Avoiding Attendance and Admission in Long Term Conditions 

NIHR Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber (CLAHRC YH)

'USING BIG DATA TO EVALUATE THE WHOLE EMERGENCY AND URGENT CARE SYSTEM FROM THE POINT OF CALL TO DISCHARGE'

Study contacts:

Project Manager: Colin O’Keeffe
Email: c.okeeffe@sheffield.ac.uk
Tel: 0114 222 0780

Project lead: Suzanne Mason
Email: s.mason@sheffield.ac.uk
Tel: 0114 222

Background:

Avoiding Attendances and Admissions is a theme within the Collaboration for Leadership in Applied Health Research and Care (CLAHRC) Yorkshire and Humber. The theme is a five-year programme of work (January 2014-December 2018) addressing avoidable hospital (ED) attendances and emergency hospital admissions in key patient groups (including those with long term conditions), which is a major priority for the NHS.

Aims:

  • Establish close collaboration with stakeholders in emergency and urgent care in Yorkshire and Humber to develop high quality evidence to answer key local NHS and user priorities
  • Use large routine NHS datasets to Identify key patient groups amenable to care outside of hospital
  • Evaluate interventions to reduce avoidable attendances and unplanned admissions for patients with long-term conditions.

Progress:

  • Completion of the region’s first emergency and urgent care routine linked dataset linking Yorkshire Ambulance Service call data (999 and NHS 111) and hospital data from all 13 acute hospital NHS trusts in Yorkshire and Humber
  • Using large Hospital Episode Statistics (HES) Data to define and identify non-urgent attenders to the emergency department (ED)
  • Early evaluation of interventions to improve care (Senior Doctor Triage and GP-collocation)
  • Further separate analyses underway to identify alternative care pathways for older people, people of working age and patients with mental health problems.

Timescales:

March 2018: Completion of analysis of large routine datasets.
December 2018: Completion of project evidence reports.