1. On-The-Day Schedule Performance in the Outpatients Department - Developing a Mental Model with Simulation, a Work-in-Progress By Philip Ruttle Funded by: Operational Research Applied to Health Services: XXXII Annual Meeting July 23 - July 28 2006 Wroclaw, Poland Sub Heading And Date

2. Overview • Objectives • OPD-An Injury and Orthopaedic Clinic • Data Collection • The Model • Some Preliminary Results

3. Objective • People complain of excessive waiting time • But • “People turn up before their appointment time” • And • “We find it difficult to turn these people away” What can be done? Our objective is to examine the system and identify possible improvements that will have a positive impact on performance especially waiting time.

4. OPD-An Injury and Orthopaedic Clinic • The service consists of: • 1 Consultant • 3-4 Registrars/MHO • 1 Plaster Room (2 “server”) • 1 Dressing Room (1 “server”) • 2 Nurses “administrative roles” • 2 Receptionists • Service demand • Ratio 1:3.5New:Review • Approx. 20,000 visits per year • Auxiliary services • X-Ray • Physiotherapy

5. System Characteristics • Do Not Attend (DNA) • On average there is an un-attendance rate of 17% • This is mostly accounted for by review patients • Urgency, Consultant driven demand • Urgent patients must be seen when requested • Consultants “dictate” next appointment regardless of demand • Patients arrive early • Dependant on busses, lifts from neighbours/relatives • Want to get seen sooner • Re-entry points • Patients need to visit a service more then once

6. Process Flow • The First process can be • Physician • X-ray • Casting • From here the patient can take a number of different routes

7. Re-entry points James Ignizio (Intel): There are many re-entry points in semiconductor manufacture making scheduling very difficult, however machines can be split to create a “flow” process. This can’t be done in this outpatient clinic.

8. The Profile • Some consultants prefer most of the patients to arrive at the start • n patients arrive at a fixed interval t • These profiles are difficult to “setup”, change

9. Data Collection • Difficulty due to: • Re-entry points • Little/no data previously available • Patient confidentiality • Accompanying persons • Layout

10. RFID Possible Solution • Give each patient a tag (label on a piece of paper) Which they can put in a pocket purse and throw away when finished • This would track when each patient would enter/leave each service

11. RFID Advantages • Very accurate • Automated • Can gather large quantities of data every day • Very little staff involvement needed • Doesn’t interfere with medical equipment

12. RFID Disadvantage • Quote Basically to track 10 entry/exit points would require the following. • 10 x Readers (1 per entry exit point) • 20 x HP Antenna (2 Per Reader to cover each entry/exit point) • 6000-12000 Tags - Based on 1-2 Months usage (1 per package - 200 per day) • 1 x Site Manager Software to collate the information gathered from each read point. • PC to host software • Networking of readers to host PC. • Resource time to implement and test. Approx. Cost for above would be €42000.00 May be some potential for rental but still very expensive

13. Solution

14. The Model

15. Entry/Earliness

16. Some Preliminary Results

17. Analysis Standard deviation of 15 runs. Box plot of 15 runs, shows the range and spread of the quantity waiting

18. Analysis Box plot of queue finish times, shows the range and spread of finish time Box plot of all runs, shows the range and spread of the quantity waiting

19. Further Work • Fully validate and verify model • Run experimentation looking at the effects of: • Single block • Individual block fixed interval • Individual block fixed interval with an initial block • Multiple block fixed interval • Multiple block fixed interval with an initial block • Variable block fixed interval • Individual block fixed interval • Multiple appointment for each area • (Inline with work by Bailey and Welsh, O’Keefe, Ho and Lau, etc.) • Examine the effects of other variable changes • Resources • The effects of earliness • Service distributions • Routing • Etc.

20. Conclusions • Re-entry points play a vital role • RFID a possibility but very costly • The model can handle the “messy” data (robust) • The inherent variability in the system can cause good/poor performance • Potential to reduce costs?? • Aim to improve patient experience • Reduced DNA rate • Increased staff satisfaction…

21. Thank you for your Time!!!