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The Liverpool system - classification, learning & prevention

Learn how the Liverpool System improved patient safety in radiation oncology by promoting reporting, analysis, and learning from incidents. Explore the process, results, and statistical trends of error reduction and risk improvement.

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The Liverpool system - classification, learning & prevention

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  1. Incident Reporting and Learning: The Liverpool system - classification, learning & prevention Anthony Arnold Director Cancer Services, Illawarra Shoalhaven Local Health District Anthony.Arnold@sesiahs.health.nsw.gov.au

  2. Context

  3. The Liverpool System Ref: IJROBP 2010 Volume 78, No 5, Pages 1548-1554

  4. A Problem Worth Solving…… • Complexity of radiation oncology • At the time no system of analysis was in place • Lack of clinical governance surrounding reporting • There was limited openness about reporting events • The culture was predominantly blame based • Standard reporting systems are ineffective for radiation oncology

  5. Classification

  6. Prescription Simulation Computing Pre-Treatment Treatment Bolus Shielding / MLC Imaging Documentation

  7. Classification Advantage

  8. Error / Event Definitions • Event: • “event or circumstance which could have resulted, or did result in harm to a patient” • Actual Error: • “Error resulting in radiation exposure other than that intended or prescribed – correctable or otherwise” • Near Miss: • “Error or non-conformance detected before reaching the patient”

  9. High Level Structure…….PDSA

  10. Reporting and Managing an Event

  11. Department Analysis

  12. Results - Initial Pilot • 688 reports were logged during the study period • 155 Actual errors (23%) • 533 Near Miss (77%)

  13. Results - Subsequent Pilot • 670 reports were logged during the study period • 67 Actual errors (10%) • 603 Near Miss (90%)

  14. Time Trends Statistics Ref: Simple Interactive Statistical Analysis online statistical calculator. Available at: http://www.quantitativeskills.com/sisa/statistics/t-test.htm. Accessed 29 January 2008

  15. Time Trends Statistics Ref: Simple Interactive Statistical Analysis online statistical calculator. Available at: http://www.quantitativeskills.com/sisa/statistics/t-test.htm. Accessed 29 January 2008

  16. Time Trends 1st Pilot: Attendances

  17. Time Trends – 2nd Pilot: Courses

  18. Outcomes – Key Measures

  19. Patient Safety Risk Improvement • REDUCTION IN REPORTED EVENTS as a function of attendances • Actual Error rate reduced from 0.26% to 0.08% (p=0.0017) • Near Miss rate reduced from 2.33% to 1.01% (p<0.0001) • IMPROVED RELATIVE PATIENT SAFETY RISK per treatment course • Actual error rate reduced from 1 in 19 courses to 1 in 75 courses; in other words from 5% down to 1.3% risk of detectable error (p=0.0003) • Near miss rate reduced from 1 in 2 courses to 1 in 6 courses; in other words from 50% down to 17% (p<0.0001)

  20. Thank you

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