Health System


Improving Accident & Emergency Performance

15%fewer A&E time breaches

Purpose

Our client was a national health system facing significant pressure to understand and improve performance against the ‘4-hour wait-time target’ within Accident & Emergency centres.

Our goal was to understand and optimise performance against the ‘4 hour A&E target’ to help reduce breaches.

Approach

This is a single metric seemingly focused on one small stage in the full Emergency Pathway. However, it serves as a good proxy for overall performance as it reflects the volume and type of patients presenting in earlier stages in the pathway, the operational performance of the Urgent Care Centre and Emergency Departments, and the later stages of the pathway, because it depends on the smooth flow or blockages that can happen elsewhere in the hospital.

We combined data from over 12 systems including:

  • Patient Administration System (PAS) data: patient flow/movement data, time-stamped patient movement data (e.g. check-in by source, patient-to-room assignment data)
  • Emergency Department (ED) Management data (e.g. Symphony)
  • Bed management data (e.g. GCIS)
  • Referrals from GP surgeries, emergency calls and walk-ins to Urgent Care Centres and Emergency Departments
  • Staff rotas
  • Staff HR data (including years of experience, banding, education level, compensation structure, agency staff or permanent)
  • Diagnostics and test results from Medical Assessment Unit
  • Discharges, whether to home, community hospital nursing homes or other locations

We tracked patients through the Emergency Pathway from first interaction with GP to UCC or ED, to wards and patient discharge.

Outcome

We identified 15% fewer breaches by optimising the transfer of patients, repurposing beds and adjusting elective procedures. These insights were delivered as very specific action points, such as 25% less time breaches when a senior Band 7 nurse is present in A&E and impact thresholds on A&E breach performance of smooth flow or blockages in MAU, Inpatient Wards or Discharge.

In addition we were able to validate a predictive model by using congestion in diagnostic testing as leading indicator of admissions breaches, and improve the overall A&E demand models to enhance resource planning for inflow of patients.

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