background targeted changes analytical approach optimization

Using Simulation Based Optimization To Structure And Manage Ambulatory Clinics
Phil Troy, Nadia Lahrichi, Dana Porubska and Lawrence Rosenberg
BACKGROUND
·
The Sir Mortimer B. Davis Jewish General Hospital
· A full service university affiliated medical center
a broad range of inpatient and outpatient
· Provides
services
major tertiary and quaternary cardiovascular,
· Has
neurosciences, oncology (including robotic surgery)
·
·
·
TARGETED CHANGES
and colo-rectal programmes
Performs 13,000-15,000 operative procedures per year
To improve patient outcomes and satisfaction the
hospital decided to standardize and expand the
services provided by its Pre-Admission Testing (PAT)
Clinic
·
Patient
Sees
Surgeon
In
Surgeon's
Office
Patient
Goes
Through
Screening
Process
Patient
Undergoes
Procedure
At Hospital
·
Patient
Recovers
Of JGH's Pre-Admission Testing Clinic
· Activities
Patient logistical preparations (phone call)
·
· When to arrive to the clinic
· Expected wait time at the clinic
· Information and preparation for the visit
· Administrative (on-site)
· Register
· Submit insurance information
· Medical (on-site)
· Obtain medical/surgical history
· Perform physical exam
· Adjust medications
· Take ECG
· Perform other tests as required
· Refer abnormal results to surgeon for follow-up
·
Clinic Issues
· Existing
Not enough space:
·
·
·
·
·
·
·
·
·
· Waiting area (wheelchair access)
· Exam rooms
· Individual training rooms
· Administrative work area
Not enough nurses and administrative technicians
Teaching provided only to same day surgery patients
Inefficient patient flow resulting in long wait times
Inconsistent medication adjustments
Inconsistent handling of abnormal results
Not completing charts on-time
Inadequate follow-up
Lack of comprehensive services increases post-op
complications (literature)
· Provide medication reconciliation for all patients
· Provide training for all patients
· Increase nursing and administrative staffing
· Streamline patient flow
· Identify patients with infections and allergies
· Consistently manage abnormal results
· Complete charts at least 72 hours before procedure
Pre-Surgical Screening Clinic Activities
· Proposed
Up to 35 patients/day would do some of the following:
A Simplified View Of JGH's Peri-Operative Process
Patient
Is
Referred
To
Surgeon
By GP
Analysis of the Existing Clinic Indicated Need To:
· Register for the clinic
· Submit insurance information
· Watch a video orientation at the start of visit
· See a pharmacist
· Change into a gown
· Have ECG taken
· See General Practitioner (GP)
· See Internist
· Get dressed
· Provide blood and urine samples
· Receive group or DVD training
· Receive individual training
· Undergo X-rays test
Management Challenges
· Minimizing/determining space requirements
staffing costs (including
· Minimizing/determining
overtime)
· Minimizing/preventing physician idleness
· Minimizing excessive patient waiting
· Handling patients having differing needs
· Handling uncertainty about times needed for each task
· Handling patients' need to see pharmacist
· Handling no shows and cancellations
· Making sure that staff have breaks and lunch
staff and patients while taking into
· Scheduling
consideration the above challenges
REFERENCES
discrete event simulation model to evaluate
· Aoperational
performance of a colonoscopy suite, Bjorn
Berg, Brian Denton, Heidi Nelson, Hari Balasubramanian,
Ahmed Rahman, Angela Bailey, Keith Lindor, Medical
Decision Making (2009), 3, 380-387.
·
Reducing patient wait times and improving resource
utilization at British Columbia Cancer Agency's
ambulatory care unit through simulation, Santibáñez P,
Chow VS, French J, Puterman ML, Tyldesley S., Health Care
Management Science (2009), 12, 392-407.
ANALYTICAL APPROACH
·
SIMULATION ANIMATION SNAPSHOT
Simulation Based Optimization
RESULTS AND CONCLUSIONS
●
· To handle processing time variability
· To facilitate the development of acceptable model
· To facilitate measurement of multiple statistics
· To facilitate what-if analysis
· To facilitate automatic scheduling improvements
Staff And Patient Schedule
Entity Type
Admissions Staff
Type
Count
Times
Arrival
2 6:00 6:00
Break
8:55 8:55
Lunch
11:50 11:50
Registered Nurse
Arrival
3 7:00 7:00 7:05
Break
9:55 9:55 9:50
Lunch
12:25 12:25 12:20
Pharmacist
Arrival
1 7:30
Break
9:30
Lunch
12:00
ECG Technician
Arrival
1 6:25
Break
8:25
Lunch
10:55
Blood Taker
Arrival
1 7:55
Break
10:05
Lunch
12:40
General Practitioner Arrival
2 6:30 7:00
Internist
Arrival
1 10:10
DVD Player
12
Patient
Arrival
35 6:00 6:00 6:00 6:00 6:00
6:00 6:00 6:05 6:05 6:05
6:05 6:05 6:20 6:25 6:35
6:45 7:30 7:30 7:40 7:40
7:50 8:10 8:15 8:20 8:20
8:25 8:40 8:50 9:00 9:05
9:30 9:45 10:05 10:35 11:40
Simulation Model
· TheChallenges
·
entities so as to allow dynamic and
· Modelling
individual decision making
activities involving different entities that
· Modelling
needed to be coordinated
· Simulation modelling approach
· Could have treated:
·Patients as entities
·Staff, exam rooms, DVD players, . . . as resources
· Instead modelled all objects as entities
· Animated state information for each entity type
· Used logic to handle events and entity flows
· Requirements
· Tasks needed for each patient profile
· Patient profile distribution
· Service time distributions
· Count of tasks needing to be done each day
· Actual simulation model data
profiles guesstimates from Subject Matter
· Patient
Experts (SMEs) in existing (PAT) clinic
· Service time distributions
·Triangular distribution guesstimates from SMEs
·Patient self-time studies
·Count of tasks needing to be done each day
· Room allocation issues
· Determine room allocation by physician type
· Preclude room pooling between GP and Internist
· Internist must have two rooms
· Validating the simulation model
· Was difficult
· Plan for Pre-Surgical Screening (PSS) clinic is in flux
· Incomplete data
· Received feedback from management
·Pre-Surgical Screening Clinic Nursing Coordinator
·The Chief Of Surgical Services
·Associate Director Of Professional Services
against schedule with deterministic service
· Tested
times
were sensitive to values of service time
· Results
distribution guesstimates
●
Entity Type
Admissions Staff
Registered Nurse
Pharmacist
ECG Technician
Blood Taker
General Practitioner
Internist
Patient
OPTIMIZATION
·
Optimization Problem
·
Objective function - minimize sum of costs of:
Physician idle time
Staff overtime
Excessive patient waiting time
Constraints
Finish all patient tasks by end of work day
Respect staff break and lunch times
Both general practitioners work in the morning
The sole internist works in the afternoon
At least 8 people in group training sessions
Sending staff home at end of their shift if there is
another staff member who can finish up for them
·
·
·
·
·
·
·
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Optimization Issues And Challenges
·
·
·
·
Need to finish all patient tasks before end of work day
Certain staff had to arrive before other staff
Breaks and lunches had to fit into 8 hour day
Not enough nursing staff
·
Optimization Procedure
·
·
Initially used Optquest to minimize physician idle time
Subsequently developed simple search heuristic
Set initial values for each time variable
Set upper and lower bounds for each time variable
Select initial time increment
Repeat forever
Loop through variables one at a time
Change variable positively and then negatively by
time increment
If solution is feasible evaluate average of total
simulated cost over a pre-determined number of
days
Keep test solution if it is an improvement
Gradually decrease magnitude of time increment
·
·
·
·
Overtime, Idle Time And Excessive Waiting Costs
·
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Source Of Cost
$/Hour
Overtime
$30
Overtime
$80
Overtime
$80
Overtime
$45
Overtime
$45
Idle time
$100
Idle time
$100
Excessive waiting
$20
Mean Total
Hours/Day Cost/Day
0.15
$4.50
0.13
$10.67
0.00
0.00
0.00
0.18
$18.33
0.02
$1.67
2.65
$53.00
$88.17
Simulation Had No Method To Substantiate
· Before
Amount of physician idle time
·
· Amount of staff overtime
· Number of exam rooms
· Staff requirements
A Result
· AsPhysicians
believed they would have a lot of idle time
·
·
· Physicians pushed to each have three exam rooms
· Professional services pushed for less rooms
· No realistic understanding of staff requirements
· No understanding of effect of breaks on throughput
Using Simulation With Different Assumptions
· Was able to determine room requirements
· Was able to quantify expected physician idle time
· Was able to quantify expected overtime
able to identify bottleneck that would have very
· Was
significantly increased excessive patient waiting times
able to improve scheduling for physicians, staff
· Was
and patients