This report presents the output of a week-long collaboration between the Alan Turing Institute, NHS Scotland, and the National Services Scotland’s Information Services Division (ISD) to investigate and update the current decision support tool for identifying patients at risk of admission - the SPARRA (Scottish Patients at Risk of Readmission and Admission) model
Unplanned readmissions are a popular factor to determine the quality of healthcare services that can...
OBJECTIVES: Emergency admission is associated with the potential for adverse events in older people ...
Abstract Data and information generated through the provision and administration of health and soci...
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the co...
Funding The receipt of financial support from the MRC National Preventive Research Initiative Phase ...
Funding Information: This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R00...
Before a meaningful evaluation of the performance of a hospital over any output or outcome can be ma...
ABSTRACT Objectives The use of “real-time” data to support individual patient management and outc...
Objective To determine the association between risk factors and hospital admission. Methods The ...
This study identifies factors associated with a high prevalence of healthcare‐associated infection (...
Background As the COVID-19 pandemic continues, national-level surveillance platforms with real-time ...
ObjectivesEvaluating whether future studies to develop prediction models for early readmissions base...
Introduction: COVID-19 is commonly experienced as an acute illness, yet some people continue to have...
ABSTRACT Background Several Risk Prediction models exist for predicting emergency admissions. One...
If patients at risk of admission or readmission to hospital or other forms of care could be identifi...
Unplanned readmissions are a popular factor to determine the quality of healthcare services that can...
OBJECTIVES: Emergency admission is associated with the potential for adverse events in older people ...
Abstract Data and information generated through the provision and administration of health and soci...
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the co...
Funding The receipt of financial support from the MRC National Preventive Research Initiative Phase ...
Funding Information: This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R00...
Before a meaningful evaluation of the performance of a hospital over any output or outcome can be ma...
ABSTRACT Objectives The use of “real-time” data to support individual patient management and outc...
Objective To determine the association between risk factors and hospital admission. Methods The ...
This study identifies factors associated with a high prevalence of healthcare‐associated infection (...
Background As the COVID-19 pandemic continues, national-level surveillance platforms with real-time ...
ObjectivesEvaluating whether future studies to develop prediction models for early readmissions base...
Introduction: COVID-19 is commonly experienced as an acute illness, yet some people continue to have...
ABSTRACT Background Several Risk Prediction models exist for predicting emergency admissions. One...
If patients at risk of admission or readmission to hospital or other forms of care could be identifi...
Unplanned readmissions are a popular factor to determine the quality of healthcare services that can...
OBJECTIVES: Emergency admission is associated with the potential for adverse events in older people ...
Abstract Data and information generated through the provision and administration of health and soci...