Lead: Kelvin Jordan
Dates: 01 October 2024 – 31 March 2026
Background:
When patients are admitted to hospital, a delayed transfer of care (DTOC) may occur where an inpatient is medically ready to go home but is still occupying a hospital bed. This may be due to many reasons; for example, they live alone and are too frail to look after themselves. Delays to discharge can have serious implications for that patient and for patients who are waiting for admission from emergency portals in the hospital.
University Hospitals of North Midlands (UHNM) NHS Trust have designed and implemented a high risk of delay transfers of care tool. The tool functions in the emergency department (ED) to aim to identify early those patients at risk of DTOC, enabling the relevant teams to promptly assess at risk patients and place them on the appropriate discharge pathways. UHNM tested the use of this tool and whilst it was successful in identifying those at highest risk, the tool was including too many “false-positives” i.e. patients deemed to be at high risk of DTOC by the tool but were actually found to be at lower risk once reviewed. The current tool is based only on limited information and UHNM are augmenting this information by linking to other data such as primary care and social care data via GRAPHNET.
Partners:
University Hospitals of North Midlands (UHNM) NHS Trust
Aims and Objectives:
Keele University’s School of Medicine Biostatistics team will use the linked anonymised data from UHNM to develop a new tool to help improve discharge planning for future patients. The objectives are to:
- assess feasibility of developing a prediction model for DTOC in people who are admitted after presenting to the emergency department at UHNM;
- develop and internally validate a prediction model for DTOC;
- develop and internally validate a prediction model for virtual ward use after discharge versus full discharge.