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Research Theme

Modernising Medical Microbiology and Big Infection Diagnostics

Home > Research Themes Overview > Modernising Medical Microbiology and Big Infection Diagnostics > Infections in Oxfordshire Research Database > IORD Projects > Emergency hospital readmissions: determinants, weekend effect and time-trend

IORD Project

Emergency hospital readmissions: determinants, weekend effect and time-trend

COMPLETED
IORD category: Electronic Health Records
Chief Investigator: Dr Julia Pakpoor
Sponsor: OUH
Research location: Oxford University
Approval date: 12 Oct 2017

Re-admissions are instances where patients are admitted to hospital within a short period of time after having been sent home from a previous hospital admission. Re-admissions have been thought to reflect poor hospital care, with a potential negative impact on patient health. They are expensive for hospitals, which also face financial penalties if re-admissions occur. Health-care leaders have tried to implement policy changes to reduce the burden of patient re-admissions on the health service. Further, characteristics about patients, such as diseases they have or their economic or ethnic background, as well as characteristics of hospitals such as how busy they are, have been studied to see if it is possible to predict which patients are more likely to require re-admission. Patients who are sent home on weekend compared to a weekday have also been proposed to have a higher re-admission risk. We aim to study the role of a range of patient and hospital characteristics on the risk of patient re-admission, with a particular focus on the impact of underlying infections, or risk of infection in patients, on the re-admission risk. We also aim to investigate whether the burden of re-admissions has been getting worse or better over recent years.

Modernising Medical Microbiology and Big Infection Diagnostics

  • Modernising Medical Microbiology and Big Infection Diagnostics
  • Sub-theme 1: Novel rapid, high-throughput diagnostic workflows for infection
  • Sub-theme 2: Big data-led infection diagnosis and management strategies
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