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Improving Emergency Response Efficiency in Calgary EMS Using Analytics

Explore how analytics combines math, data, and computers to enhance insight and efficiency in Calgary EMS response times. Investigate the data from 2000-2004 for priority 1 calls, analyzing response times, particularly for different times of day. Additionally, delve into defining ward boundaries based on criteria such as geographical contiguity, population equality, and community interests. Discover how optimizing resources can lead to better emergency services in Calgary.

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Improving Emergency Response Efficiency in Calgary EMS Using Analytics

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  1. From Ambulances to Ward Boundaries Daniel Haight U of A Centre for Excellence in OperationsDarkhorse Analytics

  2. Analytics

  3. The Goal

  4. Analytics <Combining math, data, and computers to improve insight and efficiency>

  5. Math Finance Computer Science Computers Data Accounting IT/MIS

  6. Calgary EMS:Q: What’s going on?

  7. Response time % Response < 8min Data from 2000-2004 – priority 1 calls.

  8. Response time % Response < 8min Data from 2000-2004 – priority 1 calls.

  9. Priority 1 calls 12:00am

  10. Priority 1 calls 1:00am

  11. Priority 1 calls 2:00am

  12. Priority 1 calls 3:00am

  13. Priority 1 calls 4:00am

  14. Priority 1 calls 5:00am

  15. Priority 1 calls 6:00am

  16. Priority 1 calls 7:00am

  17. Priority 1 calls 8:00am

  18. Priority 1 calls 9:00am

  19. Priority 1 calls 10:00am

  20. Priority 1 calls 11:00am

  21. Priority 1 calls 12:00pm

  22. Priority 1 calls 1:00pm

  23. Priority 1 calls 2:00pm

  24. Priority 1 calls 3:00pm

  25. Priority 1 calls 4:00pm

  26. Priority 1 calls 5:00pm

  27. Priority 1 calls 6:00pm

  28. Priority 1 calls 7:00pm

  29. Priority 1 calls 8:00pm

  30. Priority 1 calls 9:00pm

  31. Priority 1 calls 10:00pm

  32. Priority 1 calls 11:00pm

  33. Ward Criteria • Geographical • Contiguity • Compactness • Natural boundaries • Socio-political • Population equality (± 10%) • Electoral equality (± 25%) • Groups of interest (community leagues, socio-demographics) • Similarity to existing solution

  34. 64 Units 4 Districts 360 Population 90 each 90 each 88, 88, 91, 93

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