| Field | Value |
|---|---|
| Driver | Grace Adekemi Adebowale |
| Approval | Bhetty Sams |
| Due Date | 7/17/2026 |
| Status | Draft |
Key Outcomes:
Northern Health NHS Foundation Trust's A&E departments are not a capacity problem — all three sites have enough physical space for current patient volumes — but a process and data visibility problem. Only 72% of patients complete their A&E journey within the NHS four-hour standard, well short of the 95% constitutional target (and below the 78% threshold that triggers formal NHS England oversight). This gap has persisted for over three years and is now flagged in the Trust's most recent CQC inspection report.
Patients are delayed by three main things: inconsistent triage practice across staff cohorts, insufficient time-to-first-clinician cover (especially on night shifts, 34% slower than day), and prolonged "boarding" — patients medically cleared but waiting an average of 3.1 hours for an inpatient bed, which alone accounts for ~38% of all breaches. Underneath that, frontline teams manage the department from whiteboards and experience rather than live data, and staffing rotas don't reflect actual demand patterns.
Without a structured, data-evidenced understanding of where and why time is lost in the patient journey, the Trust can't reliably close the 23-point gap to 95%, satisfy the CQC's 12-month re-inspection window, or reduce the patient safety, financial, and staff-retention risks that come with sustained overcrowding.
To conduct a structured, evidence-based analysis of A&E patient flow data to identify, quantify, and rank bottleneck stages driving four-hour breaches, then translate findings into a live Power BI dashboard, SQL query library, and recommendations report Trust leadership can act on and sustain independently.
| # | Objective | Success Metric |
|---|---|---|
| OBJ-01 | Establish a data-driven baseline of average patient wait times at each stage of the A&E journey across all three sites | Stage-by-stage average wait time calculated from EPR timestamp data and validated against manual sample checks |
| OBJ-02 | Identify the top three bottleneck stages contributing most significantly to four-hour target breaches | Bottleneck stages ranked by volume and proportion of breach contribution, supported by SQL analysis |
| OBJ-03 | Correlate staffing levels with wait time performance by shift type and site to identify resourcing gaps | SQL analysis joining staff_shifts to patient_visits breach data, producing a staffing vs breach matrix |
| OBJ-04 | Model 12-month patient arrival demand by hour and day of week to support rota optimisation | Demand heatmap produced and reviewed with A&E operational lead |
| OBJ-05 | Design and deliver a Power BI operational dashboard for frontline A&E team leads | Dashboard reviewed and approved by A&E Clinical Director; adopted by charge nurses on all three sites |
| OBJ-06 | Deliver a written findings and recommendations report with an impact/effort prioritisation matrix | Report reviewed and accepted by A&E Clinical Director and Head of Operations |
| Metric | Current | Target | Gap |
|---|---|---|---|
| Four-hour standard compliance | 72% | 95% | -23 pts |
| Walk-in triage delay (average / peak) | 28 min / 50+ min | 15 min | 13–35+ min |
| Time-to-first-clinician (codes 3 & 4) | 74 min avg | 60 / 120 min | Breach on code 3 |
| Night-shift time-to-clinician penalty | +34% vs day | Parity | 34 pts |
| Boarding wait for inpatient bed | 3.1 hrs avg | Minimal | ~38% of all breaches |
| A&E nursing vacancy rate | 18% | Sector benchmark | Elevated |