A data generator for covid-19 patients’ care requirements inside hospitals

Juan A. Marin-Garcia, Angel Ruiz, Maheut Julien, Jose P. Garcia-Sabater

Abstract

This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness’ stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic.

Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems.

Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. Conversely, plausible data sets can be generated from publicly available information and, later, when real data become available, the accuracy of the model can be evaluated (Garcia-Sabater and Maheut, 2021).

This work opens lines of future research, both theoretical and practical. From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. Regarding the lines of research applied, it is evident that the formalism proposed for the generation of sound patients is not limited to patients affected by SARS-CoV-2 infection. The generation of heterogeneous patients can represent the needs of a specific population and serve as a basis for studying complex health service delivery systems.

 

 


Keywords

data paper; simulated data set; covid-19; hospital; bed management; healthcare; operations management

Subject classification

SDG03 Good Health and Well-Being; SDG09 Industry, Innovation, and Infrastructure

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References

Alexander, G. L. (2007). The nurse-patient trajectory framework. Medinfo. MEDINFO, 12(Pt 2), 910- 914.

Belciug, S., Bejinariu, S. I., & Costin, H. (2020). An artificial immune system approach for a multicompartment queuing model for improving medical resources and inpatient bed occupancy in pandemics. Advances in Electrical and Computer Engineering, 20(3). https://doi.org/10.4316/AECE.2020.03003

BOE-A-2013-12887. (2013). Ley 19/2013, de 9 de diciembre, de transparencia acceso a la información pública y buen gobierno. 1-32.

Brochard, L. (2003). Mechanical ventilation: Invasive versus noninvasive. European Respiratory Journal, Supplement, 22(47), 31s-37s. https://doi.org/10.1183/09031936.03.00050403

Buckley, D., & Gillham, M. (2007). Invasive Respiratory Support. In Cardiothoracic Critical Care (pp. 419-436). Elsevier Inc. https://doi.org/10.1016/B978-075067572-7.50032-1

Casas-Rojo, J. M., Antón-Santos, J. M., Millán-Núñez-Cortés, J., Lumbreras-Bermejo, C., RamosRincón, J. M., Roy-Vallejo, E., Artero-Mora, A., Arnalich-Fernández, F., García-Bruñén, J. M., Vargas-Núñez, J. A., Freire-Castro, S. J., Manzano-Espinosa, L., Perales-Fraile, I., CresteloViéitez, A., Puchades-Gimeno, F., Rodilla-Sala, E., Solís-Marquínez, M. N., Bonet-Tur, D., Fidalgo-Moreno, M. P., … Gómez-Huelgas, R. (2020). Clinical characteristics of patients hospitalized with COVID-19 in Spain: Results from the SEMI-COVID-19 Registry. Revista Clinica Espanola, 220(8), 480-494. https://doi.org/10.1016/j.rce.2020.07.003

Castelnuovo, F., Marchese, V., Cristini, G., Crosato, V., Pennati, F., Renisi, G., Izzo, I., Paraninfo, G., Van Hauwermeiren, E., & Castelli, F. (2020). Discharge ward during the sars-cov-2 pandemic: An effective way to increase patient turnover when human resources are scarce. Infezioni in Medicina, 28(4), 539-544. https://doi.org/10.1007/s15010-020-01522-4

Celeux, G., Lavergne, C., Vernaz, Y., Celeux, G., Lavergne, C., Vernaz, Y., Material, A., & Censored, D. (2006). Assessing Material Aging from Doubly Censored Data : Weibull Distribution vs . Poisson Process To cite this version : HAL Id : inria-00072799 Assessing material aging from doubly censored data : Weibull distribution vs . Poisson process apport. [Research Report] RR-3857, INRIA. 2000. inria-00072799.

Claudio, D., Cosgriff, V., Nino, V., & Valladares, L. (2021). An Agile Standardized Work Procedure for Cleaning the Operating Room. Journal of Industrial Engineering and Management, 14, in press. https://doi.org/https://doi.org/jiem.3440

CNE -Centro Nacional de Epidemiología. (2020). Información científico-técnica, enfermedad por coronavirus, COVID-19 (actualizado 20201112).

Comtois, D. (2021). summarytools: Tools to Quickly and Neatly Summarize Data.

Corbin, J. M., & Strauss, A. L. (1988). Unending Work and Care: Managing Chronic Illness at Home. Jossey-Bass Inc.

Daniel, P., Mecklenburg, M., Massiah, C., Joseph, M. A., Wilson, C., Parmar, P., Rosengarten, S., Maini, R., Kim, J., Oomen, A., & Zehtabchi, S. (2021). Non-invasive positive pressure ventilation versus endotracheal intubation in treatment of COVID-19 patients requiring ventilatory support. American Journal of Emergency Medicine, 43, 103-108. https://doi.org/10.1016/j.ajem.2021.01.068

Dominguez-Lara, S. A. (2018). Odds-ratios and their interpretation as effect size in research. In Educacion Medica (Vol. 19, Issue 1, pp. 65-66). Fundacion Educacion Medica. https://doi.org/10.1016/j.edumed.2017.01.008

ECDP. (2020). Guidance for discharge and ending isolation in the context of widespread community transmission of COVID-19-first update Scope of this document. In European Centre for Disease Prevention (Issue April, pp. 1-8).

Epstein, R. H., & Dexter, F. (2020). A Predictive Model for Patient Census and Ventilator Requirements at Individual Hospitals During the Coronavirus Disease 2019 (COVID-19) Pandemic: A Preliminary Technical Report. Cureus. https://doi.org/10.7759/cureus.8501

European center for disease prevention and control. (2020). Coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK - seventh update. 2019 (March).

Fowler, R., Hatchette, T., Salvadori, M., Baclic, O., Volling, C., Murthy, S., Emeriaud, G., Money, D., Brooks, J., Decou, M., & Ofner, M. (2020). Clinical management of patients with COVID-19: Second interim guidance. Canadian Critical Care Society and Association of Medical Microbiology and Infectious Disease (AMMI) Canada, 1-67.

Garcia-Sabater, J. P., & Maheut, J. (2021). Introducción al Modelado Matematico, Nota Técnica. RiuNet. Repositorio Institucional UPV. https://doi.org/http://hdl.handle.net/10251/158555

Garcia-Sabater, J. P., Maheut, J., Ruiz, A., & Garcia-Sabater, J. J. (2020). A framework for capacity and operations planning in services organizations employing workers with intellectual disabilities. Sustainability (Switzerland), 12(22), 1-17. https://doi.org/10.3390/su12229713

Generalitat Valenciana. Conselleria de Sanitat Universal i Salut Pública. (2019). Memoria de gestión conselleria de sanitat universal i salut pública 2019. 14493-14496.

Generalitat Valenciana. (2018). Memoria de Gestión de la Conselleria de Sanitat Universal i Salut Pública.

Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708-1720. https://doi.org/10.1056/NEJMoa2002032

Gunal, M. M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17-25. https://doi.org/10.1057/hs.2012.8

Hair, J. F., Black, W. C., Babin, B., & Anderson, R. E. (2009). Multivariate data analysis (7th edition). Prentice Hall. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497- 506. https://doi.org/10.1016/S0140-6736(20)30183-5

Lagarda-Leyva, E. A., & Ruiz, A. (2019). A Systems Thinking Model to Support Long-Term Bearability of the Healthcare System: The Case of the Province of Quebec. Sustainability, 11(24), 7028. https://doi.org/10.3390/su11247028

Manninen, K. (2020). Typical progress of covid-19. Marin-Garcia, J. A. (2015). Publishing in two phases for focused research by means of "research collaborations." WPOM-Working Papers on Operations Management, 6(2), 76. https://doi.org/10.4995/wpom.v6i2.4459

Marin-Garcia, J. A., Bonavia, T., & Losilla, J.-M. (2020). Changes in the Association between European Workers' Employment Conditions and Employee Well-Being in 2005, 2010 and 2015. International Journal of Environmental Research and Public Health, 17(3), 1048. https://doi.org/10.3390/ijerph17031048

Marin-Garcia, J. A., Garcia-Sabater, J. P., Ruiz, A., Maheut, J., & Garcia-Sabater, J. J. (2020). Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak. Journal of Industrial Engineering and Management, 13(2), 213. https://doi.org/10.3926/jiem.3190

Marin-Garcia, J. A., Vidal-Carreras, P. I., Garcia Sabater, J. J., & Escribano-Martinez, J. (2019). Protocol: Value Stream Maping in Healthcare. A systematic literature review. WPOM-Working Papers on Operations Management, 10(2), 36. https://doi.org/10.4995/wpom.v10i2.12297

Ministerio De Sanidad, Servicios Sociales e Igualdad. (2017). Hábitos de Vida Informe Anual del Sistema Nacional de salud 2016 (INFORMES,). MINISTERIO DE SANIDAD, SERVICIOS SOCIALES E IGUALDAD.

Mun, J. (2008). Appendix C. Understanding and Choosing the Right Probability Distributions. Advanced Analytical Models: Over 800 Models and 300 Applications from the Basel II Accord to Wall Street and Beyond, 899-917. https://doi.org/10.1002/9781119197096.app03

Nino, V., Gomez, K., Martinez, K., & Claudio, D. (2021). Improving the registration process in a healthcare facility with lean principles. Journal of Industrial Engineering and Management, 14, in press. https://doi.org/https://doi.org/jiem.3432

Olivieri, A., Palù, G., & Sebastiani, G. (2021). COVID-19 cumulative incidence, intensive care, and mortality in Italian regions compared to selected European countries. International Journal of Infectious Diseases, 102. https://doi.org/10.1016/j.ijid.2020.10.070

Parlamento Europeo y del Consejo de la Unión Europea. (2019). Directiva

(UE) 2019/1024 DEL PARLAMENTO EUROPEO Y DEL CONSEJO de la Unión Europea de 20 de junio de 2019 relativa a los datos abiertos y la reutilización de la información del sector público (versión refundida). 172/56-172/78.

Petermann-Rocha, F., Hanlon, P., Gray, S. R., Welsh, P., Gill, J. M. R., Foster, H., Katikireddi, S. V., Lyall, D., Mackay, D. F., O'Donnell, C. A., Sattar, N., Nicholl, B. I., Pell, J. P., Jani, B. D., Ho, F. K., Mair, F. S., & Celis-Morales, C. (2020). Comparison of two different frailty measurements and risk of hospitalisation or death from COVID-19: findings from UK Biobank. BMC Medicine, 18(1). https://doi.org/10.1186/s12916-020-01822-4

Pinaire, J., Azé, J., Bringay, S., & Landais, P. (2017). Patient healthcare trajectory. An essential monitoring tool: a systematic review. Health Information Science and Systems, 5(1), 1-18. https://doi.org/10.1007/s13755-017-0020-2

Plaza, J. (2021). Informe Científico-Divulgativo: Un Año De Coronavirus Sars-Cov-2. Ministerio de Ciencia e Innovación.

Popat, B., & Jones, A. T. (2012). Invasive and non-invasive mechanical ventilation. In Medicine (United Kingdom) (Vol. 40, Issue 6, pp. 298-304). Elsevier Ltd. https://doi.org/10.1016/j.mpmed.2012.03.010

Posso, M., Comas, M., Román, M., Domingo, L., Louro, J., González, C., Sala, M., Anglès, A., Cirera, I., Cots, F., Frías, V.-M., Gea, J., Güerri-Fernández, R., Masclans, J. R., Noguès, X., Vázquez, O., Villar-García, J., Horcajada, J. P., Pascual, J., & Castells, X. (2020). Comorbidities and Mortality in Patients With COVID-19 Aged 60 Years and Older in a University Hospital in Spain. Archivos de Bronconeumología, 56(11), 756-758. https://doi.org/10.1016/j.arbres.2020.06.012

R Core Team. (2020). R: A Language and Environment for Statistical Computing. Revelle, W. (2021). psych: Procedures for Psychological, Psychometric, and Personality Research.

Roa-Martínez, S. M., Vidotti, S. A. B., & Santana, R. C. (2017). Estructura propuesta del artículo de datos como publicación científica. Revista Espanola de Documentacion Cientifica, 40(1), 1-12. https://doi.org/10.3989/redc.2017.1.1375

Romeo Casabona, C. M., & Urruela Mora, A. (2020). Informe Del Ministerio De Sanidad Sobre Los Aspectos Éticos En Situaciones De Pandemia: El Sars-Cov-2. 12.

RStudio Team. (2020). RStudio: Integrated Development for R. RStudio, PBC. Rubio-Rivas, M., Corbella, X., Mora-Luján, J. M., Loureiro-Amigo, J., López Sampalo, A., Yera Bergua, C., Esteve Atiénzar, P. J., Díez García, L. F., Gonzalez Ferrer, R., Plaza Canteli, S., Pérez Piñeiro, A., Cortés Rodríguez, B., Jorquer Vidal, L., Pérez Catalán, I., León Téllez, M., Martín Oterino, J. Á., Martín González, M. C., Serrano Carrillo de Albornoz, J. L., García Sardon, E., … GómezHuelgas, R. (2020). Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID19. Journal of Clinical Medicine, 9(11), 3488. https://doi.org/10.3390/jcm9113488

Ruckdeschel, P., Kohl, M., Stabla, T., & Camphausen, F. (2006). S4 Classes for Distributions. R News, 6(2), 2-6. Ruza, F. (2008). Cuidados Intensivos Pediatricos. 6(6), 336. Schauberger, P., & Walker, A. (2020). openxlsx: Read, Write and Edit xlsx Files.

Stang, A., Stang, M., & Jöckel, K. H. (2020). Estimated use of intensive care beds due to COVID-19 in Germany over time. Deutsches Arzteblatt International, 117(19). https://doi.org/10.3238/arztebl.2020.0329

Unroe, M., Kahn, J. M., Carson, S. S., Govert, J. A., Martinu, T., Sathy, S. J., Clay, A. S., Chia, J., Gray, A., Tulsky, J. A., & Cox, C. E. (2010). One-year trajectories of care and resource utilization for recipients of prolonged mechanical ventilation: A cohort study. Annals of Internal Medicine, 153(3), 167-175. https://doi.org/10.7326/0003-4819-153-3-201008030-00007

Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (Fourth). Springer. https://doi.org/10.1007/978-0-387-21706-2

Wang, Y., Wang, Y., Chen, Y., & Qin, Q. (2020). Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. In Journal of Medical Virology (Vol. 92, Issue 6, pp. 568-576). John Wiley and Sons Inc. https://doi.org/10.1002/jmv.25748

Wickham, H. (2007). Reshaping Data with the {reshape} Package. Journal of Statistical Software, 21(12), 1-20. https://doi.org/10.18637/jss.v021.i12

Wickham, H. (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. https://doi.org/10.18637/jss.v040.i01

Wiersema, U. F. (2007). Noninvasive Respiratory Support. In Cardiothoracic Critical Care (pp. 410- 418). Elsevier Inc. https://doi.org/10.1016/B978-075067572-7.50031-X

Winck, J. C., & Scala, R. (2021). Non-invasive respiratory support paths in hospitalized patients with COVID-19: proposal of an algorithm. Pulmonology. https://doi.org/10.1016/j.pulmoe.2020.12.005

Wong, G. N., Weiner, Z. J., Tkachenko, A. V., Elbanna, A., Maslov, S., & Goldenfeld, N. (2020). Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions. In medRxiv. https://doi.org/10.1101/2020.06.03.20120691

Wu, H., Godfrey, A. J. R., Govindaraju, K., & Pirikahu, S. (2020). ExtDist: Extending the Range of Functions for Probability Distributions.

Xia, W., & Sun, J. (2013). Simulation guided value stream mapping and lean improvement: A case study of a tubular machining facility. Journal of Industrial Engineering and Management, 6(2), 456-476. https://doi.org/10.3926/jiem.532

Xu, X. W., Wu, X. X., Jiang, X. G., Xu, K. J., Ying, L. J., Ma, C. L., Li, S. B., Wang, H. Y., Zhang, S., Gao, H. N., Sheng, J. F., Cai, H. L., Qiu, Y. Q., & Li, L. J. (2020). Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: Retrospective case series. The BMJ, 368. https://doi.org/10.1136/bmj.m606

Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., Ye, C., Zhang, P., Xing, Y., Guo, H., & Tang, W. (2020). Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. In Journal of Infection (Vol. 81, Issue 2, pp. e16- e25). W.B. Saunders Ltd. https://doi.org/10.1016/j.jinf.2020.04.021

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