ArticleScientific reports2022
Transition matrices model as a way to better understand and predict intra-hospital pathways of covid-19 patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A near real-time interactive dashboard for monitoring and anticipating demands in emergency care in the Île-de-France region (France).Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Persistency of catastrophic out-of-pocket health expenditures: Measurement with evidence from three African countries - Malawi, Tanzania, and Uganda.Social science & medicine (1982) · 2024Article
- A Markov network approach for reproducing purchase behaviours observed in convenience stores.Scientific reports · 2024Article
- Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy.Omega · 2023Article
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12 authors.
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Abstract
Since January 2020, the SARS-CoV-2 pandemic has severely affected hospital systems worldwide. In Europe, the first 3 epidemic waves (periods) have been the most severe in terms of number of infected and hospitalized patients. There are several descriptions of the demographic and clinical profiles of patients with COVID-19, but few studies of their hospital pathways. We used transition matrices, constructed from Markov chains, to illustrate the transition probabilities between different hospital wards for 90,834 patients between March 2020 and July 2021 managed in Paris area. We identified 3 epidemic periods (waves) during which the number of hospitalized patients was significantly high. Between the 3 periods, the main differences observed were: direct admission to ICU, from 14 to 18%, mortality from ICU, from 28 to 24%, length of stay (alive patients), from 9 to 7 days from CH and from 18 to 10 days from ICU. The proportion of patients transferred from CH to ICU remained stable. Understanding hospital pathways of patients is crucial to better monitor and anticipate the impact of SARS-CoV-2 pandemic on health system.
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