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

[EN] A Spanish version of the article is provided (see section before references). 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.[ES] En este trabajo se presenta cómo se ha generado un conjunto de datos verosímiles relacionados con las necesidades de pacientes covid-19 con síntomas severe or critical. Se considerarán las etapas posibles con los conocimientos médicos a fecha de enero de 2021. Los parámetros elegidos en este data set están personalizados para adecuarse a los valores poblacionales de la región de Valencia (Spain), unos 2.5 Millones de habitantes y la evolución de la pandemia entre los meses de septiembre 2020 y marzo 2021, un periodo de tiempo que contemple dos olas completas de pandemia.En contra de lo que cabría esperar, a pesar de la ley de transparencia europea y nacional (BOE-A-2013-12887, 2013; Parlamento Europeo y del Consejo de la Unión Europea, 2019), los datos reales relacionados con la pandemia covid-19, al menos en España, tardan mucho en actualizarse y estar disponibles (normalmente una semana o más días). Además, algunos datos relevantes para trabajar los modelos de gestión de camas de hospital no están accesibles públicamente. Bien porque no se hayan recogido esos datos, o porque los organismos públicos no los ofrecen (a pesar de tenerlos indexados en sus bases de datos), o los ofrecen camuflados en indicadores procesados y no muestran los datos en bruto, o simplemente los publican en un formato de difícil reutilización (por ejemplo, en documentos PDF en lugar de en tablas CSV). A pesar de que los sistemas de información de los hospitales son bastante potentes, siguen existiendo datos que ni siquiera están recogidos adecuadamente en el sistema de información de salud.Por otra parte, los datos recogidos en un hospital dependen de las estrategias y practicas propias de ese hospital o sistema de salud. Este efecto limita la generalización de los datos “reales” y es necesario trabajar con datos “realistas” o verosímiles que están limpios de interacciones con variables o decisiones locales (Gunal, 2012; Marin-Garcia et al., 2020). Por un lado, se puede parametrizar el modelo y definir la estructura de datos que sería necesaria para ejecutar el modelo con datos reales. Por otro lado, se pueden generar conjuntos de datos verosímiles a partir de la información pública disponible y, posteriormente, cuando se disponga de los datos reales evaluar la bondad del modelo (Garcia-Sabater & Maheut, 2021).Marin-Garcia, JA.; Ruiz, A.; Julien, M.; Garcia-Sabater, JP. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management. 12(1):76-115. https://doi.org/10.4995/wpom.1533276115121Alexander, 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. 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