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Diagnosing patient flow problems in A&E at Hillingdon hospital

Diagnosing patient flow problems in A&E at Hillingdon hospital . Yiping Chen (MSc student, Management Science), Dave Worthington (Supervisor, Management Science) d.worthington@lancaster.ac.uk Dan Suen (PhD student, STORi ) d.suen@lancaster.ac.uk. Executive Summary.

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Diagnosing patient flow problems in A&E at Hillingdon hospital

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  1. Diagnosing patient flow problems in A&E at Hillingdon hospital Yiping Chen (MSc student, Management Science), Dave Worthington (Supervisor, Management Science) d.worthington@lancaster.ac.uk Dan Suen (PhD student, STORi) d.suen@lancaster.ac.uk

  2. Executive Summary • Improving patient flows: • Considerable scope for improving patient flows through A&E by fine tuning staffing levels; • Patient flow analytics identifies problems, and indicates direction of change for staffing levels; • Modelling patient flows would provide greater confidence in proposed solutions; • Early warning system: • A wholly statistical method based on patient characteristics and state of A&E would have too many false positives.

  3. Patient Flow Analytics - The Symptoms Length of stay in A&E distribution The large spike at the 4 hour point indicates the extreme measures that are often necessary to make sure that as few patients as possible breach the 4 hour target.

  4. Patient Flow Analytics - The Symptoms # of breaches by day and hour # of breaches on Mondays are almost double those on other days. Lower numbers of breaches between midnight and 8am mainly because fewer patients going through A&E.

  5. The Number of Arrival Time (o’clock) Patient Flow Analytics - The Causes Patient arrivals by day and hour Very strong time of day effect, with Mondays clearly having higher rates

  6. Patient Flow Analytics - The Causes Patients in Dept. by day and hour Large variations throughout the day, with Monday again standing out.

  7. Patient Flow Analytics – Part of the solution Marked variations throughout the day, but out of kilter with variations in workload. Staffing levels by day and hour

  8. Patient Flow Analytics – Staffing levels need fine tuning to ………. Staffing levels (SL total) V arrivals Vpatients in A&E (# all) Vtime in A&E (LoS) V breaches

  9. Tentative conclusions • (Reducing attendance rates & increasing staffing levels would reduce congestion levels) • Like many other A&E depts, staffing levels are not well adjusted to best cope with the varying workloads • Patient flow analytics can guide revised staffing levels • ongoing research shows that it would be wrong to have staffing levels following arrival rates (too early); • it would also be wrong to have staffing levels following the total patients in the department (too late); • Modelling patient flows would provide greater confidence in proposed solutions

  10. An early warning system? • Statistical methods (logistic regression) used to predict breaches using: • day, time, staffing levels, patients already in A&E by HRG code, # of investigations needed, # of treatments needed, HRG code; • Same info after 2 hours in A&E; • Same info after 3 hours in A&E. • Significant risk factors identified, but would lead to many many false positives!

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