Reducing Patient Wait Times & Improving Resource Utilization at the BC Cancer Agency s s Ambulatory Care Unit

Transcription

1 Reducing Patient Wait Times & Improving Resource Utilization at the BC Cancer Agency s s Ambulatory Care Unit Pablo Santibanez Vincent Chow John French Martin Puterman Scott Tyldesley

2 The CIHR Team in Operations Research for Improved Cancer Care Oncologists Administrators Statisticians Faculty Researchers PhD Students BCCA UBC Operations Research Professionals 2

3 Outline The Ambulatory Care Unit Data, Analysis & Modelling Scenario Analysis Conclusions 3

4 The Ambulatory Care Unit 4

5 Background BC Cancer Agency, Vancouver Centre: - Three programs: radiation, medical and surgical oncology - Referral centre for provincial programs Ambulatory Care unit (ACU): - Multiple visit types: New patients diagnosed with cancer Follow-up patients (in active treatment or regular check-up) Consults (inter-program consultations) - Multiple physicians: oncologists, residents, students - 15 to 25 clinics per day; 45 exam rooms patient visits per day; 50,000+ visits in 2007/2008 5

6 The Ambulatory Care Unit: Floor Plan Surgical Systemic Radiation Therapy Waiting Area Reception Nursing Station 6

7 Problems Lack of exam rooms, physicians space and patient waiting area Increasing pressure due to: - 9% annual increase in patient appointments - more clinical trials - academic / teaching duties - new information technology assisted care (i.e. electronic charts and tele-health) Concern about the patient experience excessive wait times during visit to the centre 7

8 ACU Process Reception Waiting Room Waiting Room Arrival & check-inn Wait for Exam Room Preparation for Consult Wait for Consult 1st Consult Wait for Consult 2nd Consult and Exit Room Wait for Discharge (a) (b) (c) (d) (e) (f1) (f2) (g) Non Value Added Times 8

9 Data, Analysis & Modelling 9

10 Data Analysis Available data: - Historical bookings, clinic schedules, room capacity - No process data! Need to better understand process: - Resource utilization - Patient wait times Data collection: - Determined key points in process - Observed selected clinics for 2 weeks - Collected up to 14 time stamps for 600+ appointments - Hard and expensive, but extremely useful 10

11 Exam Room Utilization 6 5 Patient in room Physician in room # of Rooms :30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 Time 11

12 Patient Wait Time % Wait Time % Cumm 90% 25 80% 20 70% 60% 15 50% 40% % of patients wait at least 28 10% of patients wait at least 46 30% 20% 10% 0 0%

13 Methodology Complex system: - Multiple physicians: oncologists, student/residents - Multiple exam rooms - Variable process times and arrival rates - Multiple patient paths After mapping process and identifying waste, need to try changes in the system and evaluate results Decided on discrete event simulation - Focus on patients: from arrival until exam room departure 13

14 Simulation Model 14

15 Scenario Analysis 15

16 Scenarios & Metrics Changes in the process: - Clinic Start - Appointment Order - Appointment Duration - Schedule Add-ons Improve patient turn around times Improve patient wait times Improve productivity Changes in the system: - Exam room allocation: dedicated vs. dynamic Performance metrics: - Patient wait time - Clinic duration - Doctor idle time - Exam / wait room utilization 16

17 Results 20 Unique combination of: Clinic Start Appt Order Appt Duration Schedule Add-ons 15 Change in Clinic Duration Efficient Frontier Current State Decreased Wait time and Clinic end time Change in Average Wait Time

18 Results 120% Wait Time and Clinic Duration for Selected Scenarios 100% % of Current Level 80% 60% 40% 20% 0% Current Clinic On Time Clinic On Time Clinic On Time Clinic On Time Sched Work Ins Sched Work Ins Sched Work Ins Average Wait Time Average Clinic Duration appt dur +15% appt dur + 30% Changes to Current State Significant reductions in wait time can be achieved with minor impact on current clinic durations 18

19 Room Allocation: Dedicated (current) # rooms Clinics RT SYS Exam Rooms 19

20 Room Allocation: Dynamic (proposed) # rooms Clinics RT SYS Exam Rooms 20

21 Impact of Dynamic Room Allocation Average Wait SYS Average Wait RT Average Wait Time No significant change in wait No significant change in wait Current RT capacity Current SYS capacity Exam Room Capacity When pooling exam rooms - Up to 6 rooms can be reduced in Systemic - Up to 4 rooms can be reduced in Radiation Therapy 21

22 Conclusions 22

23 Conclusions Up to 70% reduction in patient wait times can be achieved with only limited increase in clinic duration Result of not one but multiple strategies combined: - Clinic punctuality - Schedule add-ons (avoid double-booking) - Accurate appt. booking Potential to reduce 26% exam room requirements by using dynamic room allocation 23

24 Conclusions Operations Research allowed to: - Identify areas to focus on (waste) - Go to next step and develop & test changes Stakeholder involvement is fundamental: - To understand the process - To propose viable solutions - To embrace change 24

25 Next Steps Patient input: wait time survey - Pilot study carried out during modelling stages - Full study recently finished Implementation: - Try changes in some clinics - Evaluate results - Implement on all clinics 25

26 Thank you! 26