Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by impl...

Full description

Autores:
Ortiz Barrios, Miguel Angel
Coba Blanco, Dayana Milena
Alfaro-Saiz, Juan-Jose
Stand-González, Daniela
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8835
Acceso en línea:
https://hdl.handle.net/11323/8835
https://doi.org/10.3390/ijerph18168814
https://repositorio.cuc.edu.co/
Palabra clave:
Healthcare
Emergency department
COVID-19
Process improvement
Systematic review
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_92f8c25d5d162e3d0e91dd1be170160c
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8835
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
title Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
spellingShingle Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
Healthcare
Emergency department
COVID-19
Process improvement
Systematic review
title_short Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
title_full Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
title_fullStr Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
title_full_unstemmed Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
title_sort Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review
dc.creator.fl_str_mv Ortiz Barrios, Miguel Angel
Coba Blanco, Dayana Milena
Alfaro-Saiz, Juan-Jose
Stand-González, Daniela
dc.contributor.author.spa.fl_str_mv Ortiz Barrios, Miguel Angel
Coba Blanco, Dayana Milena
Alfaro-Saiz, Juan-Jose
Stand-González, Daniela
dc.subject.spa.fl_str_mv Healthcare
Emergency department
COVID-19
Process improvement
Systematic review
topic Healthcare
Emergency department
COVID-19
Process improvement
Systematic review
description The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-11-05T13:58:20Z
dc.date.available.none.fl_str_mv 2021-11-05T13:58:20Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
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/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 1660-4601
1661-7827
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8835
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/ijerph18168814
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 1660-4601
1661-7827
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8835
https://doi.org/10.3390/ijerph18168814
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Anderson, R.M.; Heesterbeek, H.; Klinkenberg, D.; Hollingsworth, T.D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 2020, 395, 931–934. [CrossRef]
2. Sun, X.; Wang, T.; Cai, D.; Hu, Z.; Liao, H.; Zhi, L.; Wei, H.; Zhang, Z.; Qiu, Y.; Wang, J.; et al. Cytokine storm intervention in the early stages of COVID-19 pneumonia. Cytokine Growth Factor Rev. 2020, 53, 38–42. [CrossRef]
3. Walker, P.G.T.; Whittaker, C.; Watson, O.J.; Baguelin, M.; Winskill, P.; Hamlet, A.; Ghani, A.C. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 2020, 369, 413–422. [CrossRef]
4. World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/ (accessed on 21 May 2021).
5. Singer, D.R. A new pandemic out of China: The Wuhan 2019-nCoV coronavirus syndrome. Health Policy Technol. 2020, 9, 1. [CrossRef]
6. 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]
7. World Health Organization. Coronavirus disease 2019 (COVID-19) Situation Report-93. 2020. Available online: https://www. who.int/docs/default-source/coronaviruse/situation-reports/20200422-sitrep-93-covid-19.pdf?sfvrsn=35cf80d7_4 (accessed on 16 August 2021).
8. Mareiniss, D.P. The impending storm: COVID-19, pandemics and our overwhelmed emergency departments. Am. J. Emerg. Med. 2020, 38, 1293–1294. [CrossRef]
9. 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. In Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2019; Volume 11582. [CrossRef]
10. Singh, R.P.; Javaid, M.; Haleem, A.; Suman, R. Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 521–524. [CrossRef]
11. Hundal, G.S.; Thiyagarajan, S.; Alduraibi, M.; Laux, C.M.; Furterer, S.L.; Cudney, E.A.; Antony, J. Lean Six Sigma as an organizational resilience mechanism in health care during the era of COVID-19. Int. J. Lean Six Sigma 2021, in press. [CrossRef]
12. Gupta, S.; Starr, M.K.; Farahani, R.Z.; Asgari, N. Pandemics/epidemics: Challenges and opportunities for operations management research. Manuf. Serv. Oper. Manag. 2020, in press.
13. Munn, Z.; Stern, C.; Aromataris, E.; Lockwood, C.; Jordan, Z. What kind of systematic review should i conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC Med Res. Methodol. 2018, 18, 5. [CrossRef] [PubMed]
14. Elamir, H. Improving patient flow through applying lean concepts to emergency department. Lead. Health Serv. 2018, 31, 293–309. [CrossRef]
15. Rotteau, L.; Webster, F.; Salkeld, E.; Hellings, C.; Guttmann, A.; Vermeulen, M.J.; Bell, R.S.; Zwarenstein, M.; Rowe, B.H.; Nigam, A.; et al. Ontario’s emergency department process improvement program: The experience of implementation. Acad. Emerg. Med. 2015, 22, 720–729. [CrossRef] [PubMed]
16. Cheng, I.; Zwarenstein, M.; Kiss, A.; Castren, M.; Brommels, M.; Schull, M. Factors associated with failure of emergency wait-time targets for high acuity discharges and intensive care unit admissions. Can. J. Emerg. Med. 2018, 20, 112–124. [CrossRef]
17. Ashour, O.M.; Okudan Kremer, G.E. Dynamic patient grouping and prioritization: A new approach to emergency department flow improvement. Health Care Manag. Sci. 2016, 19, 192–205. [CrossRef]
18. Feng, Y.; Wu, I.; Chen, T. Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Health Care Manag. Sci. 2017, 20, 55–75. [CrossRef] [PubMed]
19. Bellew, S.D.; Collins, S.P.; Barrett, T.W.; Russ, S.E.; Jones, I.D.; Slovis, C.M.; Self, W.H. Implementation of an Opioid Detoxification Management Pathway Reduces Emergency Department Length of Stay. Acad. Emerg. Med. 2018, 25, 1157–1163. [CrossRef] [PubMed]
20. Yousefi, M.; Ferreira, R.P.M. An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department. Braz. J. Med. Biol. Res. 2017, 50, e5955. [CrossRef]
21. Nezamoddini, N.; Khasawneh, M.T. Modeling and optimization of resources in multi-emergency department settings with patient transfer. Oper. Res. Health Care 2016, 10, 23–34. [CrossRef]
22. Bish, P.A.; McCormick, M.A.; Otegbeye, M. Ready-JET-Go: Split Flow Accelerates ED Throughput. J. Emerg. Nurs. 2016, 42, 114–119. [CrossRef]
23. Blick, K.E. Providing critical laboratory results on time, every time to help reduce emergency department length of stay: How our laboratory achieved a six sigma level of performance. Am. J. Clin. Pathol. 2013, 140, 193–202. [CrossRef]
24. Azadeh, A.; Rouhollah, F.; Davoudpour, F.; Mohammadfam, I. Fuzzy modelling and simulation of an emergency department for improvement of nursing schedules with noisy and uncertain inputs. Int. J. Serv. Oper. Manag. 2013, 15, 58–77. [CrossRef]
25. Acuna, J.A.; Zayas-Castro, J.L.; Charkhgard, H. Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida. Socio. Econ. Plan. Sci. 2020, 71, 100747. [CrossRef]
26. Sorrentino, P. Use of failure mode and effects analysis to improve emergency department handoff processes. Clin. Nurse Spec. 2016, 30, 28–37. [CrossRef] [PubMed]
27. Saghafian, S.; Austin, G.; Traub, S.J. Operations research/management contributions to emergency department patient flow optimization: Review and research prospects. IIE Trans. Healthc. Syst. Eng. 2015, 5, 101–123. [CrossRef]
28. Effective Practice and Organisation of Care Group. EPOC Website. Available online: https://epoc.cochrane.org/resources/epocresources-review-authors (accessed on 2 August 2021).
29. Silal, S.P. Operational research: A multidisciplinary approach for the management of infectious disease in a global context. Eur. J. Oper. Res. 2021, 291, 929–934. [CrossRef] [PubMed]
30. Gibbs, N.; Kwon, J.; Balen, J.; Dodd, P.J. Operational research to support equitable non-communicable disease policy in lowincome and middle-income countries in the sustainable development era: A scoping review. BMJ Glob. Health 2020, 5, e002259. [CrossRef] [PubMed]
31. Ortiz-Barrios, M.; Alfaro-Saiz, J. A hybrid fuzzy multi-criteria decision-making model to evaluate the overall performance of public emergency departments: A case study. Int. J. Inf. Technol. Decis. Mak. 2020, 19, 1485–1548. [CrossRef]
32. Mosadeghrad, A.M. Developing and validating a total quality management model for healthcare organisations. TQM J. 2015, 27, 544–564. [CrossRef]
33. Gambella, C.; Ghaddar, B.; Naoum-Sawaya, J. Optimization Problems for Machine Learning: A Survey. Eur. J. Oper. Res. 2021, 290, 807–828. [CrossRef]
34. Borges Do Nascimento, I.J.; Marcolino, M.S.; Abdulazeem, H.M.; Weerasekara, I.; Azzopardi-Muscat, N.; Goncalves, M.A.; Novillo-Ortiz, D. Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies. J. Med Internet Res. 2021, 23, e27275. [CrossRef]
35. Retzlaff, K.J. COVID-19 Emergency Management Structure and Protocols. AORN J. 2020, 112, 197–203. [CrossRef]
36. Sangal, R.B.; Scofi, J.E.; Parwani, V.; Pickens, A.T.; Ulrich, A.; Venkatesh, A.K. Less Social Emergency Departments: Implementation of Workplace Contact Reduction during COVID-19. Emerg. Med. J. 2020, 37, 463–466. [CrossRef]
37. Ballini, L.; Negro, A.; Maltoni, S.; Vignatelli, L.; Flodgren, G.; Simera, I.; Grilli, R. Interventions to reduce waiting times for elective procedures. Cochrane Database Syst. Rev. 2015, CD005610. [CrossRef]
38. Haidich, A.B. Meta-analysis in medical research. Hippokratia 2010, 14, 29. [PubMed]
39. Abadi, M.Q.H.; Rahmati, S.; Sharifi, A.; Ahmadi, M. HSSAGA: Designation and Scheduling of Nurses for Taking Care of COVID-19 Patients Using Novel Method of Hybrid Salp Swarm Algorithm and Genetic Algorithm. Appl. Soft Comput. 2021, 108, 107449. [CrossRef] [PubMed]
40. AbdelAziz, A.M.; Alarabi, L.; Basalamah, S.; Hendawi, A. A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients. Algorithms 2021, 14, 38. [CrossRef]
41. Aggarwal, L.; Goswami, P.; Sachdeva, S. Multi-Criterion Intelligent Decision Support System for COVID-19. Appl. Soft Comput. 2021, 101, 107056. [CrossRef]
42. Albahri, A.S.; Hamid, R.A.; Albahri, O.S.; Zaidan, A.A. Detection-Based Prioritisation: Framework of Multi-Laboratory Characteristics for Asymptomatic COVID-19 Carriers Based on Integrated Entropy–TOPSIS Methods. Artif. Intell. Med. 2021, 111, 101983. [CrossRef]
43. Alfaro-Martínez, J.J.; Calbo Mayo, J.; Molina Cifuentes, M.; Abizanda Soler, P.; Guillén Martínez, S.; Rodríguez Marín, Y.; Sirvent Segovia, A.E.; Nuñez Ares, A.; Alcaraz Barcelona, M.; Paterna Mellinas, G.; et al. Generation and Validation of In-Hospital Mortality Prediction Score in COVID-19 Patients: Alba-Score. Curr. Med Res. Opin. 2021, 37, 719–726. [CrossRef]
44. Angeli, E.; Dalto, S.; Marchese, S.; Setti, L.; Bonacina, M.; Galli, F.; Rulli, E.; Torri, V.; Monti, C.; Meroni, R.; et al. Prognostic Value of CT Integrated with Clinical and Laboratory Data during the First Peak of the COVID-19 Pandemic in Northern Italy: A Nomogram to Predict Unfavorable Outcome. Eur. J. Radiol. 2021, 137, 109612. [CrossRef]
45. Araz, O.M.; Ramirez-Nafarrate, A.; Jehn, M.; Wilson, F.A. The Importance of Widespread Testing for COVID-19 Pandemic: Systems Thinking for Drive-through Testing Sites. Health Syst. 2020, 9, 119–123. [CrossRef] [PubMed]
46. Assaf, D.; Gutman, Y.; Neuman, Y.; Segal, G.; Amit, S.; Gefen-Halevi, S.; Shilo, N.; Epstein, A.; Mor-Cohen, R.; Biber, A.; et al. Utilization of Machine-Learning Models to Accurately Predict the Risk for Critical COVID-19. Intern. Emerg. Med. 2020, 15, 1435–1443. [CrossRef]
47. Balbi, M.; Caroli, A.; Corsi, A.; Milanese, G.; Surace, A.; Di Marco, F.; Novelli, L.; Silva, M.; Lorini, F.L.; Duca, A.; et al. Chest X-Ray for Predicting Mortality and the Need for Ventilatory Support in COVID-19 Patients Presenting to the Emergency Department. Eur. Radiol. 2020, 31, 1999–2012. [CrossRef] [PubMed]
48. Balmaks, R.; Gramatniece, A.; Vilde, A.; ¯ L, ul,l,a, M.; Dumpis, U.; Gross, I.; Šlezi ¯ n, a, I. A Simulation-Based Failure Mode Analysis of SARS-CoV-2 Infection Control and Prevention in Emergency Departments. Simul. Healthc. 2020, in press. [CrossRef] [PubMed]
49. Brendish, N.J.; Poole, S.; Naidu, V.V.; Mansbridge, C.T.; Norton, N.J.; Wheeler, H.; Presland, L.; Kidd, S.; Cortes, N.J.; Borca, F.; et al. Clinical Impact of Molecular Point-of-Care Testing for Suspected COVID-19 in Hospital (COV-19POC): A Prospective, Interventional, Non-Randomised, Controlled Study. Lancet Respir. Med. 2020, 8, 1192–1200. [CrossRef]
50. Bolourani, S.; Brenner, M.; Wang, P.; McGinn, T.; Hirsch, J.S.; Barnaby, D.; Zanos, T.P.; Barish, M.; Cohen, S.L.; Coppa, K.; et al. A Machine Learning Prediction Model of Respiratory Failure within 48 Hours of Patient Admission for COVID-19: Model Development and Validation. J. Med Internet Res. 2021, 23, e24246. [CrossRef] [PubMed]
51. Carlile, M.; Hurt, B.; Hsiao, A.; Hogarth, M.; Longhurst, C.A.; Dameff, C. Deployment of Artificial Intelligence for Radiographic Diagnosis of COVID-19 Pneumonia in the Emergency Department. J. Am. Coll. Emerg. Physicians Open 2020, 1, 1459–1464. [CrossRef]
52. Casiraghi, E.; Malchiodi, D.; Trucco, G.; Frasca, M.; Cappelletti, L.; Fontana, T.; Esposito, A.A.; Avola, E.; Jachetti, A.; Reese, J.; et al. Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE Access 2020, 8, 196299–196325. [CrossRef]
53. Chen, T.; Ma, X.; Zhou, S.; Wang, H.; Pan, Y.; Chen, L.; Lv, H.; Lu, Y. Establishing a Standardized FUO Emergency Department: Design and Practice in Dealing with COVID-19. Ann. Transl. Med. 2020, 8, 749. [CrossRef]
54. Chopra, Z.; Holmes, A.R.; Nelson, J.R.; Hirschl, J.R.; Perkins, S.J.; Fung, C.; Medlin, R.P.; Korley, F.K. 63 Incidence and Determinants of COVID-19 Emergency Department Revisits. Ann. Emerg. Med. 2020, 76, S25. [CrossRef]
55. Chou, E.H.; Wang, C.H.; Hsieh, Y.L.; Namazi, B.; Wolfshohl, J.; Bhakta, T.; Tsai, C.L.; Lien, W.C.; Sankaranarayanan, G.; Lee, C.C.; et al. Clinical Features of Emergency Department Patients from Early COVID-19 Pandemic That Predict SARS-CoV-2 Infection: Machine-Learning Approach. Western J. Emerg. Med. 2021, 22, 244–251. [CrossRef] [PubMed]
56. Diep, A.N.; Gilbert, A.; Saegerman, C.; Gangolf, M.; D’Orio, V.; Ghuysen, A.; Donneau, A.-F. Development and Validation of a Predictive Model to Determine the Level of Care in Patients Confirmed with COVID-19. Infect. Dis. 2021, 53, 590–599. [CrossRef] [PubMed]
57. De Moraes, A.F.; Miraglia, J.L.; Rizzi-Donato, T.H.; Porto-Chiavegatto-Filho, A. COVID-19 diagnosis prediction in emergency care patients: A machine learning approach. medRxiv 2020, 20052092. [CrossRef]
58. De Nardo, P.; Gentilotti, E.; Mazzaferri, F.; Cremonini, E.; Hansen, P.; Goossens, H.; Tacconelli, E. Multi-Criteria Decision Analysis to Prioritize Hospital Admission of Patients Affected by COVID-19 in Low-Resource Settings with Hospital-Bed Shortage. Int. J. Infect. Dis. 2020, 98, 494–500. [CrossRef] [PubMed]
59. Esposito, A.; Palmisano, A.; Cao, R.; Rancoita, P.; Landoni, G.; Grippaldi, D.; Boccia, E.; Cosenza, M.; Messina, A.; la Marca, S.; et al. Quantitative Assessment of Lung Involvement on Chest CT at Admission: Impact on Hypoxia and Outcome in COVID-19 Patients. Clin. Imaging 2021, 77, 194–201. [CrossRef]
60. Feng, C.; Wang, L.; Chen, X.; Zhai, Y.; Zhu, F.; Chen, H.; Wang, Y.; Su, X.; Huang, S.; Tian, L.; et al. A Novel Artificial IntelligenceAssisted Triage Tool to Aid in the Diagnosis of Suspected COVID-19 Pneumonia Cases in Fever Clinics. Ann. Transl. Med. 2021, 9, 201. [CrossRef]
61. Freund, Y.; Drogrey, M.; Miró, Ò.; Marra, A.; Féral-Pierssens, A.L.; Penaloza, A.; Hernandez, B.A.L.; Beaune, S.; Gorlicki, J.; Vaittinada Ayar, P.; et al. Association Between Pulmonary Embolism and COVID-19 in Emergency Department Patients Undergoing Computed Tomography Pulmonary Angiogram: The PEPCOV International Retrospective Study. Acad. Emerg. Med. 2020, 27, 811–820. [CrossRef]
62. Garbey, M.; Joerger, G.; Furr, S.; Fikfak, V. A Model of Workflow in the Hospital during a Pandemic to Assist Management. PLoS ONE 2020, 15, e0242183. [CrossRef]
63. García de Guadiana-Romualdo, L.; Calvo Nieves, M.D.; Rodríguez Mulero, M.D.; Calcerrada Alises, I.; Hernández Olivo, M.; Trapiello Fernández, W.; González Morales, M.; Bolado Jiménez, C.; Albaladejo-Otón, M.D.; Fernández Ovalle, H.; et al. MR-ProADM as Marker of Endotheliitis Predicts COVID-19 Severity. European. J. Clin. Investig. 2021, 51, e13511. [CrossRef]
64. Gavelli, F.; Castello, L.M.; Bellan, M.; Azzolina, D.; Hayden, E.; Beltrame, M.; Galbiati, A.; Gardino, C.A.; Gastaldello, M.L.; Giolitti, F.; et al. Clinical Stability and In-Hospital Mortality Prediction in COVID-19 Patients Presenting to the Emergency Department. Minerva Med. 2020, 112, 118–123. [CrossRef]
65. Goodacre, S.; Thomas, B.; Sutton, L.; Burnsall, M.; Lee, E.; Bradburn, M.; Loban, A.; Waterhouse, S.; Simmonds, R.; Biggs, K.; et al. Derivation and Validation of a Clinical Severity Score for Acutely Ill Adults with Suspected COVID-19: The PRIEST Observational Cohort Study. PLoS ONE 2021, 16, e0245840. [CrossRef] [PubMed]
66. Gordon, W.J.; Henderson, D.; Desharone, A.; Fisher, H.N.; Judge, J.; Levine, D.M.; MacLean, L.; Sousa, D.; Su, M.Y.; Boxer, R. Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients. Appl. Clin. Inform. 2020, 11, 792–801. [CrossRef]
67. Haddad, Y.; Salonitis, K.; Emmanouilidis, C. Design of Emergency Response Manufacturing Networks: A Decision-Making Framework. Procedia CIRP 2020, 96, 151–156. [CrossRef]
68. Heldt, F.S.; Vizcaychipi, M.P.; Peacock, S.; Cinelli, M.; McLachlan, L.; Andreotti, F.; Jovanovi´c, S.; Dürichen, R.; Lipunova, N.; Fletcher, R.A.; et al. Early Risk Assessment for COVID-19 Patients from Emergency Department Data Using Machine Learning. Sci. Rep. 2021, 11, 4200. [CrossRef]
69. Joshi, R.P.; Pejaver, V.; Hammarlund, N.E.; Sung, H.; Lee, S.K.; Furmanchuk, A.; Lee, H.Y.; Scott, G.; Gombar, S.; Shah, N.; et al. A Predictive Tool for Identification of SARS-CoV-2 PCR-Negative Emergency Department Patients Using Routine Test Results. J. Clin. Virol. 2020, 129, 104502. [CrossRef] [PubMed]
70. Kim, C.; Yeo, I.H.; Kim, J.K.; Cho, Y.; Lee, M.J.; Jung, H.; Cho, J.W.; Ham, J.Y.; Lee, S.H.; Chung, H.S.; et al. Confirmation of COVID-19 in Out-of-Hospital Cardiac Arrest Patients and Postmortem Management in the Emergency Department during the COVID-19 Outbreak. Infect. Chemother. 2020, 52, 572. [CrossRef]
71. Kirby, J.J.; Shaikh, S.; Bryant, D.P.; Ho, A.F.; d’Etienne, J.P.; Schrader, C.D.; Wang, H. A Simplified Comorbidity Evaluation Predicting Clinical Outcomes Among Patients with Coronavirus Disease 2019. J. Clin. Med. Res. 2021, 13, 237–244. [CrossRef]
72. Kline, J.A.; Camargo, C.A.; Courtney, D.M.; Kabrhel, C.; Nordenholz, K.E.; Aufderheide, T.; Baugh, J.J.; Beiser, D.G.; Bennett, C.L.; Bledsoe, J.; et al. Clinical Prediction Rule for SARS-CoV-2 Infection from 116 U.S. Emergency Departments. PLoS ONE 2021, 16, e0248438. [CrossRef]
73. Lancet, E.A.; Gonzalez, D.; Alexandrou, N.A.; Zabar, B.; Lai, P.H.; Hall, C.B.; Braun, J.; Zeig-Owens, R.; Isaacs, D.; Ben-Eli, D.; et al. Prehospital Hypoxemia, Measured by Pulse Oximetry, Predicts Hospital Outcomes during the New York City COVID-19 Pandemic. J. Am. Coll. Emerg. Physicians 2021, 2, e12407. [CrossRef]
74. Levine, D.M.; Lipsitz, S.R.; Co, Z.; Song, W.; Dykes, P.C.; Samal, L. Derivation of a Clinical Risk Score to Predict 14-Day Occurrence of Hypoxia, ICU Admission, and Death Among Patients with Coronavirus Disease 2019. J. Gen. Intern. Med. 2020, 36, 730–737. [CrossRef] [PubMed]
75. Liu, P.Y.; Tsai, Y.S.; Chen, P.L.; Tsai, H.P.; Hsu, L.W.; Wang, C.S.; Lee, N.Y.; Huang, M.S.; Wu, Y.C.; Ko, W.C.; et al. Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients with SARS-CoV-2 at a Smart Quarantine Station: Observational Study. J. Med Internet Res. 2020, 22, e19878. [CrossRef] [PubMed]
76. McDonald, S.A.; Medford, R.J.; Basit, M.A.; Diercks, D.B.; Courtney, D.M. Derivation with Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients. Acad. Emerg. Med. 2021, 28, 206–214. [CrossRef] [PubMed]
77. Mehrotra, S.; Rahimian, H.; Barah, M.; Luo, F.; Schantz, K. A Model of Supply-chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19. Nav. Res. Logist. (NRL) 2020, 67, 303–320. [CrossRef]
78. Mitchell, R.; McKup, J.J.; Bue, O.; Nou, G.; Taumomoa, J.; Banks, C.; O’Reilly, G.; Kandelyo, S.; Bornstein, S.; Cole, T.; et al. Implementation of a Novel Three-Tier Triage Tool in Papua New Guinea: A Model for Resource-Limited Emergency Departments. Lancet Reg. Health West. Pac. 2020, 5, 100051. [CrossRef]
79. Möckel, M.; Stegemann, M.; Burst, V.; Kümpers, P.; Risse, J.; Koehler, F.; Schunk, D.; Hitzek, J.; Dietrich, T.; Slagman, A. Which parameters support disposition decision in suspected COVID-19 cases in the emergency department (ED): A German clinical cohort study. BMJ Open 2021, 11, 044853. [CrossRef]
80. Moss, R.; Wood, J.; Brow, D.; Shearer, F.; Black, A.J.; Cheng, A.C.; McCare, J.M.; McVernon, J. Modelling the impact of COVID-19 in Australia to inform transmission reducing measures and health system preparedness. medRxiv 2020, 20056184. [CrossRef]
81. Nepomuceno, T.C.C.; Silva, W.M.N.; Nepomuceno, K.T.C.; Barros, I.K.F. A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak. J. Healthc. Eng. 2020, 2020, 8857553. [CrossRef]
82. Nguyen, Y.; Corre, F.; Honsel, V.; Curac, S.; Zarrouk, V.; Burtz, C.P.; Weiss, E.; Moyer, J.D.; Gauss, T.; Grégory, J.; et al. A Nomogram to Predict the Risk of Unfavourable Outcome in COVID-19: A Retrospective Cohort of 279 Hospitalized Patients in Paris Area. Ann. Med. 2020, 52, 367–375. [CrossRef] [PubMed]
83. O0Reilly, G.M.; Mitchell, R.D.; Noonan, M.P.; Hiller, R.; Mitra, B.; Brichko, L.; Luckhoff, C.; Paton, A.; Smit, D.V.; Santamaria, M.J.; et al. Informing Emergency Care for COVID-19 Patients: The COVID-19 Emergency Department Quality Improvement Project Protocol. EMA Emerg. Med. Australas. 2020, 32, 511–514. [CrossRef]
84. Parker, F.; Sawczuk, H.; Ganjkhanloo, F.; Ahmadi, F.; Ghobadi, K. Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19. arXiv 2020, arXiv:2011.03528.
85. Peng, Q.; Yang, J.; Strome, T.; Weldon, E.; Chochinov, A. Bottleneck Detection and Reduction Using Simulation Modeling to Reduce Overcrowding of Hospital Emergency Department. J. Modeling Optim. 2020, 12, 100–109. [CrossRef]
86. Plante, T.B.; Blau, A.M.; Berg, A.N.; Weinberg, A.S.; Jun, I.C.; Tapson, V.F.; Kanigan, T.S.; Adib, A.B. Development and External Validation of a Machine Learning Tool to Rule out COVID-19 among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study. J. Med Internet Res. 2020, 22, e24048. [CrossRef] [PubMed]
87. Romero-Gameros, C.A.; Colin-Martínez, T.; Waizel-Haiat, S.; Vargas-Ortega, G.; Ferat-Osorio, E.; Guerrero-Paz, J.A.; Intriago-Alor, M.; López-Moreno, M.A.; Cuevas-García, C.F.; Mendoza-Zubieta, V.; et al. Diagnostic Accuracy of Symptoms as a Diagnostic Tool for SARS-CoV 2 Infection: A Cross-Sectional Study in a Cohort of 2173 Patients. BMC Infect. Dis. 2021, 21, 255. [CrossRef]
88. Saegerman, C.; Gilbert, A.; Donneau, A.-F.; Gangolf, M.; Diep, A.N.; Meex, C.; Bontems, S.; Hayette, M.-P.; D0Orio, V.; Ghuysen, A. Clinical Decision Support Tool for Diagnosis of COVID-19 in Hospitals. PLoS ONE 2021, 16, e0247773. [CrossRef]
89. Shamout, F.E.; Shen, Y.; Wu, N.; Kaku, A.; Park, J.; Makino, T.; Jastrz ˛ebski, S.; Witowski, J.; Wang, D.; Zhang, B.; et al. An Artificial Intelligence System for Predicting the Deterioration of COVID-19 Patients in the Emergency Department. arXiv 2020, arXiv:2008.01774.
90. Sherren, P.B.; Camporota, L.; Sanderson, B.; Jones, A.; Shankar-Hari, M.; Meadows, C.I.; Barrett, N.; Ostermann, M.; Hart, N. Outcomes of Critically Ill COVID-19 Patients Managed in a High-Volume Severe Respiratory Failure and ECMO Centre in the United Kingdom. J. Intensive Care Soc. 2020, 175114372097885. [CrossRef]
91. Suh, E.H.; Bodnar, D.J.; Melville, L.D.; Sharma, M.; Farmer, B.M. Crisis Clinical Pathway for COVID-19. Emerg. Med. J. 2020, 37, 700–704. [CrossRef] [PubMed]
92. Sung, J.; Choudry, N.; Bachour, R. Development and Validation of a Simple Risk Score for Diagnosing Covid-19 in the Emergency Room. Epidemiol. Infect. 2020, 148, e273. [CrossRef]
93. Tang, S.; McDonald, S.; Furmaga, J.; Piel, C.; Courtney, M.; Diercks, D.; Rojas Cordova, A. 185 Data-Driven Staffing DecisionMaking at an Emergency Department in Response to COVID-19. Ann. Emerg. Med. 2020, 76, S71–S72. [CrossRef]
94. Teklewold, B.; Anteneh, D.; Kebede, D.; Gezahegn, W. Use of Failure Mode and Effect Analysis to Reduce Admission of Asymptomatic COVID-19 Patients to the Adult Emergency Department: An Institutional Experience. Risk Manag. Healthc. Policy 2021, 14, 273–282. [CrossRef] [PubMed]
95. Van Klaveren, D.; Rekkas, A.; Alsma, J.; Verdonschot, R.J.; Koning, D.T.; Kamps, M.J.; Dormans, T.; Stassen, R.; Weijer, S.; Arnold, K.-S.; et al. COVID Outcome Prediction in the Emergency Department (COPE): Development and Validation of a Model for Predicting Death and Need for Intensive Care in COVID-19 Patients. medRxiv 2021, 20249023. [CrossRef]
96. Van Singer, M.; Brahier, T.; Ngai, M.; Wright, J.; Weckman, A.M.; Erice, C.; Meuwly, J.Y.; Hugli, O.; Kain, K.C.; Boillat-Blanco, N. COVID-19 Risk Stratification Algorithms Based on STREM-1 and IL-6 in Emergency Department. J. Allergy Clin. Immunol. 2021, 147, 99–106.e4. [CrossRef] [PubMed]
97. Wang, A.Z.; Ehrman, R.; Bucca, A.; Croft, A.; Glober, N.; Holt, D.; Lardaro, T.; Musey, P.; Peterson, K.; Schaffer, J.; et al. Can We Predict Which COVID-19 Patients Will Need Transfer to Intensive Care within 24 Hours of Floor Admission? Acad. Emerg. Med 2021, 28, 511–518. [CrossRef] [PubMed]
98. Zeinalnezhad, M.; Chofreh, A.G.; Goni, F.A.; Klemeš, J.J.; Sari, E. Simulation and Improvement of Patients’ Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets. Int. J. Environ. Res. Public Health 2020, 17, 8577. [CrossRef] [PubMed]
99. Zhang, W.; Zhang, C.; Bi, Y.; Yuan, L.; Jiang, Y.; Hasi, C.; Zhang, X.; Kong, X. Analysis of COVID-19 Epidemic and Clinical Risk Factors of Patients under Epidemiological Markov Model. Results Phys. 2021, 22, 103881. [CrossRef] [PubMed]
100. Zhang, Y.; Cheng, S.R. Evaluating the Need for Routine COVID-19 Testing of Emergency Department Staff: Quantitative Analysis. JMIR Public Health Surveill. 2020, 6, e20260. [CrossRef]
101. Zhou, W.; Liu, Y.; Xu, B.; Wang, S.; Li, S.; Liu, H.; Huang, Z.; Luo, Y.; Hu, M.; Wu, W.; et al. Early Identification of Patients with Severe COVID-19 at Increased Risk of in-Hospital Death: A Multicenter Case-Control Study in Wuhan. J. Thorac. Dis. 2021, 13, 1380–1395. [CrossRef]
102. Medford-Davis, L.; Marcozzi, D.; Agrawal, S.; Carr, B.G.; Carrier, E. Value-based approaches for emergency care in a new era. Ann. Emerg. Med. 2017, 69, 675–683. [CrossRef]
103. Rocha, T.A.H.; da Silva, N.C.; Amaral, P.V.; Barbosa, A.C.Q.; Rocha, J.V.M.; Alvares, V.; Facchini, L.A. Access to emergency care services: A transversal ecological study about brazilian emergency health care network. Public Health 2017, 153, 9–15. [CrossRef]
104. Ortiz-Barrios, M.; Alfaro-Saiz, J.J. An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector. PLoS ONE 2020, 15, e0234984. [CrossRef]
105. Lemos, D.R.Q.; D’angelo, S.M.; Farias, L.A.B.G.; Almeida, M.M.; Gomes, R.G.; Pinto, G.P.; Cavalcanti, L.P.G. Health system collapse 45 days after the detection of COVID-19 in ceará, northeast brazil: A preliminary analysis. Rev. Soc. Bras. Med. Trop. 2020, 53, 1–6. [CrossRef]
106. Thornton, J. Covid-19: A&E visits in england fall by 25% in week after lockdown. BMJ (Clin. Res. Ed.) 2020, 369, m1401. [CrossRef]
107. Perry, R.; Banaras, A.; Werring, D.J.; Simister, R. What has caused the fall in stroke admissions during the COVID-19 pandemic? J. Neurol. 2020, 267, 3457–3458. [CrossRef] [PubMed]
108. Hartnett, K.P.; Kite-Powell, A.; DeVies, J.; Coletta, M.A.; Boehmer, T.K.; Adjemian, J.; Gundlapalli, A.V. National Syndromic Surveillance Program Community of Practice. Impact of the COVID-19 Pandemic on Emergency Department Visits—United States, January1, 2019–May 30, 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 699–704. [CrossRef]
109. Pashazadeh, A.; Navimipour, N.J. Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review. J. Biomed. Inform. 2018, 82, 47–62. [CrossRef] [PubMed]
110. Romero-Conrado, A.R.; Castro-Bolaño, L.J.; Montoya-Torres, J.R.; Jiménez-Barros, M.Á. Operations research as a decision-making tool in the health sector: A state of the art. [La utilización de la investigación de operaciones como soporte a la toma de decisiones en el sector salud: Un estado del arte]. DYNA 2017, 84, 129–137. [CrossRef]
111. Sun, C.; Hong, S.; Song, M.; Li, H.; Wang, Z. Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning. BMC Med Inform. Decis. Mak. 2021, 21, 45. [CrossRef]
112. Apornak, A. Human resources allocation in the hospital emergency department during COVID-19 pandemic. Int. J. Healthc. Manag. 2021, 14, 264–270. [CrossRef]
113. Emanuel, E.J.; Persad, G.; Upshur, R.; Thome, B.; Parker, M.; Glickman, A.; Phillips, J.P. Fair allocation of scarce medical resources in the time of covid-19. New Engl. J. Med. 2020, 382, 2049–2055. [CrossRef]
114. Dinh, M.M.; Berendsen Russell, S. Overcrowding kills: How COVID-19 could reshape emergency department patient flow in the new normal. EMA Emerg. Med. Australas. 2021, 33, 175–177. [CrossRef]
115. Ortiz-Barrios, M.; Pancardo, P.; Jiménez-Delgado, G.; De Ávila-Villalobos, J. Applying multi-phase DES approach for modelling the patient journey through accident and emergency departments. In Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2019; Volume 11582. [CrossRef]
116. 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. In Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2017; Volume 10586. [CrossRef]
117. Artiga-Sainz, L.M.; Sarria-Santamera, A.; Martínez-Alés, G.; Quintana-Díaz, M. New approach to managing COVID-19 pandemic in a complex tertiary care medical centre in madrid, spain. Disaster Med. Public Health Prep. 2021, 110, 220–228. [CrossRef]
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv International Journal of Environmental Research and Public Health
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://www.mdpi.com/1660-4601/18/16/8814
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/95f09497-54b1-444f-8763-84d1c422060b/download
https://repositorio.cuc.edu.co/bitstreams/71d68985-a91a-43ac-9ec1-0a3f120afc9b/download
https://repositorio.cuc.edu.co/bitstreams/c7083a52-2ac9-4ee5-9ded-56d5b88b66df/download
https://repositorio.cuc.edu.co/bitstreams/cace826f-965f-4b96-bb83-96d89d42dedc/download
https://repositorio.cuc.edu.co/bitstreams/10096e2a-b6fe-44ac-8bab-cc6671063bd6/download
https://repositorio.cuc.edu.co/bitstreams/2967f28e-521d-468e-86cb-909c9f9635e0/download
bitstream.checksum.fl_str_mv 348f67812a7200610fb4b3f8999932b8
42fd4ad1e89814f5e4a476b409eb708c
e30e9215131d99561d40d6b0abbe9bad
3ee335a728195e39f30161fa8282aa32
3ee335a728195e39f30161fa8282aa32
1b3d94f9c65740dd1e01e503dfb2a8d2
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166843933655040
spelling Ortiz Barrios, Miguel AngelCoba Blanco, Dayana MilenaAlfaro-Saiz, Juan-JoseStand-González, Daniela2021-11-05T13:58:20Z2021-11-05T13:58:20Z20211660-46011661-7827https://hdl.handle.net/11323/8835https://doi.org/10.3390/ijerph18168814Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.Ortiz Barrios, Miguel Angel-will be generated-orcid-0000-0001-6890-7547-600Coba Blanco, Dayana Milena-will be generated-orcid-0000-0002-0395-8172-600Alfaro-Saiz, Juan-Jose-will be generated-orcid-0000-0003-2587-6853-600Stand-González, Danielaapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2International Journal of Environmental Research and Public Healthhttps://www.mdpi.com/1660-4601/18/16/8814HealthcareEmergency departmentCOVID-19Process improvementSystematic reviewProcess improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic reviewArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Anderson, R.M.; Heesterbeek, H.; Klinkenberg, D.; Hollingsworth, T.D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 2020, 395, 931–934. [CrossRef]2. Sun, X.; Wang, T.; Cai, D.; Hu, Z.; Liao, H.; Zhi, L.; Wei, H.; Zhang, Z.; Qiu, Y.; Wang, J.; et al. Cytokine storm intervention in the early stages of COVID-19 pneumonia. Cytokine Growth Factor Rev. 2020, 53, 38–42. [CrossRef]3. Walker, P.G.T.; Whittaker, C.; Watson, O.J.; Baguelin, M.; Winskill, P.; Hamlet, A.; Ghani, A.C. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 2020, 369, 413–422. [CrossRef]4. World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/ (accessed on 21 May 2021).5. Singer, D.R. A new pandemic out of China: The Wuhan 2019-nCoV coronavirus syndrome. Health Policy Technol. 2020, 9, 1. [CrossRef]6. 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]7. World Health Organization. Coronavirus disease 2019 (COVID-19) Situation Report-93. 2020. Available online: https://www. who.int/docs/default-source/coronaviruse/situation-reports/20200422-sitrep-93-covid-19.pdf?sfvrsn=35cf80d7_4 (accessed on 16 August 2021).8. Mareiniss, D.P. The impending storm: COVID-19, pandemics and our overwhelmed emergency departments. Am. J. Emerg. Med. 2020, 38, 1293–1294. [CrossRef]9. 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. In Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2019; Volume 11582. [CrossRef]10. Singh, R.P.; Javaid, M.; Haleem, A.; Suman, R. Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 521–524. [CrossRef]11. Hundal, G.S.; Thiyagarajan, S.; Alduraibi, M.; Laux, C.M.; Furterer, S.L.; Cudney, E.A.; Antony, J. Lean Six Sigma as an organizational resilience mechanism in health care during the era of COVID-19. Int. J. Lean Six Sigma 2021, in press. [CrossRef]12. Gupta, S.; Starr, M.K.; Farahani, R.Z.; Asgari, N. Pandemics/epidemics: Challenges and opportunities for operations management research. Manuf. Serv. Oper. Manag. 2020, in press.13. Munn, Z.; Stern, C.; Aromataris, E.; Lockwood, C.; Jordan, Z. What kind of systematic review should i conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC Med Res. Methodol. 2018, 18, 5. [CrossRef] [PubMed]14. Elamir, H. Improving patient flow through applying lean concepts to emergency department. Lead. Health Serv. 2018, 31, 293–309. [CrossRef]15. Rotteau, L.; Webster, F.; Salkeld, E.; Hellings, C.; Guttmann, A.; Vermeulen, M.J.; Bell, R.S.; Zwarenstein, M.; Rowe, B.H.; Nigam, A.; et al. Ontario’s emergency department process improvement program: The experience of implementation. Acad. Emerg. Med. 2015, 22, 720–729. [CrossRef] [PubMed]16. Cheng, I.; Zwarenstein, M.; Kiss, A.; Castren, M.; Brommels, M.; Schull, M. Factors associated with failure of emergency wait-time targets for high acuity discharges and intensive care unit admissions. Can. J. Emerg. Med. 2018, 20, 112–124. [CrossRef]17. Ashour, O.M.; Okudan Kremer, G.E. Dynamic patient grouping and prioritization: A new approach to emergency department flow improvement. Health Care Manag. Sci. 2016, 19, 192–205. [CrossRef]18. Feng, Y.; Wu, I.; Chen, T. Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Health Care Manag. Sci. 2017, 20, 55–75. [CrossRef] [PubMed]19. Bellew, S.D.; Collins, S.P.; Barrett, T.W.; Russ, S.E.; Jones, I.D.; Slovis, C.M.; Self, W.H. Implementation of an Opioid Detoxification Management Pathway Reduces Emergency Department Length of Stay. Acad. Emerg. Med. 2018, 25, 1157–1163. [CrossRef] [PubMed]20. Yousefi, M.; Ferreira, R.P.M. An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department. Braz. J. Med. Biol. Res. 2017, 50, e5955. [CrossRef]21. Nezamoddini, N.; Khasawneh, M.T. Modeling and optimization of resources in multi-emergency department settings with patient transfer. Oper. Res. Health Care 2016, 10, 23–34. [CrossRef]22. Bish, P.A.; McCormick, M.A.; Otegbeye, M. Ready-JET-Go: Split Flow Accelerates ED Throughput. J. Emerg. Nurs. 2016, 42, 114–119. [CrossRef]23. Blick, K.E. Providing critical laboratory results on time, every time to help reduce emergency department length of stay: How our laboratory achieved a six sigma level of performance. Am. J. Clin. Pathol. 2013, 140, 193–202. [CrossRef]24. Azadeh, A.; Rouhollah, F.; Davoudpour, F.; Mohammadfam, I. Fuzzy modelling and simulation of an emergency department for improvement of nursing schedules with noisy and uncertain inputs. Int. J. Serv. Oper. Manag. 2013, 15, 58–77. [CrossRef]25. Acuna, J.A.; Zayas-Castro, J.L.; Charkhgard, H. Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida. Socio. Econ. Plan. Sci. 2020, 71, 100747. [CrossRef]26. Sorrentino, P. Use of failure mode and effects analysis to improve emergency department handoff processes. Clin. Nurse Spec. 2016, 30, 28–37. [CrossRef] [PubMed]27. Saghafian, S.; Austin, G.; Traub, S.J. Operations research/management contributions to emergency department patient flow optimization: Review and research prospects. IIE Trans. Healthc. Syst. Eng. 2015, 5, 101–123. [CrossRef]28. Effective Practice and Organisation of Care Group. EPOC Website. Available online: https://epoc.cochrane.org/resources/epocresources-review-authors (accessed on 2 August 2021).29. Silal, S.P. Operational research: A multidisciplinary approach for the management of infectious disease in a global context. Eur. J. Oper. Res. 2021, 291, 929–934. [CrossRef] [PubMed]30. Gibbs, N.; Kwon, J.; Balen, J.; Dodd, P.J. Operational research to support equitable non-communicable disease policy in lowincome and middle-income countries in the sustainable development era: A scoping review. BMJ Glob. Health 2020, 5, e002259. [CrossRef] [PubMed]31. Ortiz-Barrios, M.; Alfaro-Saiz, J. A hybrid fuzzy multi-criteria decision-making model to evaluate the overall performance of public emergency departments: A case study. Int. J. Inf. Technol. Decis. Mak. 2020, 19, 1485–1548. [CrossRef]32. Mosadeghrad, A.M. Developing and validating a total quality management model for healthcare organisations. TQM J. 2015, 27, 544–564. [CrossRef]33. Gambella, C.; Ghaddar, B.; Naoum-Sawaya, J. Optimization Problems for Machine Learning: A Survey. Eur. J. Oper. Res. 2021, 290, 807–828. [CrossRef]34. Borges Do Nascimento, I.J.; Marcolino, M.S.; Abdulazeem, H.M.; Weerasekara, I.; Azzopardi-Muscat, N.; Goncalves, M.A.; Novillo-Ortiz, D. Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies. J. Med Internet Res. 2021, 23, e27275. [CrossRef]35. Retzlaff, K.J. COVID-19 Emergency Management Structure and Protocols. AORN J. 2020, 112, 197–203. [CrossRef]36. Sangal, R.B.; Scofi, J.E.; Parwani, V.; Pickens, A.T.; Ulrich, A.; Venkatesh, A.K. Less Social Emergency Departments: Implementation of Workplace Contact Reduction during COVID-19. Emerg. Med. J. 2020, 37, 463–466. [CrossRef]37. Ballini, L.; Negro, A.; Maltoni, S.; Vignatelli, L.; Flodgren, G.; Simera, I.; Grilli, R. Interventions to reduce waiting times for elective procedures. Cochrane Database Syst. Rev. 2015, CD005610. [CrossRef]38. Haidich, A.B. Meta-analysis in medical research. Hippokratia 2010, 14, 29. [PubMed]39. Abadi, M.Q.H.; Rahmati, S.; Sharifi, A.; Ahmadi, M. HSSAGA: Designation and Scheduling of Nurses for Taking Care of COVID-19 Patients Using Novel Method of Hybrid Salp Swarm Algorithm and Genetic Algorithm. Appl. Soft Comput. 2021, 108, 107449. [CrossRef] [PubMed]40. AbdelAziz, A.M.; Alarabi, L.; Basalamah, S.; Hendawi, A. A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients. Algorithms 2021, 14, 38. [CrossRef]41. Aggarwal, L.; Goswami, P.; Sachdeva, S. Multi-Criterion Intelligent Decision Support System for COVID-19. Appl. Soft Comput. 2021, 101, 107056. [CrossRef]42. Albahri, A.S.; Hamid, R.A.; Albahri, O.S.; Zaidan, A.A. Detection-Based Prioritisation: Framework of Multi-Laboratory Characteristics for Asymptomatic COVID-19 Carriers Based on Integrated Entropy–TOPSIS Methods. Artif. Intell. Med. 2021, 111, 101983. [CrossRef]43. Alfaro-Martínez, J.J.; Calbo Mayo, J.; Molina Cifuentes, M.; Abizanda Soler, P.; Guillén Martínez, S.; Rodríguez Marín, Y.; Sirvent Segovia, A.E.; Nuñez Ares, A.; Alcaraz Barcelona, M.; Paterna Mellinas, G.; et al. Generation and Validation of In-Hospital Mortality Prediction Score in COVID-19 Patients: Alba-Score. Curr. Med Res. Opin. 2021, 37, 719–726. [CrossRef]44. Angeli, E.; Dalto, S.; Marchese, S.; Setti, L.; Bonacina, M.; Galli, F.; Rulli, E.; Torri, V.; Monti, C.; Meroni, R.; et al. Prognostic Value of CT Integrated with Clinical and Laboratory Data during the First Peak of the COVID-19 Pandemic in Northern Italy: A Nomogram to Predict Unfavorable Outcome. Eur. J. Radiol. 2021, 137, 109612. [CrossRef]45. Araz, O.M.; Ramirez-Nafarrate, A.; Jehn, M.; Wilson, F.A. The Importance of Widespread Testing for COVID-19 Pandemic: Systems Thinking for Drive-through Testing Sites. Health Syst. 2020, 9, 119–123. [CrossRef] [PubMed]46. Assaf, D.; Gutman, Y.; Neuman, Y.; Segal, G.; Amit, S.; Gefen-Halevi, S.; Shilo, N.; Epstein, A.; Mor-Cohen, R.; Biber, A.; et al. Utilization of Machine-Learning Models to Accurately Predict the Risk for Critical COVID-19. Intern. Emerg. Med. 2020, 15, 1435–1443. [CrossRef]47. Balbi, M.; Caroli, A.; Corsi, A.; Milanese, G.; Surace, A.; Di Marco, F.; Novelli, L.; Silva, M.; Lorini, F.L.; Duca, A.; et al. Chest X-Ray for Predicting Mortality and the Need for Ventilatory Support in COVID-19 Patients Presenting to the Emergency Department. Eur. Radiol. 2020, 31, 1999–2012. [CrossRef] [PubMed]48. Balmaks, R.; Gramatniece, A.; Vilde, A.; ¯ L, ul,l,a, M.; Dumpis, U.; Gross, I.; Šlezi ¯ n, a, I. A Simulation-Based Failure Mode Analysis of SARS-CoV-2 Infection Control and Prevention in Emergency Departments. Simul. Healthc. 2020, in press. [CrossRef] [PubMed]49. Brendish, N.J.; Poole, S.; Naidu, V.V.; Mansbridge, C.T.; Norton, N.J.; Wheeler, H.; Presland, L.; Kidd, S.; Cortes, N.J.; Borca, F.; et al. Clinical Impact of Molecular Point-of-Care Testing for Suspected COVID-19 in Hospital (COV-19POC): A Prospective, Interventional, Non-Randomised, Controlled Study. Lancet Respir. Med. 2020, 8, 1192–1200. [CrossRef]50. Bolourani, S.; Brenner, M.; Wang, P.; McGinn, T.; Hirsch, J.S.; Barnaby, D.; Zanos, T.P.; Barish, M.; Cohen, S.L.; Coppa, K.; et al. A Machine Learning Prediction Model of Respiratory Failure within 48 Hours of Patient Admission for COVID-19: Model Development and Validation. J. Med Internet Res. 2021, 23, e24246. [CrossRef] [PubMed]51. Carlile, M.; Hurt, B.; Hsiao, A.; Hogarth, M.; Longhurst, C.A.; Dameff, C. Deployment of Artificial Intelligence for Radiographic Diagnosis of COVID-19 Pneumonia in the Emergency Department. J. Am. Coll. Emerg. Physicians Open 2020, 1, 1459–1464. [CrossRef]52. Casiraghi, E.; Malchiodi, D.; Trucco, G.; Frasca, M.; Cappelletti, L.; Fontana, T.; Esposito, A.A.; Avola, E.; Jachetti, A.; Reese, J.; et al. Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE Access 2020, 8, 196299–196325. [CrossRef]53. Chen, T.; Ma, X.; Zhou, S.; Wang, H.; Pan, Y.; Chen, L.; Lv, H.; Lu, Y. Establishing a Standardized FUO Emergency Department: Design and Practice in Dealing with COVID-19. Ann. Transl. Med. 2020, 8, 749. [CrossRef]54. Chopra, Z.; Holmes, A.R.; Nelson, J.R.; Hirschl, J.R.; Perkins, S.J.; Fung, C.; Medlin, R.P.; Korley, F.K. 63 Incidence and Determinants of COVID-19 Emergency Department Revisits. Ann. Emerg. Med. 2020, 76, S25. [CrossRef]55. Chou, E.H.; Wang, C.H.; Hsieh, Y.L.; Namazi, B.; Wolfshohl, J.; Bhakta, T.; Tsai, C.L.; Lien, W.C.; Sankaranarayanan, G.; Lee, C.C.; et al. Clinical Features of Emergency Department Patients from Early COVID-19 Pandemic That Predict SARS-CoV-2 Infection: Machine-Learning Approach. Western J. Emerg. Med. 2021, 22, 244–251. [CrossRef] [PubMed]56. Diep, A.N.; Gilbert, A.; Saegerman, C.; Gangolf, M.; D’Orio, V.; Ghuysen, A.; Donneau, A.-F. Development and Validation of a Predictive Model to Determine the Level of Care in Patients Confirmed with COVID-19. Infect. Dis. 2021, 53, 590–599. [CrossRef] [PubMed]57. De Moraes, A.F.; Miraglia, J.L.; Rizzi-Donato, T.H.; Porto-Chiavegatto-Filho, A. COVID-19 diagnosis prediction in emergency care patients: A machine learning approach. medRxiv 2020, 20052092. [CrossRef]58. De Nardo, P.; Gentilotti, E.; Mazzaferri, F.; Cremonini, E.; Hansen, P.; Goossens, H.; Tacconelli, E. Multi-Criteria Decision Analysis to Prioritize Hospital Admission of Patients Affected by COVID-19 in Low-Resource Settings with Hospital-Bed Shortage. Int. J. Infect. Dis. 2020, 98, 494–500. [CrossRef] [PubMed]59. Esposito, A.; Palmisano, A.; Cao, R.; Rancoita, P.; Landoni, G.; Grippaldi, D.; Boccia, E.; Cosenza, M.; Messina, A.; la Marca, S.; et al. Quantitative Assessment of Lung Involvement on Chest CT at Admission: Impact on Hypoxia and Outcome in COVID-19 Patients. Clin. Imaging 2021, 77, 194–201. [CrossRef]60. Feng, C.; Wang, L.; Chen, X.; Zhai, Y.; Zhu, F.; Chen, H.; Wang, Y.; Su, X.; Huang, S.; Tian, L.; et al. A Novel Artificial IntelligenceAssisted Triage Tool to Aid in the Diagnosis of Suspected COVID-19 Pneumonia Cases in Fever Clinics. Ann. Transl. Med. 2021, 9, 201. [CrossRef]61. Freund, Y.; Drogrey, M.; Miró, Ò.; Marra, A.; Féral-Pierssens, A.L.; Penaloza, A.; Hernandez, B.A.L.; Beaune, S.; Gorlicki, J.; Vaittinada Ayar, P.; et al. Association Between Pulmonary Embolism and COVID-19 in Emergency Department Patients Undergoing Computed Tomography Pulmonary Angiogram: The PEPCOV International Retrospective Study. Acad. Emerg. Med. 2020, 27, 811–820. [CrossRef]62. Garbey, M.; Joerger, G.; Furr, S.; Fikfak, V. A Model of Workflow in the Hospital during a Pandemic to Assist Management. PLoS ONE 2020, 15, e0242183. [CrossRef]63. García de Guadiana-Romualdo, L.; Calvo Nieves, M.D.; Rodríguez Mulero, M.D.; Calcerrada Alises, I.; Hernández Olivo, M.; Trapiello Fernández, W.; González Morales, M.; Bolado Jiménez, C.; Albaladejo-Otón, M.D.; Fernández Ovalle, H.; et al. MR-ProADM as Marker of Endotheliitis Predicts COVID-19 Severity. European. J. Clin. Investig. 2021, 51, e13511. [CrossRef]64. Gavelli, F.; Castello, L.M.; Bellan, M.; Azzolina, D.; Hayden, E.; Beltrame, M.; Galbiati, A.; Gardino, C.A.; Gastaldello, M.L.; Giolitti, F.; et al. Clinical Stability and In-Hospital Mortality Prediction in COVID-19 Patients Presenting to the Emergency Department. Minerva Med. 2020, 112, 118–123. [CrossRef]65. Goodacre, S.; Thomas, B.; Sutton, L.; Burnsall, M.; Lee, E.; Bradburn, M.; Loban, A.; Waterhouse, S.; Simmonds, R.; Biggs, K.; et al. Derivation and Validation of a Clinical Severity Score for Acutely Ill Adults with Suspected COVID-19: The PRIEST Observational Cohort Study. PLoS ONE 2021, 16, e0245840. [CrossRef] [PubMed]66. Gordon, W.J.; Henderson, D.; Desharone, A.; Fisher, H.N.; Judge, J.; Levine, D.M.; MacLean, L.; Sousa, D.; Su, M.Y.; Boxer, R. Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients. Appl. Clin. Inform. 2020, 11, 792–801. [CrossRef]67. Haddad, Y.; Salonitis, K.; Emmanouilidis, C. Design of Emergency Response Manufacturing Networks: A Decision-Making Framework. Procedia CIRP 2020, 96, 151–156. [CrossRef]68. Heldt, F.S.; Vizcaychipi, M.P.; Peacock, S.; Cinelli, M.; McLachlan, L.; Andreotti, F.; Jovanovi´c, S.; Dürichen, R.; Lipunova, N.; Fletcher, R.A.; et al. Early Risk Assessment for COVID-19 Patients from Emergency Department Data Using Machine Learning. Sci. Rep. 2021, 11, 4200. [CrossRef]69. Joshi, R.P.; Pejaver, V.; Hammarlund, N.E.; Sung, H.; Lee, S.K.; Furmanchuk, A.; Lee, H.Y.; Scott, G.; Gombar, S.; Shah, N.; et al. A Predictive Tool for Identification of SARS-CoV-2 PCR-Negative Emergency Department Patients Using Routine Test Results. J. Clin. Virol. 2020, 129, 104502. [CrossRef] [PubMed]70. Kim, C.; Yeo, I.H.; Kim, J.K.; Cho, Y.; Lee, M.J.; Jung, H.; Cho, J.W.; Ham, J.Y.; Lee, S.H.; Chung, H.S.; et al. Confirmation of COVID-19 in Out-of-Hospital Cardiac Arrest Patients and Postmortem Management in the Emergency Department during the COVID-19 Outbreak. Infect. Chemother. 2020, 52, 572. [CrossRef]71. Kirby, J.J.; Shaikh, S.; Bryant, D.P.; Ho, A.F.; d’Etienne, J.P.; Schrader, C.D.; Wang, H. A Simplified Comorbidity Evaluation Predicting Clinical Outcomes Among Patients with Coronavirus Disease 2019. J. Clin. Med. Res. 2021, 13, 237–244. [CrossRef]72. Kline, J.A.; Camargo, C.A.; Courtney, D.M.; Kabrhel, C.; Nordenholz, K.E.; Aufderheide, T.; Baugh, J.J.; Beiser, D.G.; Bennett, C.L.; Bledsoe, J.; et al. Clinical Prediction Rule for SARS-CoV-2 Infection from 116 U.S. Emergency Departments. PLoS ONE 2021, 16, e0248438. [CrossRef]73. Lancet, E.A.; Gonzalez, D.; Alexandrou, N.A.; Zabar, B.; Lai, P.H.; Hall, C.B.; Braun, J.; Zeig-Owens, R.; Isaacs, D.; Ben-Eli, D.; et al. Prehospital Hypoxemia, Measured by Pulse Oximetry, Predicts Hospital Outcomes during the New York City COVID-19 Pandemic. J. Am. Coll. Emerg. Physicians 2021, 2, e12407. [CrossRef]74. Levine, D.M.; Lipsitz, S.R.; Co, Z.; Song, W.; Dykes, P.C.; Samal, L. Derivation of a Clinical Risk Score to Predict 14-Day Occurrence of Hypoxia, ICU Admission, and Death Among Patients with Coronavirus Disease 2019. J. Gen. Intern. Med. 2020, 36, 730–737. [CrossRef] [PubMed]75. Liu, P.Y.; Tsai, Y.S.; Chen, P.L.; Tsai, H.P.; Hsu, L.W.; Wang, C.S.; Lee, N.Y.; Huang, M.S.; Wu, Y.C.; Ko, W.C.; et al. Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients with SARS-CoV-2 at a Smart Quarantine Station: Observational Study. J. Med Internet Res. 2020, 22, e19878. [CrossRef] [PubMed]76. McDonald, S.A.; Medford, R.J.; Basit, M.A.; Diercks, D.B.; Courtney, D.M. Derivation with Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients. Acad. Emerg. Med. 2021, 28, 206–214. [CrossRef] [PubMed]77. Mehrotra, S.; Rahimian, H.; Barah, M.; Luo, F.; Schantz, K. A Model of Supply-chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19. Nav. Res. Logist. (NRL) 2020, 67, 303–320. [CrossRef]78. Mitchell, R.; McKup, J.J.; Bue, O.; Nou, G.; Taumomoa, J.; Banks, C.; O’Reilly, G.; Kandelyo, S.; Bornstein, S.; Cole, T.; et al. Implementation of a Novel Three-Tier Triage Tool in Papua New Guinea: A Model for Resource-Limited Emergency Departments. Lancet Reg. Health West. Pac. 2020, 5, 100051. [CrossRef]79. Möckel, M.; Stegemann, M.; Burst, V.; Kümpers, P.; Risse, J.; Koehler, F.; Schunk, D.; Hitzek, J.; Dietrich, T.; Slagman, A. Which parameters support disposition decision in suspected COVID-19 cases in the emergency department (ED): A German clinical cohort study. BMJ Open 2021, 11, 044853. [CrossRef]80. Moss, R.; Wood, J.; Brow, D.; Shearer, F.; Black, A.J.; Cheng, A.C.; McCare, J.M.; McVernon, J. Modelling the impact of COVID-19 in Australia to inform transmission reducing measures and health system preparedness. medRxiv 2020, 20056184. [CrossRef]81. Nepomuceno, T.C.C.; Silva, W.M.N.; Nepomuceno, K.T.C.; Barros, I.K.F. A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak. J. Healthc. Eng. 2020, 2020, 8857553. [CrossRef]82. Nguyen, Y.; Corre, F.; Honsel, V.; Curac, S.; Zarrouk, V.; Burtz, C.P.; Weiss, E.; Moyer, J.D.; Gauss, T.; Grégory, J.; et al. A Nomogram to Predict the Risk of Unfavourable Outcome in COVID-19: A Retrospective Cohort of 279 Hospitalized Patients in Paris Area. Ann. Med. 2020, 52, 367–375. [CrossRef] [PubMed]83. O0Reilly, G.M.; Mitchell, R.D.; Noonan, M.P.; Hiller, R.; Mitra, B.; Brichko, L.; Luckhoff, C.; Paton, A.; Smit, D.V.; Santamaria, M.J.; et al. Informing Emergency Care for COVID-19 Patients: The COVID-19 Emergency Department Quality Improvement Project Protocol. EMA Emerg. Med. Australas. 2020, 32, 511–514. [CrossRef]84. Parker, F.; Sawczuk, H.; Ganjkhanloo, F.; Ahmadi, F.; Ghobadi, K. Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19. arXiv 2020, arXiv:2011.03528.85. Peng, Q.; Yang, J.; Strome, T.; Weldon, E.; Chochinov, A. Bottleneck Detection and Reduction Using Simulation Modeling to Reduce Overcrowding of Hospital Emergency Department. J. Modeling Optim. 2020, 12, 100–109. [CrossRef]86. Plante, T.B.; Blau, A.M.; Berg, A.N.; Weinberg, A.S.; Jun, I.C.; Tapson, V.F.; Kanigan, T.S.; Adib, A.B. Development and External Validation of a Machine Learning Tool to Rule out COVID-19 among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study. J. Med Internet Res. 2020, 22, e24048. [CrossRef] [PubMed]87. Romero-Gameros, C.A.; Colin-Martínez, T.; Waizel-Haiat, S.; Vargas-Ortega, G.; Ferat-Osorio, E.; Guerrero-Paz, J.A.; Intriago-Alor, M.; López-Moreno, M.A.; Cuevas-García, C.F.; Mendoza-Zubieta, V.; et al. Diagnostic Accuracy of Symptoms as a Diagnostic Tool for SARS-CoV 2 Infection: A Cross-Sectional Study in a Cohort of 2173 Patients. BMC Infect. Dis. 2021, 21, 255. [CrossRef]88. Saegerman, C.; Gilbert, A.; Donneau, A.-F.; Gangolf, M.; Diep, A.N.; Meex, C.; Bontems, S.; Hayette, M.-P.; D0Orio, V.; Ghuysen, A. Clinical Decision Support Tool for Diagnosis of COVID-19 in Hospitals. PLoS ONE 2021, 16, e0247773. [CrossRef]89. Shamout, F.E.; Shen, Y.; Wu, N.; Kaku, A.; Park, J.; Makino, T.; Jastrz ˛ebski, S.; Witowski, J.; Wang, D.; Zhang, B.; et al. An Artificial Intelligence System for Predicting the Deterioration of COVID-19 Patients in the Emergency Department. arXiv 2020, arXiv:2008.01774.90. Sherren, P.B.; Camporota, L.; Sanderson, B.; Jones, A.; Shankar-Hari, M.; Meadows, C.I.; Barrett, N.; Ostermann, M.; Hart, N. Outcomes of Critically Ill COVID-19 Patients Managed in a High-Volume Severe Respiratory Failure and ECMO Centre in the United Kingdom. J. Intensive Care Soc. 2020, 175114372097885. [CrossRef]91. Suh, E.H.; Bodnar, D.J.; Melville, L.D.; Sharma, M.; Farmer, B.M. Crisis Clinical Pathway for COVID-19. Emerg. Med. J. 2020, 37, 700–704. [CrossRef] [PubMed]92. Sung, J.; Choudry, N.; Bachour, R. Development and Validation of a Simple Risk Score for Diagnosing Covid-19 in the Emergency Room. Epidemiol. Infect. 2020, 148, e273. [CrossRef]93. Tang, S.; McDonald, S.; Furmaga, J.; Piel, C.; Courtney, M.; Diercks, D.; Rojas Cordova, A. 185 Data-Driven Staffing DecisionMaking at an Emergency Department in Response to COVID-19. Ann. Emerg. Med. 2020, 76, S71–S72. [CrossRef]94. Teklewold, B.; Anteneh, D.; Kebede, D.; Gezahegn, W. Use of Failure Mode and Effect Analysis to Reduce Admission of Asymptomatic COVID-19 Patients to the Adult Emergency Department: An Institutional Experience. Risk Manag. Healthc. Policy 2021, 14, 273–282. [CrossRef] [PubMed]95. Van Klaveren, D.; Rekkas, A.; Alsma, J.; Verdonschot, R.J.; Koning, D.T.; Kamps, M.J.; Dormans, T.; Stassen, R.; Weijer, S.; Arnold, K.-S.; et al. COVID Outcome Prediction in the Emergency Department (COPE): Development and Validation of a Model for Predicting Death and Need for Intensive Care in COVID-19 Patients. medRxiv 2021, 20249023. [CrossRef]96. Van Singer, M.; Brahier, T.; Ngai, M.; Wright, J.; Weckman, A.M.; Erice, C.; Meuwly, J.Y.; Hugli, O.; Kain, K.C.; Boillat-Blanco, N. COVID-19 Risk Stratification Algorithms Based on STREM-1 and IL-6 in Emergency Department. J. Allergy Clin. Immunol. 2021, 147, 99–106.e4. [CrossRef] [PubMed]97. Wang, A.Z.; Ehrman, R.; Bucca, A.; Croft, A.; Glober, N.; Holt, D.; Lardaro, T.; Musey, P.; Peterson, K.; Schaffer, J.; et al. Can We Predict Which COVID-19 Patients Will Need Transfer to Intensive Care within 24 Hours of Floor Admission? Acad. Emerg. Med 2021, 28, 511–518. [CrossRef] [PubMed]98. Zeinalnezhad, M.; Chofreh, A.G.; Goni, F.A.; Klemeš, J.J.; Sari, E. Simulation and Improvement of Patients’ Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets. Int. J. Environ. Res. Public Health 2020, 17, 8577. [CrossRef] [PubMed]99. Zhang, W.; Zhang, C.; Bi, Y.; Yuan, L.; Jiang, Y.; Hasi, C.; Zhang, X.; Kong, X. Analysis of COVID-19 Epidemic and Clinical Risk Factors of Patients under Epidemiological Markov Model. Results Phys. 2021, 22, 103881. [CrossRef] [PubMed]100. Zhang, Y.; Cheng, S.R. Evaluating the Need for Routine COVID-19 Testing of Emergency Department Staff: Quantitative Analysis. JMIR Public Health Surveill. 2020, 6, e20260. [CrossRef]101. Zhou, W.; Liu, Y.; Xu, B.; Wang, S.; Li, S.; Liu, H.; Huang, Z.; Luo, Y.; Hu, M.; Wu, W.; et al. Early Identification of Patients with Severe COVID-19 at Increased Risk of in-Hospital Death: A Multicenter Case-Control Study in Wuhan. J. Thorac. Dis. 2021, 13, 1380–1395. [CrossRef]102. Medford-Davis, L.; Marcozzi, D.; Agrawal, S.; Carr, B.G.; Carrier, E. Value-based approaches for emergency care in a new era. Ann. Emerg. Med. 2017, 69, 675–683. [CrossRef]103. Rocha, T.A.H.; da Silva, N.C.; Amaral, P.V.; Barbosa, A.C.Q.; Rocha, J.V.M.; Alvares, V.; Facchini, L.A. Access to emergency care services: A transversal ecological study about brazilian emergency health care network. Public Health 2017, 153, 9–15. [CrossRef]104. Ortiz-Barrios, M.; Alfaro-Saiz, J.J. An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector. PLoS ONE 2020, 15, e0234984. [CrossRef]105. Lemos, D.R.Q.; D’angelo, S.M.; Farias, L.A.B.G.; Almeida, M.M.; Gomes, R.G.; Pinto, G.P.; Cavalcanti, L.P.G. Health system collapse 45 days after the detection of COVID-19 in ceará, northeast brazil: A preliminary analysis. Rev. Soc. Bras. Med. Trop. 2020, 53, 1–6. [CrossRef]106. Thornton, J. Covid-19: A&E visits in england fall by 25% in week after lockdown. BMJ (Clin. Res. Ed.) 2020, 369, m1401. [CrossRef]107. Perry, R.; Banaras, A.; Werring, D.J.; Simister, R. What has caused the fall in stroke admissions during the COVID-19 pandemic? J. Neurol. 2020, 267, 3457–3458. [CrossRef] [PubMed]108. Hartnett, K.P.; Kite-Powell, A.; DeVies, J.; Coletta, M.A.; Boehmer, T.K.; Adjemian, J.; Gundlapalli, A.V. National Syndromic Surveillance Program Community of Practice. Impact of the COVID-19 Pandemic on Emergency Department Visits—United States, January1, 2019–May 30, 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 699–704. [CrossRef]109. Pashazadeh, A.; Navimipour, N.J. Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review. J. Biomed. Inform. 2018, 82, 47–62. [CrossRef] [PubMed]110. Romero-Conrado, A.R.; Castro-Bolaño, L.J.; Montoya-Torres, J.R.; Jiménez-Barros, M.Á. Operations research as a decision-making tool in the health sector: A state of the art. [La utilización de la investigación de operaciones como soporte a la toma de decisiones en el sector salud: Un estado del arte]. DYNA 2017, 84, 129–137. [CrossRef]111. Sun, C.; Hong, S.; Song, M.; Li, H.; Wang, Z. Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning. BMC Med Inform. Decis. Mak. 2021, 21, 45. [CrossRef]112. Apornak, A. Human resources allocation in the hospital emergency department during COVID-19 pandemic. Int. J. Healthc. Manag. 2021, 14, 264–270. [CrossRef]113. Emanuel, E.J.; Persad, G.; Upshur, R.; Thome, B.; Parker, M.; Glickman, A.; Phillips, J.P. Fair allocation of scarce medical resources in the time of covid-19. New Engl. J. Med. 2020, 382, 2049–2055. [CrossRef]114. Dinh, M.M.; Berendsen Russell, S. Overcrowding kills: How COVID-19 could reshape emergency department patient flow in the new normal. EMA Emerg. Med. Australas. 2021, 33, 175–177. [CrossRef]115. Ortiz-Barrios, M.; Pancardo, P.; Jiménez-Delgado, G.; De Ávila-Villalobos, J. Applying multi-phase DES approach for modelling the patient journey through accident and emergency departments. In Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2019; Volume 11582. [CrossRef]116. 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. In Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2017; Volume 10586. [CrossRef]117. Artiga-Sainz, L.M.; Sarria-Santamera, A.; Martínez-Alés, G.; Quintana-Díaz, M. New approach to managing COVID-19 pandemic in a complex tertiary care medical centre in madrid, spain. Disaster Med. Public Health Prep. 2021, 110, 220–228. [CrossRef]PublicationORIGINALProcess Improvement Approaches for Increasing the Response.pdfProcess Improvement Approaches for Increasing the Response.pdfapplication/pdf3361923https://repositorio.cuc.edu.co/bitstreams/95f09497-54b1-444f-8763-84d1c422060b/download348f67812a7200610fb4b3f8999932b8MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/71d68985-a91a-43ac-9ec1-0a3f120afc9b/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/c7083a52-2ac9-4ee5-9ded-56d5b88b66df/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILProcess Improvement Approaches for Increasing the Response.pdf.jpgProcess Improvement Approaches for Increasing the Response.pdf.jpgimage/jpeg75547https://repositorio.cuc.edu.co/bitstreams/cace826f-965f-4b96-bb83-96d89d42dedc/download3ee335a728195e39f30161fa8282aa32MD54THUMBNAILProcess Improvement Approaches for Increasing the Response.pdf.jpgProcess Improvement Approaches for Increasing the Response.pdf.jpgimage/jpeg75547https://repositorio.cuc.edu.co/bitstreams/10096e2a-b6fe-44ac-8bab-cc6671063bd6/download3ee335a728195e39f30161fa8282aa32MD54TEXTProcess Improvement Approaches for Increasing the Response.pdf.txtProcess Improvement Approaches for Increasing the Response.pdf.txttext/plain121180https://repositorio.cuc.edu.co/bitstreams/2967f28e-521d-468e-86cb-909c9f9635e0/download1b3d94f9c65740dd1e01e503dfb2a8d2MD5511323/8835oai:repositorio.cuc.edu.co:11323/88352024-09-17 14:17:08.725http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares