Process Redesign to Improve Chemotherapy Appointment Booking at the BC Cancer Agency Vincent Chow BC Cancer Agency vchow@bccancer.bc.ca Ruben Aristizabal Pablo Santibáñ áñez Kevin Huang Martin Puterman Nancy Runzer www.orincancercare.org/cihrteam
The Presentation in One Slide Background: scheduling for outpatient clinic Problem: late appt. confirmation and long waitlist Causes: outdated, over constrained processes Solution: addition of flexibility through process redesign and optimization-based scheduling tool Results: 58% reduction in late appt. confirmation; 84% decrease in waitlist; patient/staff satisfaction improvement 2
Background
BC Cancer Agency Province-wide, population-based cancer control program for residents of British Columbia & Yukon Full spectrum of cancer care, from prevention and screening, to diagnosis, treatment, and through to rehabilitation 4
Cancer Treatment Options Surgery Radiotherapy Chemotherapy - Drug treatment: orally intravenously - Mostly outpatient 5
IV Treatment CHEMO ROOM 6
Chemotherapy Unit in Vancouver Centre 14,000+ appts/year: - 60-65 patients/day - 200+ protocols - ½ to 10+ hrs treatment 10 nurses, 9 rooms, 33 chairs Open 09:00 to 19:00 7
Old Process Ambulatory Clinic Oncologist Orders Oncologist Appt Booking Process
Booking Process Appointment request Available Taken Patient waitlisted when there is no capacity left for that treatment type There is capacity available but it is restricted to other treatment types There may be flexibility in appointment day but old process did not use it Capacity by treatment type Mon Tue Wed Thu Fri?Waitlist Booked appointments 9
Old Process Ambulatory Clinic Oncologist Orders Chemo Treatment List Oncologist Appt Booking Process Confirmation to Patient Chemo Waitlist Chemo Unit Chemo Treatment List Chemo Waitlist Scheduling Process
Scheduling Process One or two days before the treatment date: Waitlist Treatment List Wed? Manage capacity: Release pre-specified capacity Add nursing time Cancellations Manage demand: Triage patients Re-schedule patients to different days based on clinical tolerance - These were not carefully defined or used 11
Old Process Ambulatory Clinic Oncologist Orders Chemo Treatment List Oncologist Appt Booking Process Confirmation to Patient Chemo Waitlist Chemo Unit Chemo Treatment List Manual Schedule Chemo Waitlist Scheduling Process Confirmation to Patient 12
Symptoms
Symptoms Ambulatory Clinic Oncologist Orders Chemo Treatment List Oncologist Appt Booking Process Confirmation to Patient Chemo Waitlist Chemo Unit Chemo Treatment List Manual Schedule Late confirmation Chemo Waitlist Scheduling Process Confirmation to Patient 14
Symptoms Operations: - Patients: late notification - Nurses: uneven workload and complexity distribution - Clerks: extensive rework; difficult scheduling - Pharmacy: drug preparation capacity exceeded - Oncologists: frustrated patients; delays to start treatment Complaints, stress, low morale, dissatisfaction and potentially adverse impact on health outcomes 15
Symptoms by the Numbers In the data: - Late appointment confirmation: 42% < 7 days (IQR: 11) 22% < 3 days (IQR: 21) - Waitlist: 24 pats. 1 week in advance (IQR: 15) 6 pats. 1 day in advance (IQR: 6) Figures based on extensive data analysis of appointment data from CAIS, the BCCA information system. 16
Appointment Waitlist Appointment Waitlist Chemotherapy Appointment Waitlist (1 week before) 60 50 40 30 20 10 0 17 Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Jan-10 Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Appointment Date Waitlist Size
Impact on the Patient Cancer and chemotherapy can be physically and emotionally challenging; no need for added stress! Appointment uncertainty problematic because: - Need for advanced notice to coordinate transportation - Sufficient confirmation to take pre-treatment drugs - Travel complications and additional financial burdens for those traveling from outside Metro Vancouver 18
Identified Problems Root causes: - Over-constrained capacity: Several appt. types with reserved capacity Reservation levels were ad hoc and static - Inflexibility in existing booking and scheduling processes: Delayed use of tolerance and release of protected capacity Difficult to assess impact of last-minute changes to schedule on nurse and pharmacy workloads Leads to: - Unnecessary capacity conflicts - Inefficient use of existing capacity! Need to effectively use flexibility in the process 19
Solution
Computer Simulation Process Changes INPUT 19300 appt 2008/2009 Computer Simulation Model OUTPUT Waitlist Confirmation time Utilization 21
Simulation Results Simulation Results 80 70 60 50 40 30 20 10 0 22 Jan-08 Feb-08 Mar-08 Apr-08 May -08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 Appointments Daily Waitlist Size Eliminate most protected capacity and add tolerance Appointment Date Old Process Scenario_1 Scenario_2 Eliminate most protected capacity
Simulation Results 80 70 Daily Waitlist Size Old Process Scenario_1 Scenario_2 60 Old Process Scenario_1 Scenario_2 50 40 30 20 10 0 Jan-08 Feb-08 Mar-08 Apr-08 May -08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 Appointments Eliminate most protected capacity and add tolerance Eliminate most protected capacity Appointment Date 23
Redesigning the Process Ambulatory Clinic Oncologist Orders Appt tolerance Chemo Treatment List Oncologist Appt Booking Process Confirmation to Patient Eliminate most protected capacity Chemo Waitlist Chemo Unit Chemo Treatment List p ijk ˆ p p i ξ iˆ i p p i ν iˆ i j x 1 p xˆ ijk 1 p + p xijk + δk = xijk ( pij) AM ( pij) PM Min Φ+ + δ ( δ ) + k + δk k k Manual Schedule Earlier confirmation Chemo Waitlist Scheduling Process Confirmation to Patient 24
Our Solution Simulation model used to determine process changes with highest impact Key redesign elements: - Eliminate most protected capacity - Use treatment tolerance in booking stage - Create schedule one week before treatment (assign start times & nurse) New processes implemented June 2010 25
Results
Evaluation Framework quantitative qualitative Waitlist size Confirmation time Patients Satisfaction Reasonable conf. time, # changes Nursing workload Clerical rework Satisfaction Workload New process Staff Administration Utilization Capacity availability Pharmacy workload Transferability to other centres and clinics 27
Quantitative: Appointment Waitlist Chemotherapy Appointment Waitlist (1 week before) 60 50 40 30 20 10 0 Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Jan-10 Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Pre-Implementation Waitlist Size Appointment Date Pre-Implementation Post-Implementation Post-Implementation 28
Quantitative: Summary Metric Pre- Implementation 1 Post- Implementation 2 Decrease Patients Waitlist size One day before appointment 6 3 50% One week before appointment 24 4 83% Confirmation time Appointments confirmed < 7 days (% appts. per day) 42% 18% 58% Appointments confirmed < 3 days (% appts. per day) 22% 11% 50% Evaluation timeframe: 1.Pre-Implementation: June 29 th to October 23 th, 2009 2.Post-Implementation: June 28 th to October 22 th, 2010 Figures shown: median 29
Quantitative: Comparing Actual Results with Simulated 80 70 60 50 40 30 20 10 0 30 Jan-08 Feb-08 Mar-08 Apr-08 May -08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 Appointments Daily Waitlist Size Appointment Date Old Process Scenario_1 Scenario_2 Current
Quantitative: Comparing Actual Results with Simulated 80 70 Daily Waitlist Size Old Process Scenario_1 Scenario_2 Current Appointments 60 50 40 30 20 10 0 Jan-08 Feb-08 Mar-08 Apr-08 May -08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 Old Process Scenario_1 Scenario_2 Current Appointment Date 31
Qualitative: Patient Survey Significant improvements in satisfaction due to new booking process 100 Satisfaction with Date and Time Notification Pre- and Post-Implementation 80 % Responses 60 40 20 0 Date notification Time notification Pre-implementation 82% 87% Post-implementation 97% 94% 32
Qualitative: Staff Survey/Interviews Booking clerks survey: - Over 80% prefer new booking process to previous one - Over 40% noted a reduction in stress level and time required to book a patient while none reported increases Nurses survey: - Over 80% like new schedules better than the old ones Interviews: - Overall positive feedback; fine-tuning required - Better workload distribution - Realized potential of advanced analytics 33
Summary
Summary The changes: - Redesign of processes to better use flexibility - Development of optimization-based scheduling tool Successful implementation under multiple criteria - Increased notification times, reduced waitlist - Improved workload distribution and capacity adherence Other benefits: - Standardization and formalization of processes - Use of advanced analytics for process improvement - Additional data for other analyses 35
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