HDdUHB Health Analytics

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HDdUHB Health Analytics by Mind Map: HDdUHB Health Analytics

1. Knowledge Discovery: The "WHY": How we got here and where do we go next

1.1. Unscheduled Care EDAPT Atlas

1.1.1. Cluster Analysis Emergency Length of Stay CDU length of stay over 3 day Ward length of stay over 10 days Emergency Bed Occupancy Profiles Data relationships Emergency Medically Fit (SharePoint Data) Profiles Data relationships Escalation Level Profiles Data relationships Cross-sectional analysis Compare Christmas period Compare predicted "black day" periods Compare predicted "quiet" periods

1.1.2. Root Cause Analysis Emergency Length of Stay Excessive stays Emergency Bed Occupancy Why does is vary? Why did high occupancy occur? Emergency Medically Fit (SharePoint Data) Why does it vary? Why are some patients delayed? Escalation Level Why does it vary? Why did high escalation occur?

2. Business Intelligence: The "WHAT": What happened in the past

2.1. Unscheduled Care Dashboards

2.1.1. Unscheduled Care Current Snapshop Dashboard Hospital Flow Wards Emergency Depertment As live as possible, but full view of flow is one day in arrears due to timeliness of data entry Blockages 118 Data Who is calling When are they calling What is happening

2.1.2. Unscheduled Care Programme Board High Level Metrics

2.1.3. Control Charts (including SPC)

2.2. Unscheduled Care EDAPT Historian (What happened in the past)

2.2.1. Self Service (controlled or uncontrolled)

2.2.2. Drill Down Information

2.2.3. Slicing and Dicing

2.2.4. Patient Profiles for time period

2.2.5. Summary for time period

3. Modelling & Forecasting: The "WHEN": What we think could happen and when

3.1. Simulation

3.1.1. Scenario testing

3.1.2. Demand and Capacity

3.1.3. Process/Pathway Optimisation

3.2. Unscheduled Care EDAPT Forecaster

3.2.1. Emergency Admissions (including medical and surgical split)

3.2.2. Emergency Discharges (including medical and surgical split)

3.2.3. Emergency Bed Occupancy

3.2.4. Emergency Average Length of Stay

3.2.5. Emergency Black Days

3.2.6. Emergency Department Attendances (including major, minor and ambulance arrivals)

3.2.7. Emergency Expected Required Staffing Levels

3.2.8. Predicted data into SITREP

3.2.9. 70-90% accurate, depending on the predicted metric (some are more difficult to predict than others)

3.2.10. Unscheduled Care High Level Metrics