrad. Med. 2020, 132, 489–494. [CrossRef]
30. AlTakarli, N.S. China’s response to the COVID-19 outbreak: A model for epidemic preparedness and management. Dubai Med. J. 2020, 3, 44–49. [CrossRef]
31. Noh, J.Y.; Song, J.Y.; Yoon, J.G.; Seong, H.; Cheong, H.J.; Kim, W.J. Safe hospital preparedness in the era of COVID-19: The Swiss cheese model. Int. J. Infect. Dis. 2020, 98, 294–296. [CrossRef]
32. Chang, T.W.; Pai, C.J.; Lo, H.W.; Hu, S.K. A hybrid decision-making model for sustainable supplier evaluation in electronics manufacturing. Comput. Ind. Eng. 2021, 156, 107283. [CrossRef]
33. Ortiz-Barrios, M.; Borrego-Areyanes, A.A.; Gómez-Villar, I.D.; De Felice, F.; Petrillo, A.; Gul, M.; Yucesan, M. 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. 2021, 62, 102411. [CrossRef]
34. Moheimani, A.; Sheikh, R.; Hosseini, S.M.H.; Sana, S.S. Assessing the preparedness of hospitals facing disasters using the rough set theory: Guidelines for more preparedness to cope with the COVID-19. Int. J. Syst. Sci. Oper. Logist. 2021, 9, 339–354. [CrossRef]
35. Moheimani, A.; Sheikh, R.; Hosseini, S.M.H.; Sana, S.S. Assessing the agility of hospitals in disaster management: Application of interval type-2 fuzzy Flowsort inference system. Soft Comput. 2021, 25, 3955–3974. [CrossRef]
36. Saner, H.S.; Yucesan, M.; Gul, M. A Bayesian BWM and VIKOR-based model for assessing hospital preparedness in the face of disasters. Nat. Hazards 2021, 111, 1603–1635. [CrossRef] [PubMed]
37. Ortiz-Barrios, M.; Gul, M.; López-Meza, P.; Yucesan, M.; Navarro-Jiménez, E. Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals. Int. J. Disaster Risk Reduct. 2020, 49, 101748. [CrossRef] [PubMed]
38. Hosseini, S.M.; Bahadori, M.; Raadabadi, M.; Ravangard, R. Ranking hospitals based on the disasters preparedness using the TOPSIS technique in western Iran. Hosp. Top. 2019, 97, 23–31. [CrossRef] [PubMed]
39. Marzaleh, M.A.; Rezaee, R.; Rezaianzadeh, A.; Rakhshan, M.; Haddadi, G.; Peyravi, M. Developing a model for hospitals’ emergency department preparedness in radiation and nuclear incidents and nuclear terrorism in Iran. Bull. Emerg. Trauma 2019, 7, 300. [CrossRef]
40. Ortiz-Barrios, M.; Gul, M.; Yucesan, M.; Alfaro-Sarmiento, I.; Navarro-Jiménez, E.; Jiménez-Delgado, G. A fuzzy hybrid decisionmaking framework for increasing the hospital disaster preparedness: The colombian case. Int. J. Disaster Risk Reduct. 2022, 72, 102831. [CrossRef]
41. Hezam, I.M.; Nayeem, M.K.; Foul, A.; Alrasheedi, A.F. COVID-19 Vaccine: A neutrosophic MCDM approach for determining the priority groups. Results Phys. 2021, 20, 103654. [CrossRef] [PubMed]
42. Atanassov, K.T. Intuitionistic Fuzzy Sets; Physica: Heidelberg, Germany, 1999. [CrossRef]
43. Zadeh, L.A. Fuzzy Sets, Fuzzy Logic and Fuzzy Systems; World Scientific: Singapore, 1996. [CrossRef]
44. Saaty, T.L. Decision-making with the AHP: Why is the principal eigenvector necessary. Eur. J. Oper. Res. 2003, 145, 85–91. [CrossRef]
45. Si, S.-L.; You, X.-Y.; Liu, H.-C.; Zhang, P. DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Math. Probl. Eng. 2018, 2018, 3696457. [CrossRef]
46. Ortiz-Barrios, M.; Silvera-Natera, E.; Petrillo, A.; Gul, M.; Yucesan, M. A multicriteria approach to integrating occupational safety health performance and industry systems productivity in the context of aging workforce: A case study. Saf. Sci. 2022, 152, 105764. [CrossRef]
47. Juliet Orji, I.; Ojadi, F.; Kalu Okwara, U. The nexus between e-commerce adoption in a health pandemic and firm performance: The role of pandemic response strategies. J. Bus. Res. 2022, 145, 616–635. [CrossRef]
48. Ocampo, L.; Yamagishi, K. Modeling the lockdown relaxation protocols of the philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis. Socio-Econ. Plan. Sci. 2020, 72, 100911. [CrossRef]
49. Peng, X.; Krishankumar, R.; Ravichandran, K.S. A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation. Soft Comput. 2021, 25, 4213–4241. [CrossRef]
50. Ocampo, L.A. Applying fuzzy AHP–TOPSIS technique in identifying the content strategy of sustainable manufacturing for food production. Environ. Dev. Sustain. 2019, 21, 2225–2251. [CrossRef]
51. Boran, F.E.; Genç, S.; Kurt, M.; Akay, D. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst. Appl. 2009, 36, 11363–11368. [CrossRef]
52. Liu, Y.; Eckert, C.M.; Earl, C. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst. Appl. 2020, 161, 113738. [CrossRef]
53. Aicevarya Devi, S.; Felix, A.; Narayanamoorthy, S.; Ahmadian, A.; Balaenu, D.; Kang, D. An intuitionistic fuzzy decision support system for COVID-19 lockdown relaxation protocols in india. Comput. Electr. Eng. 2022, 102, 108166. [CrossRef] [PubMed]
54. Büyüközkan, G.; Göçer, F.; Karabulut, Y. A new group decision making approach with IF AHP and IF VIKOR for selecting hazardous waste carriers. Meas. J. Int. Meas. Confed. 2019, 134, 66–82. [CrossRef]
55. Aliabadi, M.M. Human error analysis in furnace start-up operation using HEART under intuitionistic fuzzy environment. J. Loss Prev. Process Ind. 2021, 69, 104372. [CrossRef]
56. Duleba, S.; Alkharabsheh, A.; Gündo ˘gdu, F.K. Creating a common priority vector in intuitionistic fuzzy AHP: A comparison of entropy-based and distance-based models. Ann. Oper. Res. 2022, 318, 163–187. [CrossRef]
57. Xu, Z. Intuitionistic Preference Modeling and Interactive Decision-Making; Springer: Berlin/Heidelberg, Germany, 2011. [CrossRef]
58. 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. 2020, 158, 113543. [CrossRef]
59. Lin, C.; Kou, G.; Peng, Y.; Alsaadi, F.E. Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM. Ann. Oper. Res. 2020, 316, 179–195. [CrossRef]
60. 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 2022, 19, 1133. [CrossRef]
61. 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. 2022, 20, 100626. [CrossRef]
62. Stevi´c, Ž.; Pamuˇcar, 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. 2020, 140, 106231. [CrossRef]
63. Sałabun, W.; Karczmarczyk, A.; W ˛atróbski, J.; Jankowski, J. Handling data uncertainty in decision making with COMET. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI); IEEE: Piscataway, NJ, USA, 2018; pp. 1478–1484. [CrossRef]
64. Aires, R.F.D.F.; Ferreira, L. The rank reversal problem in multi-criteria decision making: A literature review. Pesqui. Oper. 2018, 38, 331–362. [CrossRef]
65. Wang, Y.M.; Luo, Y. On rank reversal in decision analysis. Math. Comput. Model. 2009, 49, 1221–1229. [CrossRef]
66. Gormeli Kurt, N.; Gunes, C. How has COVID-19 pandemic affected crowded emergency services? Int. J. Clin. Pract. 2020, 74, e13624. [CrossRef] [PubMed]
67. Sen-Crowe, B.; Sutherland, M.; McKenney, M.; Elkbuli, A. A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. J. Surg. Res. 2021, 260, 56–63. [CrossRef]
68. Peiffer-Smadja, N.; Lucet, J.-C.; Bendjelloul, G.; Bouadma, L.; Gerard, S.; Choquet, C.; Jacques, S.; Khalil, A.; Maisani, P.; Casalino, E.; et al. Challenges and issues about organizing a hospital to respond to the COVID-19 outbreak: Experience from a french reference centre. Clin. Microbiol. Infect. 2020, 26, 669–672. [CrossRef]
69. AlQershi, N.A.; Thurasamy, R.; Ali, G.A.; Al-Rejal, H.A.; Al-Ganad, A.; Frhan, E. The effect of talent management and human capital on sustainable business performance: An empirical investigation in malaysian hospitals. Int. J. Ethics Syst. 2022, 38, 316–337. [CrossRef]
70. World Health Organization. Shortage Personal Protective Equipment Endangering Health Workers Worlwide. 2021. Available online: https://www.who.int/news/item/03-03-2020-shortage-of-personal-protective-equipment-endangering-health-workersworldwide (accessed on 30 November 2022).
71. Lino, D.O.D.C.; Barreto, R.; Souza, F.D.D.; Lima, C.J.M.D.; Silva Junior, G.B.D. Impact of lockdown on bed occupancy rate in a referral hospital during the COVID-19 pandemic in northeast brazil. Braz. J. Infect. Dis. 2020, 24, 466–469. [CrossRef]
72. Hosseininejad, S.M.; Aminiahidashti, H.; Pashaei, S.M.; Khatir, I.G.; Montazer, S.H.; Bozorgi, F.; Mahmoudi, F. Determinants of prolonged length of stay in the emergency department; a cross-sectional study. Arch. Acad. Emerg. Med. 2017, 5, e53.
73. Alhaidari, F.; Almuhaideb, A.; Alsunaidi, S.; Ibrahim, N.; Aslam, N.; Khan, I.; Shaikh, F.; Alshahrani, M.; Alharthi, H.; Alsenbel, Y.; et al. E-triage systems for COVID-19 outbreak: Review and recommendations. Sensors 2021, 21, 2845. [CrossRef]
74. Boserup, B.; McKenney, M.; Elkbuli, A. The impact of the COVID-19 pandemic on emergency department visits and patient safety in the united states. Am. J. Emerg. Med. 2020, 38, 1732–1736. [CrossRef]
75. 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 2020, 17, 2664. [CrossRef]
76. Aljahany, M.; Alassaf, W.; Alibrahim, A.A.; Kentab, O.; Alotaibi, A.; Alresseeni, A.; Algarni, A.; Algaeed, H.A.; Aljaber, M.I.; Alruwaili, B.; et al. Use of In Situ Simulation to Improve Emergency Department Readiness for the COVID-19 Pandemic. Prehospital Disaster Med. 2021, 36, 6–13. [CrossRef] [PubMed]
77. Łukasik, M.; Por˛ebska, A. Responsiveness and Adaptability of Healthcare Facilities in Emergency Scenarios: COVID-19 Experience. Int. J. Environ. Res. Public Health 2022, 19, 675. [CrossRef] [PubMed]
78. WSP. Hospitals After COVID-19: How Do We Design for an Uncertain Future? Available online: https://www.wsp.com/en-GL/ insights/hospitals-after-covid-19-how-do-we-design-for-an-uncertain-future (accessed on 18 June 2022).
79. Marinelli, M. Emergency Healthcare Facilities: Managing Design in a Post COVID-19 World in IEEE Engineering Management Review. Fourthquarter 2020, 48, 65–71. [CrossRef]
80. Garzotto, F.; Ceresola, E.; Panagiotakopoulou, S.; Spina, G.; Menotto, F.; Benozzi, M.; Casarotto, M.; Lanera, C.; Bonavina, M.G.; Gregori, D.; et al. COVID-19: Ensuring our medical equipment can meet the challenge. Expert Rev. Med. Devices 2020, 17, 483–489. [CrossRef]
81. Delgado, D.; Wyss Quintana, F.; Perez, G.; Sosa Liprandi, A.; Ponte-Negretti, C.; Mendoza, I.; Baranchuk, A. Personal Safety during the COVID-19 Pandemic: Realities and Perspectives of Healthcare Workers in Latin America. Int. J. Environ. Res. Public Health 2020, 17, 2798. [CrossRef]
82. Eftekhar Ardebili, M.; Naserbakht, M.; Bernstein, C.; Alazmani-Noodeh, F.; Hakimi, H.; Ranjbar, H. Healthcare providers experience of working during the COVID-19 pandemic: A qualitative study. Am. J. Infect. Control. 2021, 49, 547–554. [CrossRef]
83. Solomon, M.Z.; Wynia, M.; Gostin, L.O. Scarcity in the COVID-19 pandemic. Hastings Cent. Rep. 2020, 50, 3. [CrossRef]
84. Emanuel, E.J.; Persad, G.; Upshur, R.; Thome, B.; Parker, M.; Glickman, A.; Zhang, C.; Boyle, C.; Smith, M.; Phillips, J.P. Fair allocation of scarce medical resources in the time of COVID-19. New Engl. J. Med. 2020, 382, 2049–2055. [CrossRef]
85. Long, C.; Tang, S.; Feng, D.; Zhou, W.; Fu, H.; Li, G. Development and improvement of public health physician system in China. Chin. J. Public Health Manag. 2019, 35, 937–940. [CrossRef]
86. Cao, Y.; Shan, J.; Gong, Z.; Kuang, J.; Gao, Y. Status and Challenges of Public Health Emergency Management in China Related to COVID-19. Front. Public Health 2020, 8, 250. [CrossRef]
87. Liu, C. Status and training mode of emergency personnel in public health emergencies. Hum. Resour. Manag. 2016, 6, 244–245. [CrossRef]
88. Xu, H.; Intrator, O.; Bowblis, J.R. Shortages of staff in nursing homes during the COVID-19 pandemic: What are the driving factors? J. Am. Med. Dir. Assoc. 2020, 21, 1371–1377. [CrossRef]
89. Shrestha, A.; Shrestha, A.; Sonnenberg, T.; Shrestha, R. COVID-19 Emergency Department Protocols: Experience of Protocol Implementation through in-situ Simulation. Open Access Emerg. Med. OAEM 2020, 12, 293–303. [CrossRef]
90. Durmus, E.; Güneysu, F. Effect of COVID-19 on the admissions to the adult emergency. J. Contemp. Clin. Pract. 2020, 6, 58–63. [CrossRef]
91. Comelli, I.; Scioscioli, F.; Cervellin, G. Impact of the COVID-19 epidemic on census, organization and activity of a large urban Emergency Department. Acta Biomed. 2020, 91, 45–49. [CrossRef]
92. World Health Organization. Priority Medical Devices List for the COVID-19 Response and Associated Technical Specifications: Interim Guidance, 19 November 2020; No. WHO/2019-nCoV/MedDev/TS/O2T. V2; World Health Organization: Geneva, Switzerland, 2020; Available online: https://apps.who.int/iris/bitstream/handle/10665/336745/WHO-2019-nCoV-MedDev-TS-O2T.V2 -eng.pdf?sequence=1&isAllowed=y (accessed on 28 December 2022).
93. Stamm, T.A.; Andrews, M.R.; Mosor, E.; Ritschl, V.; Li, L.C.; Ma, J.K.; Campo-Arias, A.; Baker, S.; Burton, N.W.; Eghbali, M.; et al. The methodological quality is insufficient in clinical practice guidelines in the context of COVID-19: Systematic review. J. Clin. Epidemiol. 2021, 135, 125–135. [CrossRef]
94. Lamhoot, T.; Ben Shoshan, N.; Eisenberg, H.; Fainberg, G.; Mhiliya, M.; Cohen, N.; Bisker-Kassif, O.; Barak, O.; Weiniger, C.; Capua, T. Emergency department impaired adherence to personal protective equipment donning and doffing protocols during the COVID-19 pandemic. Isr. J. Health Policy Res. 2021, 10, 1–6. [CrossRef]
95. Santos, J.M.G.; Martínez, J.M.P.; Ortega, P.F.; Ros, M.L.; Ayala, M.C.S.; Hernández, G.P.; Martínez, P.M. Radiology departments as COVID-19 entry-door might improve healthcare efficacy and efficiency, and emergency department safety. Insights Imaging 2021, 12, 1. [CrossRef]
96. Khot, N. Navigating Healthcare Supply Shortages During the COVID-19 Pandemic: A Cardiologist’s Perspective. Circ. Cardiovasc. Qual. Outcomes 2020, 13, 6. [CrossRef]
97. Deriba, B.S.; Geleta, T.A.; Beyane, R.S.; Mohammed, A.; Tesema, M.; Jemal, K. Patient Satisfaction and Associated Factors During COVID-19 Pandemic in North Shoa Health Care Facilities. Patient Prefer. Adherence 2020, 14, 1923–1934. [CrossRef]
98. Winkelmann, J.; Webb, E.; Williams, G.A.; Hernández-Quevedo, C.; Maier, C.B.; Panteli, D. European countries’ responses in ensuring sufficient physical infrastructure and workforce capacity during the first COVID-19 wave. Health Policy 2022, 126, 362–372. [CrossRef]
99. Walton, H.; Navaratnam, A.V.; Ormond, M.; Gandhi, V.; Mann, C. Emergency medicine response to the COVID-19 pandemic in england: A phenomenological study. Emerg. Med. J. 2020, 37, 768–772. [CrossRef]
100. Remuzzi, A.; Remuzzi, G. COVID-19 and italy: What next? Lancet 2020, 395, 1225–1228. [CrossRef]
101. Sharp, A.L.; Huang, B.Z.; Broder, B.; Smith, M.; Yuen, G.; Subject, C.; Nau, C.; Creekmur, B.; Tartof, S.; Gould, M.K. Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score. Am. J. Emerg. Med. 2021, 46, 489–494. [CrossRef]
102. Choo, E.K.; Rajkumar, S.V. Medication shortages during the COVID-19 crisis: What we must do. Mayo Clin. Proc. 2020, 95, 1112–1115. [CrossRef]
103. Leite, H.; Lindsay, C.; Kumar, M. COVID-19 outbreak: Implications on healthcare operations. TQM J. 2021, 33, 247–256. [CrossRef]
104. Assistance Publique–Hôpitaux de paris’ response to the COVID-19 pandemic. Lancet 2020, 395, 1760–1761. [CrossRef]
105. Shen, Y.; Cui, Y.; Li, N.; Tian, C.; Chen, M.; Zhang, Y.-W.; Huang, Y.-Z.; Chen, H.; Kong, Q.-F.; Zhang, Q.; et al. Emergency responses to COVID-19 outbreak: Experiences and lessons from a general hospital in nanjing, china. Cardiovasc. Interv. Radiol. 2020, 43, 810–819. [CrossRef] [PubMed]
106. Etu, E.-E.; Monplaisir, L.; Arslanturk, S.; Masoud, S.; Aguwa, C.; Markevych, I.; Miller, J. Prediction of length of stay in the emergency department for COVID-19 patients: A machine learning approach. IEEE Access 2022, 10, 42229–42237. [CrossRef]
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spelling Atribución 4.0 Internacional (CC BY 4.0)© 2023 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ortíz-Barrios, MiguelJaramillo Rueda, NataliaGul, MuhammetJimenez Delgado, Genett IsabelAlfaro-Saiz, Juan-Jose2023-08-31T22:09:54Z2023-08-31T22:09:54Z2023-03-05Ortíz-Barrios, M.; Jaramillo-Rueda, N.; Gul, M.; Yucesan, M.; Jiménez-Delgado, G.; Alfaro-Saíz, J.-J. A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak. Int. J. Environ. Res. Public Health 2023, 20, 4591. https://doi.org/10.3390/ ijerph200545911661-7827https://hdl.handle.net/11323/1043710.3390/ijerph200545911660-4601Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.39 páginasapplication/pdfengMultidisciplinary Digital Publishing Institute (MDPI)Switzerlandhttps://www.mdpi.com/1660-4601/20/5/4591A fuzzy hybrid mcdm approach for assessing the emergency department performance during the COVID-19 outbreakArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85International Journal of Environmental Research and Public Health1. Hooker, E.A.; Mallow, P.J.; Oglesby, M.M. Characteristics and trends of emergency department visits in the United States (2010–2014). J. Emerg. Med. 2019, 56, 344–351. [CrossRef]2. Gul, M.; Guneri, A.F. A comprehensive review of emergency department simulation applications for normal and disaster conditions. Comput. Ind. Eng. 2015, 83, 327–344. [CrossRef]3. Paul, J.A.; Lin, L. Models for improving patient throughput and waiting at hospital emergency departments. J. Emerg. Med. 2012, 43, 1119–1126. [CrossRef] [PubMed]4. Sohrabizadeh, S.; Yousefian, S.; Bahramzadeh, A.; Vaziri, M.H. A systematic review of health sector responses to the coincidence of disasters and COVID-19. BMC Public Health 2021, 21, 1–9. [CrossRef]5. Lippi, G.; Favaloro, E.J.; Plebani, M. Laboratory medicine and natural disasters: Are we ready for the challenge? Clin. Chem. Lab. Med. 2010, 48, 573–575. [CrossRef] [PubMed]6. Lioutas, E.D.; Charatsari, C. Enhancing the ability of agriculture to cope with major crises or disasters: What the experience of COVID-19 teaches us. Agric. Syst. 2021, 187, 103023. [CrossRef]7. Quah, L.J.J.; Tan, B.K.K.; Fua, T.-P.; Wee, C.P.J.; Lim, C.S.; Nadarajan, G.; Zakaria, N.D.; Chan, S.-E.J.; Wan, P.W.; Teo, L.T.; et al. Reorganising the emergency department to manage the COVID-19 outbreak. Int. J. Emerg. Med. 2020, 13, 32. [CrossRef]8. Park, M.; Cook, A.R.; Lim, J.T.; Sun, Y.; Dickens, B.L. A systematic review of COVID-19 epidemiology based on current evidence. J. Clin. Med. 2020, 9, 967. [CrossRef]9. B ˛aczkiewicz, A.; Kizielewicz, B.; Shekhovtsov, A.; W ˛atróbski, J.; Sałabun, W. Methodical Aspects of MCDM Based E-Commerce Recommender System. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 2192–2229. [CrossRef]10. Opricovic, S.; Tzeng, G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 2004, 156, 445–455. [CrossRef]11. Fiallos, J.; Patrick, J.; Michalowski, W.; Farion, K. Using data envelopment analysis for assessing the performance of pediatric emergency department physicians. Health Care Manag. Sci. 2017, 20, 129–140. [CrossRef]12. Torkayesh, A.E.; Pamucar, D.; Ecer, F.; Chatterjee, P. An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in eastern europe. Socio-Econ. Plan. Sci. 2021, 78, 101052. [CrossRef]13. Yazdani, M.; Zarate, P.; Kazimieras Zavadskas, E.; Turskis, Z. A combined compromise solution (CoCoSo) method for multicriteria decision-making problems. Manag. Decis. 2019, 57, 2501–2519. [CrossRef]14. Hulland, E.N.; Wiens, K.; Shirude, S.; Morgan, J.D.; Bertozzi-Villa, A.; Farag, T.H.; Fullman, N.; Kraemer, M.U.G.; Miller-Petrie, M.K.; Gupta, V.; et al. Travel time to health facilities in areas of outbreak potential: Maps for guiding local preparedness and response. BMC Med. 2019, 17, 232. [CrossRef] [PubMed]15. Seyedin, H.; Moslehi, S.; Sakhaei, F.; Dowlati, M. Developing a hospital preparedness checklist to assess the ability to respond to the COVID-19 pandemic. East Mediterr. Health J. 2021, 2, 131–141. [CrossRef] [PubMed]16. Gul, M.; Yucesan, M. Hospital preparedness assessment against COVID-19 pandemic: A case study in Turkish tertiary healthcare services. Math. Probl. Eng. 2021, 2021, 2931219. [CrossRef]17. Garcia-Lopez, J.; Delgadillo, J.; Vilarrodona, A.; Querol, S.; Ovejo, J.; Coll, R.; Millan, A.; Madrigal, A.; Soria, G.; Vidal, F.; et al. SARS-CoV-2/COVID-19 pandemic: First wave, impact, response and lessons learnt in a fully integrated Regional Blood and Tissue Bank. A narrative report. Blood Transfus. 2021, 19, 158. [CrossRef] [PubMed]18. Marques, L.C.; Lucca, D.C.; Alves, E.O.; Fernandes, G.C.M.; Nascimento, K.C.D. COVID-19: Nursing care for safety in the mobile pre-hospital service. Texto Contexto-Enferm. 2020, 29. [CrossRef]19. Marshall, D.L.; Bois, F.; Jensen, S.K.; Linde, S.A.; Higby, R.; Rémy-McCort, Y.; Murray, S.; Dieckelman, B.; Sudradjat, F.; Martin, G.G. Sentinel Coronavirus environmental monitoring can contribute to detecting asymptomatic SARS-CoV-2 virus spreaders and can verify effectiveness of workplace COVID-19 controls. Microb. Risk Anal. 2020, 16, 100137. [CrossRef]20. Jöbges, S.; Vinay, R.; Luyckx, V.A.; Biller-Andorno, N. Recommendations on COVID-19 triage: International comparison and ethical analysis. Bioethics 2020, 34, 948–959. [CrossRef]21. Singh, P.K.; Nandi, S.; Ghafoor, K.Z.; Ghosh, U.; Rawat, D.B. Preventing COVID-19 spread using information and communication technology. IEEE Consum. Electron. Mag. 2020, 10, 18–27. [CrossRef]22. Xiao, Y.; Torok, M.E. Taking the right measures to control COVID-19. Lancet Infect. Dis. 2020, 20, 523–524. [CrossRef]23. Carenzo, L.; Costantini, E.; Greco, M.; Barra, F.L.; Rendiniello, V.; Mainetti, M.; Bui, R.; Zanella, A.; Grasselli, G.; Lagioia, M.; et al. Hospital surge capacity in a tertiary emergency referral centre during the COVID-19 outbreak in Italy. Anaesthesia 2020, 75, 928–934. [CrossRef]24. Suleiman, A.; Bsisu, I.; Guzu, H.; Santarisi, A.; Alsatari, M.; Abbad, A.; Jaber, A.; Harb, T.; Abuhejleh, A.; Nadi, N.; et al. Preparedness of frontline doctors in Jordan healthcare facilities to COVID-19 outbreak. Int. J. Environ. Res. Public Health 2020, 17, 3181. [CrossRef]25. Islam, M.S.; Rahman, K.M.; Sun, Y.; Qureshi, M.O.; Abdi, I.; Chughtai, A.A.; Seale, H. Current knowledge of COVID-19 and infection prevention and control strategies in healthcare settings: A global analysis. Infect. Control. Hosp. Epidemiol. 2020, 41, 1196–1206. [CrossRef] [PubMed]26. Griffin, K.M.; Karas, M.G.; Ivascu, N.S.; Lief, L. Hospital preparedness for COVID-19: A practical guide from a critical care perspective. Am. J. Respir. Crit. Care Med. 2020, 201, 1337–1344. [CrossRef]27. Boufkhed, S.; Harding, R.; Kutluk, T.; Husseini, A.; Pourghazian, N.; Shamieh, O. What is the preparedness and capacity of palliative care services in Middle-Eastern and North African countries to respond to COVID-19? A rapid survey. J. Pain Symptom Manag. 2021, 61, e13–e50. [CrossRef] [PubMed]28. Mukhtar, S. Preparedness and proactive infection control measures of Pakistan during COVID-19 pandemic outbreak. Res. Soc. Adm. Pharm. 2021, 17, 2052. [CrossRef] [PubMed]29. Gupta, S.; Federman, D.G. Hospital preparedness for COVID-19 pandemic: Experience from department of medicine at Veterans Affairs Connecticut Healthcare System. Postgrad. Med. 2020, 132, 489–494. [CrossRef]30. AlTakarli, N.S. China’s response to the COVID-19 outbreak: A model for epidemic preparedness and management. Dubai Med. J. 2020, 3, 44–49. [CrossRef]31. Noh, J.Y.; Song, J.Y.; Yoon, J.G.; Seong, H.; Cheong, H.J.; Kim, W.J. Safe hospital preparedness in the era of COVID-19: The Swiss cheese model. Int. J. Infect. Dis. 2020, 98, 294–296. [CrossRef]32. Chang, T.W.; Pai, C.J.; Lo, H.W.; Hu, S.K. A hybrid decision-making model for sustainable supplier evaluation in electronics manufacturing. Comput. Ind. Eng. 2021, 156, 107283. [CrossRef]33. Ortiz-Barrios, M.; Borrego-Areyanes, A.A.; Gómez-Villar, I.D.; De Felice, F.; Petrillo, A.; Gul, M.; Yucesan, M. 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. 2021, 62, 102411. [CrossRef]34. Moheimani, A.; Sheikh, R.; Hosseini, S.M.H.; Sana, S.S. Assessing the preparedness of hospitals facing disasters using the rough set theory: Guidelines for more preparedness to cope with the COVID-19. Int. J. Syst. Sci. Oper. Logist. 2021, 9, 339–354. [CrossRef]35. Moheimani, A.; Sheikh, R.; Hosseini, S.M.H.; Sana, S.S. Assessing the agility of hospitals in disaster management: Application of interval type-2 fuzzy Flowsort inference system. Soft Comput. 2021, 25, 3955–3974. [CrossRef]36. Saner, H.S.; Yucesan, M.; Gul, M. A Bayesian BWM and VIKOR-based model for assessing hospital preparedness in the face of disasters. Nat. Hazards 2021, 111, 1603–1635. [CrossRef] [PubMed]37. Ortiz-Barrios, M.; Gul, M.; López-Meza, P.; Yucesan, M.; Navarro-Jiménez, E. Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals. Int. J. Disaster Risk Reduct. 2020, 49, 101748. [CrossRef] [PubMed]38. Hosseini, S.M.; Bahadori, M.; Raadabadi, M.; Ravangard, R. Ranking hospitals based on the disasters preparedness using the TOPSIS technique in western Iran. Hosp. Top. 2019, 97, 23–31. [CrossRef] [PubMed]39. Marzaleh, M.A.; Rezaee, R.; Rezaianzadeh, A.; Rakhshan, M.; Haddadi, G.; Peyravi, M. Developing a model for hospitals’ emergency department preparedness in radiation and nuclear incidents and nuclear terrorism in Iran. Bull. Emerg. Trauma 2019, 7, 300. [CrossRef]40. Ortiz-Barrios, M.; Gul, M.; Yucesan, M.; Alfaro-Sarmiento, I.; Navarro-Jiménez, E.; Jiménez-Delgado, G. A fuzzy hybrid decisionmaking framework for increasing the hospital disaster preparedness: The colombian case. Int. J. Disaster Risk Reduct. 2022, 72, 102831. [CrossRef]41. Hezam, I.M.; Nayeem, M.K.; Foul, A.; Alrasheedi, A.F. COVID-19 Vaccine: A neutrosophic MCDM approach for determining the priority groups. Results Phys. 2021, 20, 103654. [CrossRef] [PubMed]42. Atanassov, K.T. Intuitionistic Fuzzy Sets; Physica: Heidelberg, Germany, 1999. [CrossRef]43. Zadeh, L.A. Fuzzy Sets, Fuzzy Logic and Fuzzy Systems; World Scientific: Singapore, 1996. [CrossRef]44. Saaty, T.L. Decision-making with the AHP: Why is the principal eigenvector necessary. Eur. J. Oper. Res. 2003, 145, 85–91. [CrossRef]45. Si, S.-L.; You, X.-Y.; Liu, H.-C.; Zhang, P. DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Math. Probl. Eng. 2018, 2018, 3696457. [CrossRef]46. Ortiz-Barrios, M.; Silvera-Natera, E.; Petrillo, A.; Gul, M.; Yucesan, M. A multicriteria approach to integrating occupational safety health performance and industry systems productivity in the context of aging workforce: A case study. Saf. Sci. 2022, 152, 105764. [CrossRef]47. Juliet Orji, I.; Ojadi, F.; Kalu Okwara, U. The nexus between e-commerce adoption in a health pandemic and firm performance: The role of pandemic response strategies. J. Bus. Res. 2022, 145, 616–635. [CrossRef]48. Ocampo, L.; Yamagishi, K. Modeling the lockdown relaxation protocols of the philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis. Socio-Econ. Plan. Sci. 2020, 72, 100911. [CrossRef]49. Peng, X.; Krishankumar, R.; Ravichandran, K.S. A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation. Soft Comput. 2021, 25, 4213–4241. [CrossRef]50. Ocampo, L.A. Applying fuzzy AHP–TOPSIS technique in identifying the content strategy of sustainable manufacturing for food production. Environ. Dev. Sustain. 2019, 21, 2225–2251. [CrossRef]51. Boran, F.E.; Genç, S.; Kurt, M.; Akay, D. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst. Appl. 2009, 36, 11363–11368. [CrossRef]52. Liu, Y.; Eckert, C.M.; Earl, C. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst. Appl. 2020, 161, 113738. [CrossRef]53. Aicevarya Devi, S.; Felix, A.; Narayanamoorthy, S.; Ahmadian, A.; Balaenu, D.; Kang, D. An intuitionistic fuzzy decision support system for COVID-19 lockdown relaxation protocols in india. Comput. Electr. Eng. 2022, 102, 108166. [CrossRef] [PubMed]54. Büyüközkan, G.; Göçer, F.; Karabulut, Y. A new group decision making approach with IF AHP and IF VIKOR for selecting hazardous waste carriers. Meas. J. Int. Meas. Confed. 2019, 134, 66–82. [CrossRef]55. Aliabadi, M.M. Human error analysis in furnace start-up operation using HEART under intuitionistic fuzzy environment. J. Loss Prev. Process Ind. 2021, 69, 104372. [CrossRef]56. Duleba, S.; Alkharabsheh, A.; Gündo ˘gdu, F.K. Creating a common priority vector in intuitionistic fuzzy AHP: A comparison of entropy-based and distance-based models. Ann. Oper. Res. 2022, 318, 163–187. [CrossRef]57. Xu, Z. Intuitionistic Preference Modeling and Interactive Decision-Making; Springer: Berlin/Heidelberg, Germany, 2011. [CrossRef]58. 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. 2020, 158, 113543. [CrossRef]59. Lin, C.; Kou, G.; Peng, Y.; Alsaadi, F.E. Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM. Ann. Oper. Res. 2020, 316, 179–195. [CrossRef]60. 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 2022, 19, 1133. [CrossRef]61. 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. 2022, 20, 100626. [CrossRef]62. Stevi´c, Ž.; Pamuˇcar, 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. 2020, 140, 106231. [CrossRef]63. Sałabun, W.; Karczmarczyk, A.; W ˛atróbski, J.; Jankowski, J. Handling data uncertainty in decision making with COMET. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI); IEEE: Piscataway, NJ, USA, 2018; pp. 1478–1484. [CrossRef]64. Aires, R.F.D.F.; Ferreira, L. The rank reversal problem in multi-criteria decision making: A literature review. Pesqui. Oper. 2018, 38, 331–362. [CrossRef]65. Wang, Y.M.; Luo, Y. On rank reversal in decision analysis. Math. Comput. Model. 2009, 49, 1221–1229. [CrossRef]66. Gormeli Kurt, N.; Gunes, C. How has COVID-19 pandemic affected crowded emergency services? Int. J. Clin. Pract. 2020, 74, e13624. [CrossRef] [PubMed]67. Sen-Crowe, B.; Sutherland, M.; McKenney, M.; Elkbuli, A. A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. J. Surg. Res. 2021, 260, 56–63. [CrossRef]68. Peiffer-Smadja, N.; Lucet, J.-C.; Bendjelloul, G.; Bouadma, L.; Gerard, S.; Choquet, C.; Jacques, S.; Khalil, A.; Maisani, P.; Casalino, E.; et al. Challenges and issues about organizing a hospital to respond to the COVID-19 outbreak: Experience from a french reference centre. Clin. Microbiol. Infect. 2020, 26, 669–672. [CrossRef]69. AlQershi, N.A.; Thurasamy, R.; Ali, G.A.; Al-Rejal, H.A.; Al-Ganad, A.; Frhan, E. The effect of talent management and human capital on sustainable business performance: An empirical investigation in malaysian hospitals. Int. J. Ethics Syst. 2022, 38, 316–337. [CrossRef]70. World Health Organization. Shortage Personal Protective Equipment Endangering Health Workers Worlwide. 2021. Available online: https://www.who.int/news/item/03-03-2020-shortage-of-personal-protective-equipment-endangering-health-workersworldwide (accessed on 30 November 2022).71. Lino, D.O.D.C.; Barreto, R.; Souza, F.D.D.; Lima, C.J.M.D.; Silva Junior, G.B.D. Impact of lockdown on bed occupancy rate in a referral hospital during the COVID-19 pandemic in northeast brazil. Braz. J. Infect. Dis. 2020, 24, 466–469. [CrossRef]72. Hosseininejad, S.M.; Aminiahidashti, H.; Pashaei, S.M.; Khatir, I.G.; Montazer, S.H.; Bozorgi, F.; Mahmoudi, F. Determinants of prolonged length of stay in the emergency department; a cross-sectional study. Arch. Acad. Emerg. Med. 2017, 5, e53.73. Alhaidari, F.; Almuhaideb, A.; Alsunaidi, S.; Ibrahim, N.; Aslam, N.; Khan, I.; Shaikh, F.; Alshahrani, M.; Alharthi, H.; Alsenbel, Y.; et al. E-triage systems for COVID-19 outbreak: Review and recommendations. Sensors 2021, 21, 2845. [CrossRef]74. Boserup, B.; McKenney, M.; Elkbuli, A. The impact of the COVID-19 pandemic on emergency department visits and patient safety in the united states. Am. J. Emerg. Med. 2020, 38, 1732–1736. [CrossRef]75. 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 2020, 17, 2664. [CrossRef]76. Aljahany, M.; Alassaf, W.; Alibrahim, A.A.; Kentab, O.; Alotaibi, A.; Alresseeni, A.; Algarni, A.; Algaeed, H.A.; Aljaber, M.I.; Alruwaili, B.; et al. Use of In Situ Simulation to Improve Emergency Department Readiness for the COVID-19 Pandemic. Prehospital Disaster Med. 2021, 36, 6–13. [CrossRef] [PubMed]77. Łukasik, M.; Por˛ebska, A. Responsiveness and Adaptability of Healthcare Facilities in Emergency Scenarios: COVID-19 Experience. Int. J. Environ. Res. Public Health 2022, 19, 675. [CrossRef] [PubMed]78. WSP. Hospitals After COVID-19: How Do We Design for an Uncertain Future? Available online: https://www.wsp.com/en-GL/ insights/hospitals-after-covid-19-how-do-we-design-for-an-uncertain-future (accessed on 18 June 2022).79. Marinelli, M. Emergency Healthcare Facilities: Managing Design in a Post COVID-19 World in IEEE Engineering Management Review. Fourthquarter 2020, 48, 65–71. [CrossRef]80. Garzotto, F.; Ceresola, E.; Panagiotakopoulou, S.; Spina, G.; Menotto, F.; Benozzi, M.; Casarotto, M.; Lanera, C.; Bonavina, M.G.; Gregori, D.; et al. COVID-19: Ensuring our medical equipment can meet the challenge. Expert Rev. Med. Devices 2020, 17, 483–489. [CrossRef]81. Delgado, D.; Wyss Quintana, F.; Perez, G.; Sosa Liprandi, A.; Ponte-Negretti, C.; Mendoza, I.; Baranchuk, A. Personal Safety during the COVID-19 Pandemic: Realities and Perspectives of Healthcare Workers in Latin America. Int. J. Environ. Res. Public Health 2020, 17, 2798. [CrossRef]82. Eftekhar Ardebili, M.; Naserbakht, M.; Bernstein, C.; Alazmani-Noodeh, F.; Hakimi, H.; Ranjbar, H. Healthcare providers experience of working during the COVID-19 pandemic: A qualitative study. Am. J. Infect. Control. 2021, 49, 547–554. [CrossRef]83. Solomon, M.Z.; Wynia, M.; Gostin, L.O. Scarcity in the COVID-19 pandemic. Hastings Cent. Rep. 2020, 50, 3. [CrossRef]84. Emanuel, E.J.; Persad, G.; Upshur, R.; Thome, B.; Parker, M.; Glickman, A.; Zhang, C.; Boyle, C.; Smith, M.; Phillips, J.P. Fair allocation of scarce medical resources in the time of COVID-19. New Engl. J. Med. 2020, 382, 2049–2055. [CrossRef]85. Long, C.; Tang, S.; Feng, D.; Zhou, W.; Fu, H.; Li, G. Development and improvement of public health physician system in China. Chin. J. Public Health Manag. 2019, 35, 937–940. [CrossRef]86. Cao, Y.; Shan, J.; Gong, Z.; Kuang, J.; Gao, Y. Status and Challenges of Public Health Emergency Management in China Related to COVID-19. Front. Public Health 2020, 8, 250. [CrossRef]87. Liu, C. Status and training mode of emergency personnel in public health emergencies. Hum. Resour. Manag. 2016, 6, 244–245. [CrossRef]88. Xu, H.; Intrator, O.; Bowblis, J.R. Shortages of staff in nursing homes during the COVID-19 pandemic: What are the driving factors? J. Am. Med. Dir. Assoc. 2020, 21, 1371–1377. [CrossRef]89. Shrestha, A.; Shrestha, A.; Sonnenberg, T.; Shrestha, R. COVID-19 Emergency Department Protocols: Experience of Protocol Implementation through in-situ Simulation. Open Access Emerg. Med. OAEM 2020, 12, 293–303. [CrossRef]90. Durmus, E.; Güneysu, F. Effect of COVID-19 on the admissions to the adult emergency. J. Contemp. Clin. Pract. 2020, 6, 58–63. [CrossRef]91. Comelli, I.; Scioscioli, F.; Cervellin, G. Impact of the COVID-19 epidemic on census, organization and activity of a large urban Emergency Department. Acta Biomed. 2020, 91, 45–49. [CrossRef]92. World Health Organization. Priority Medical Devices List for the COVID-19 Response and Associated Technical Specifications: Interim Guidance, 19 November 2020; No. WHO/2019-nCoV/MedDev/TS/O2T. V2; World Health Organization: Geneva, Switzerland, 2020; Available online: https://apps.who.int/iris/bitstream/handle/10665/336745/WHO-2019-nCoV-MedDev-TS-O2T.V2 -eng.pdf?sequence=1&isAllowed=y (accessed on 28 December 2022).93. Stamm, T.A.; Andrews, M.R.; Mosor, E.; Ritschl, V.; Li, L.C.; Ma, J.K.; Campo-Arias, A.; Baker, S.; Burton, N.W.; Eghbali, M.; et al. The methodological quality is insufficient in clinical practice guidelines in the context of COVID-19: Systematic review. J. Clin. Epidemiol. 2021, 135, 125–135. [CrossRef]94. Lamhoot, T.; Ben Shoshan, N.; Eisenberg, H.; Fainberg, G.; Mhiliya, M.; Cohen, N.; Bisker-Kassif, O.; Barak, O.; Weiniger, C.; Capua, T. Emergency department impaired adherence to personal protective equipment donning and doffing protocols during the COVID-19 pandemic. Isr. J. Health Policy Res. 2021, 10, 1–6. [CrossRef]95. Santos, J.M.G.; Martínez, J.M.P.; Ortega, P.F.; Ros, M.L.; Ayala, M.C.S.; Hernández, G.P.; Martínez, P.M. Radiology departments as COVID-19 entry-door might improve healthcare efficacy and efficiency, and emergency department safety. Insights Imaging 2021, 12, 1. [CrossRef]96. Khot, N. Navigating Healthcare Supply Shortages During the COVID-19 Pandemic: A Cardiologist’s Perspective. Circ. Cardiovasc. Qual. Outcomes 2020, 13, 6. [CrossRef]97. Deriba, B.S.; Geleta, T.A.; Beyane, R.S.; Mohammed, A.; Tesema, M.; Jemal, K. Patient Satisfaction and Associated Factors During COVID-19 Pandemic in North Shoa Health Care Facilities. Patient Prefer. Adherence 2020, 14, 1923–1934. [CrossRef]98. Winkelmann, J.; Webb, E.; Williams, G.A.; Hernández-Quevedo, C.; Maier, C.B.; Panteli, D. European countries’ responses in ensuring sufficient physical infrastructure and workforce capacity during the first COVID-19 wave. Health Policy 2022, 126, 362–372. [CrossRef]99. Walton, H.; Navaratnam, A.V.; Ormond, M.; Gandhi, V.; Mann, C. Emergency medicine response to the COVID-19 pandemic in england: A phenomenological study. Emerg. Med. J. 2020, 37, 768–772. [CrossRef]100. Remuzzi, A.; Remuzzi, G. COVID-19 and italy: What next? Lancet 2020, 395, 1225–1228. [CrossRef]101. Sharp, A.L.; Huang, B.Z.; Broder, B.; Smith, M.; Yuen, G.; Subject, C.; Nau, C.; Creekmur, B.; Tartof, S.; Gould, M.K. Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score. Am. J. Emerg. Med. 2021, 46, 489–494. [CrossRef]102. Choo, E.K.; Rajkumar, S.V. Medication shortages during the COVID-19 crisis: What we must do. Mayo Clin. Proc. 2020, 95, 1112–1115. [CrossRef]103. Leite, H.; Lindsay, C.; Kumar, M. COVID-19 outbreak: Implications on healthcare operations. TQM J. 2021, 33, 247–256. [CrossRef]104. Assistance Publique–Hôpitaux de paris’ response to the COVID-19 pandemic. Lancet 2020, 395, 1760–1761. [CrossRef]105. Shen, Y.; Cui, Y.; Li, N.; Tian, C.; Chen, M.; Zhang, Y.-W.; Huang, Y.-Z.; Chen, H.; Kong, Q.-F.; Zhang, Q.; et al. Emergency responses to COVID-19 outbreak: Experiences and lessons from a general hospital in nanjing, china. Cardiovasc. Interv. Radiol. 2020, 43, 810–819. [CrossRef] [PubMed]106. Etu, E.-E.; Monplaisir, L.; Arslanturk, S.; Masoud, S.; Aguwa, C.; Markevych, I.; Miller, J. Prediction of length of stay in the emergency department for COVID-19 patients: A machine learning approach. IEEE Access 2022, 10, 42229–42237. [CrossRef]391520Emergency departments (EDs)Intuitionistic fuzzy analytic hierarchy process (IF-AHP)Intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL)Combined compromise solution (CoCoSo)Performance evaluationPublicationORIGINALA Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.pdfA Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.pdfArtículoapplication/pdf3594075https://repositorio.cuc.edu.co/bitstreams/b1edca5f-f1c3-49cc-8667-ad08d0f05044/downloadcbb4bbb1e35ed2ef271177659d9d901fMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/afba0a57-abe6-4b03-8d7b-db8a68d3a271/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTA Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.pdf.txtA Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.pdf.txtExtracted texttext/plain155131https://repositorio.cuc.edu.co/bitstreams/bc024a45-9640-401a-9c19-e40679657f91/download27ffec58ed907bad5fb821827b298080MD53THUMBNAILA Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.pdf.jpgA Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.pdf.jpgGenerated Thumbnailimage/jpeg16186https://repositorio.cuc.edu.co/bitstreams/189c6356-2078-4b6f-bc0e-ab813fca64a2/download7801f5f94122e9ccc3d03996b849aa53MD5411323/10437oai:repositorio.cuc.edu.co:11323/104372024-09-17 14:16:50.843https://creativecommons.org/licenses/by/4.0/© 2023 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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