“Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England | Zendy

Hugo De Oliveira | Zendy; Martin Prodel | Zendy; Ludovic Lamarsalle | Zendy; Matthew Inada-Kim | Zendy; Kenny Ajayi | Zendy; J. Wilkins | Zendy; Sara Sekelj | Zendy; Sue Beecroft | Zendy; Sally K. Snow | Zendy; Ruth Slater | Zendy; Andi Orlowski | Zendy

Open Access

“Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England

Author(s) -

Hugo De Oliveira,

Martin Prodel,

Ludovic Lamarsalle,

Matthew Inada-Kim,

Kenny Ajayi,

J. Wilkins,

Sara Sekelj,

Sue Beecroft,

Sally K. Snow,

Ruth Slater,

Andi Orlowski

Publication year - 2020

Publication title -

jamia open

Language(s) - English

Resource type - Journals

ISSN - 2574-2531

DOI - 10.1093/jamiaopen/ooaa039

Subject(s) - sepsis , medicine , event (particle physics) , intensive care medicine , emergency medicine , quantum mechanics , physics

The "Bow-tie" optimal pathway discovery analysis uses large clinical event datasets to map clinical pathways and to visualize risks (improvement opportunities) before, and outcomes after, a specific clinical event. This proof-of-concept study assesses the use of NHS Hospital Episode Statistics (HES) in England as a potential clinical event dataset for this pathway discovery analysis approach.

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