Which data can measure emergency department patient flow?
Measuring emergency department patient flow requires clearly defined events, agreed timestamps and a shared understanding of each stage of care.
Define what each event means first
The same label may represent different times in two systems. “Examination complete” could mean the end of the examination, recording the result or the result becoming available. Interpreting a measure requires knowing which event was used.
Alongside the case identifier, record the event type, time, source and, where the workflow requires it, the responsible role. Comparisons require consistent handling of time.
Examples of measurable stages
| Stage | Possible start and end events |
|---|---|
| Arrival and triage | Recorded arrival → start of triage |
| First medical examination | Recorded arrival → start of the first medical examination |
| Diagnostic process | Test request recorded → result becomes available |
| Consultation | Request recorded → agreed completion event |
| Ward transfer | Admission decision → actual handover |
An illustrative time calculation
If a test request is recorded at 10:00 and the result becomes available at 10:45, the interval defined this way is 45 minutes. This example does not mean that performing the test took 45 minutes, and it is not an actual IntelMed measurement.
Understanding a delay may require additional events: acceptance, patient transport, the start of the examination or approval of the result. Missing data should be identified separately rather than becoming a zero-minute interval.
Show the context alongside the measure
An average alone does not show long waits or changes in case mix. Include counts, missing data and the distribution of durations in the review.
The IntelMed emergency demonstration illustrates how a current operational picture can connect patient pathways, tasks and capacity data. The measures available must be finalised against local definitions and data connections.
- Which period and how many completed cases are included?
- How many events are missing or were recorded retrospectively?
- Which pathway types and urgency categories appear in the sample?
- Did capacity or the way events are recorded change during that period?
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