SOSTENIBILIDAD Y MEJORA DE LA CALIDAD DE LOS SERVICIOS DE SALUD MEDIANTE LA SIMULACION Y OPTIMIZACION DE SISTEMAS COMPLEJOS

PID2020-114031RB-I00

Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal de I+D+i Orientada a los Retos de la Sociedad
Subprograma Programa Estatal de I+D+i Orientada a los Retos de la Sociedad
Convocatoria Proyectos I+D
Año convocatoria 2020
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020
Centro beneficiario UNIVERSIDAD PUBLICA DE NAVARRA
Identificador persistente http://dx.doi.org/10.13039/501100011033

Publicaciones

Found(s) 2 result(s)
Found(s) 1 page(s)

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

RUC. Repositorio da Universidade da Coruña
  • García-Vicuña, Daniel
  • López-Cheda, Ana
  • Jácome, M. A.
  • Mallor, Fermín
[Abstract] Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions., DGV and FM acknowledge the support by grant PID2020-114031RB-I00 (AEI, FEDER EU) and by the Government of Navarre, 0011-3597-2020-000003 (COVID). ALC was sponsored by the BEATRIZ GALINDO JUNIOR Spanish Grant from MICINN (Ministerio de Ciencia e Innovación) with code BGP18/00154. ALC and MAJ acknowledge partial support by the MICINN Grant PID2020-113578RB-I00 and partial support of Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14). ALC and MJ wish to acknowledge the support received from the Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia and the European Union European Regional Development Fund (ERDF)-Galicia 2014-2020 Program, by grant ED431G 2019/01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript, Gobierno de Navarra; 0011-3597-2020-000003 (COVID), Xunta de Galicia; ED431C-2020-14, Xunta de Galicia; ED431G 2019/01




Estimation of patient flow in hospitals using up-to-date data: application to bed demand prediction during pandemic waves

Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
  • García de Vicuña Bilbao, Daniel
  • López-Cheda, Ana
  • Jácome, María Amalia
  • 0000-0001-9800-1498
Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions., DGV and FM acknowledge the support by grant PID2020-114031RB-I00 (AEI, FEDER EU) and by the Government of Navarre, 0011-3597-2020-000003 (COVID). ALC was sponsored by the BEATRIZ GALINDO JUNIOR Spanish Grant from MICINN (Ministerio de Ciencia e Innovación) with code BGP18/00154. ALC and MAJ acknowledge partial support by the MICINN Grant PID2020-113578RB-I00 and partial support of Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14). ALC and MJ wish to acknowledge the support received from the Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia and the European Union European Regional Development Fund (ERDF)-Galicia 2014-2020 Program, by grant ED431G 2019/01.