A Case Study of a Simulation-Based Decision Support Tool

1. A Case Study of a Simulation-Based Decision Support Tool Michael Carter Healthcare Modeling Lab, Mechanical & Industrial Engineering, University of Toronto ORAHS 2006: Poland

2. Organizations Involved • University of Toronto • The Health Care Resource Modelling Lab • Hamilton Health Sciences Centre (HHS) • Perioperative Services • Clinical Appropriateness and Efficiency Program (CARE) • Institute of Clinical Evaluative Sciences ORAHS 2006: Poland

3. Primary Team Members • University of Toronto • Jean Yong – MASc candidate • Michael Carter – Director, Healthcare Resource Lab • Carolyn Busby – Doctoral Candidate & Modeller • Hamilton Health Sciences • Kelly Campbell – Director of Perioperative Services • Steve Metham – CARE Facilitator • Dr. Kevin Teoh – Head of Cardiac Surgery • ICES • Dr. Jack Tu – Senior Scientist ORAHS 2006: Poland

4. Background • Background: • Expansion of operating room activity • Determine new surgical booking policy • Objective: • Facilitate strategic planning of cardiac surgical resource allocation • Determine OR schedule • Determine number of beds required in ICU and ward ORAHS 2006: Poland

5. No Redo/ Combined Surgery Grouping Cardiac Surgery 2002-2004 N>4000 Redo/ Combined CABG VALVE COTHR CONGD CABG VALVE AORTA CAVLV AORTA CAVLV COTHR CABG 1,2,3 TVR,AVR CONGD COTHR CABG 4,5,6,7 MVR ORAHS 2006: Poland

6. Surgery Grouping Cardiac Surgery 2002-2004 Major 1 353 mins n=116 Major 2 431 mins n=60 Intermediate 322 mins n=281 359 Minor 244 mins n=1016 266 In-btwn 284 mins n=890 313 ORAHS 2006: Poland

7. Minor 246 mins n=1530 In-btwn 285 mins n=1789 Major 461 mins n=220 Intermediate 337 mins n=499 Surgery Duration Distribution ORAHS 2006: Poland

8. Conceptual Model Queue by surgeon Surgery duration – by procedure Waiting List Prioritized by acuity Cardiac Surgical Unit Operating Room ICU Cardiac Surgical Unit Same Day Surgery Ward Discharge ORAHS 2006: Poland

9. Performance Indicators • Number of cases completed/year • Cancellation rates • Lack of ICU/ ward bed • Out of scheduled time • More urgent case took precedent • Operating room utilization • Under-utilization (hours/week) • Overtime (hours/week) • Ward bed utilization (ICU & CSU) ORAHS 2006: Poland

10. Model Validation • 50 replications of 1 year each • Imitate current scheduling rules • Run the model with 2002, 2003, 2004 data • Compare output from the 3 models with historical data • Experts’ opinions • Meeting with clinicians ORAHS 2006: Poland

11. Results ORAHS 2006: Poland

12. Applications Can we meet provincial target with 4 ORs varying room length Do we have enough ICU/ ward capacity? What if we pool all the surgeons’ urgent slots together? • Simulated what-if scenarios for 4 operating rooms to answer stakeholders’ questions • Encouraged clinicians to propose new ideas of how the system could be run differently for higher efficiency • Tested over 10 scenarios Can we book surgery differently? ORAHS 2006: Poland

13. Key issues from surgery • Ability to achieve priority funded volumes • Organization of block time – length and placement • Available beds – ICU/ward • Minimizing cancellation rate • Booking rules • Pooling of referrals • System for urgent/emergent cases ORAHS 2006: Poland

14. Modifying Cancellation Rule ORAHS 2006: Poland

15. Can we book surgery differently? ORAHS 2006: Poland

16. Scenario A Scenario B Scenarios – OR schedule ORAHS 2006: Poland

17. Model Results ORAHS 2006: Poland

18. Planning ICU and Ward Capacity ORAHS 2006: Poland

19. Questions? ORAHS 2006: Poland