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1© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Lean Options for Walk-In, Open Access, and
Traditional Appointment Scheduling in Outpatient
Health Care Clinics
© 2008 – Linda LaGanga and Stephen Lawrence
Linda R. LaGanga, Ph.D.Director of Quality Systems
Mental Health Center of Denver
Denver, CO USA
Stephen R. Lawrence, Ph.D.Leeds School of Business
University of Colorado
Boulder, CO USA
Mayo Clinic Conference on Systems Engineering & Operations
Research in Health CareRochester, Minnesota – August 17, 2009
Additional information available at: http://Leeds.colorado.edu/ApptSched
2© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Disclosure: Linda LaGanga, Ph.D.Director of Quality Systems & Operational Excellence Mental Health Center of Denver
The Mental Health Center of Denver (MHCD) is a private, not-for-profit, 501 (c) (3), community mental health care organization providing comprehensive, recovery-focused services to more than 11,500 residents in the Denver metro area each year. Founded in 1989, MHCD is Colorado’s leading provider and key health care partner in the delivery of outcomes-based mental health services.
3© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Agenda
1. Background on Appointment Scheduling
2. Lean Approaches
3. Response to Overbooking
4. Enhanced Models
5. Computational Results
6. Insights and Recommendations
7. Contributions and Future Directions
4© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
1. Background on Appointment Scheduling
5© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Motivation Healthcare Capacity
Funding restrictions Demand exceeds supply Serve more people with limited resources
Manufacturing Scheduling Resource utilization Maximize throughput
Healthcare Scheduling as the point of access
Maximize appointment yield
6© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
2007 Consumer Reports survey of 39,000 patients and 335 primary care doctors (Hitti, 2007) Top patient complaint was about time spent
in the waiting room (24% of patients) Followed by 19% of patients who complained
that they couldn’t get an appointment within a week
Fifty-nine percent of doctors in the survey complained that patients did not follow prescribed treatment and 41% complained that patients waited too long to schedule appointments.
7© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Literature: Appointment Scheduling and Yield Maximization LaGanga & Lawrence (2007)
Clinic overbooking to improve patient access and increase provider productivity. Decision Sciences, 38(2).
Qu, Rardin, Williams, & Willis (2007) Matching daily healthcare provider capacity to demand in
advanced access scheduling systems. European Journal of Operational Research, 183.
LaGanga & Lawrence (2009) Appointment Overbooking in Health Care Clinics to Improve Patient
Service and Clinic Performance, working paper, Leeds School of Business, University of Colorado, Boulder CO (in review)
8© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Literature: Access to Healthcare Institute of Medicine (2001)
Crossing the quality chasm: A new health system for the 21st century.
Murray & Berwick (2003) Advanced access: Reducing waiting and delays in
primary care. Journal of the American Medical Association, 289(8).
Green, Savin, & Murray (2007) Providing timely access to care: What is the right
patient panel size? The Joint Commission Journal on Quality and Patient Safety, 33(4).
9© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
2. Lean Approaches
Rapid Improvement Capacity Expansion (RICE) TeamJanuary, 2008
10© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Lean Approaches Reducing Waste
Underutilization Overtime No-shows Patient Wait time
Customer Service Choice Service Quality Outcomes
11© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Lean Process Improvement in Healthcare Documented success in hospitals ThedaCare, Wisconsin Prairie Lakes, South Dakota Virginia Mason, Seattle University of Pittsburgh Medical Center Denver Health Medical Center
Influences Toyota Production System Ritz Carleton Disney
Hospitals to Outpatient Clinics run by hospitals Collaborating outpatient systems
12© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Lean Process Improvement: One Year AfterRapid Improvement Capacity ExpansionRICE Results
Analysis of the1,726 intake appointments for the one year before and the full year after the lean project
27% increase in service capacity from 703 to 890 kept appointments) to intake new consumers
12% reduction in the no-show rate from 14% to 2% no-show
Capacity increase of 187 additional people who were able to access needed services, without increasing staff or other expenses for these services
93 fewer no-shows for intake appointments during the first full year of RICE improved operations.
13© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Lean Process Improvement:RICE Project System TransformationAppointments Scheduled
and No-Show Rates
050
100150200250300350400450
Mon Tue Wed Thu Fri Mon Tue Wed Thu Fri
Ap
po
intm
en
ts
0%
5%
10%
15%
20%
Appointments
No-Show Rate
Year Before Lean Improvement
Year After Lean Improvement
14© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
How was this shift accomplished? Day of the week: shifted and added
Tuesdays and Thursdays Welcome call the day before Transportation and other information Time lag eliminated
Orientation to Intake Assessment Group intakes
Overbooking Flexible capacity
15© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Recovery Marker Inventory
16© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Lean Scheduling Challenge Choice versus Certainty Variability versus Predictability Sources of Uncertainty / Variability
No-shows Service duration Customer (patients’) Demand
Time is a significant factor Airline booking models?
17© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
3. Response to Overbooking
18© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Reactions to Overbooking Article(LaGanga & Lawrence, 2007) Utility model to capture trade-offs
Serving additional patients Costs of patient wait time and provider overtime
Simulation model Compressed time between appointments More appointments without double-booking Allowed variable service times
Contacted by Newspapers Radio American Medical Association Practitioners
19© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Sample Responses Campus reporter’s visit to student health
center “Not now and never will” Patient waits 15 – 20 minutes New administration, new interests
Morning News Radio “Overbooking leading to increased patient
satisfaction? That just doesn’t make any sense!” Public Radio Interviewer
Benefits of increased access at lower cost
20© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Instant Message Response to News Radio“Overbooking at medical providers is unconscionable. Every
provider I have gone to has a policy of charging a hefty fee to those who miss appointments. Providers rarely, if ever, take into consideration the time and effort a patient must expend to attend an appointment. Extended wait times mean that many patients have to use PTO time or risk losing their jobs in order to obtain adequate medical care. An appointment should be considered a verbal contract. If the patient is a no-show then the provider should be allowed to charge for the visit. However, if the provider cannot see the patient within 30 minutes of the scheduled appointment then the patient should be commpensated [sic] for their time. Providers seem to forget who is ultimately paying the bills. When I get poor service at Macy's I have the option of shopping at Dillards. It is not so easy when it comes to medical care.”
21© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
21
Other Responses Practitioners
Dentists General medicine Child advocacy
How should we overbook? Other options
Lean Approaches Open Access (Advanced Access) Walk-ins
22© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Which one of the following is true about Appointment Overbooking?1. Airline overbooking models are very suitable.
2. Overbooking can be accomplished without double-booking.
3. It is the best choice for increasing service capacity.
4. It is not beneficial when service times are variable.
5. The utility of overbooking depends mostly on the cost of patient wait time.
23© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Which one of the following is true about Appointment Overbooking?1. Airline overbooking models are very suitable.
2. Overbooking can be accomplished without double-booking.
3. It is the best choice for increasing service capacity.
4. It is not beneficial when service times are variable.
5. The utility of overbooking depends mostly on the cost of patient wait time.
24© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
4. Enhanced Appointment Scheduling Model
0%
5%
10%
15%
20%
0 1 2 3 4 5 6 7 8 9 10 11 12
Number Waiting (k)
Pro
bab
ilit
y
25© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Objectives of Research Optimize patient flow in health-care clinics
Traditionally scheduled (TS) clinic Some patients do not “show” for scheduled
appointments TS clinic wishes to increase scheduling flexibility
Some capacity allocated to “open access” (OA) appointments, OR
Some capacity allocated to “walk-in” traffic Balance needs of clinic, providers, and patients
26© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Objectives of Research
Study impact of open access and walk-in traffic When is open access or walk-in traffic
beneficial? What mix of TS, OA, and WI traffic is
best? What are trade-offs of TS, OA, and WI
on clinic performance?
27© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Assumptions A clinic session has N treatment slots
Each slot is d time units long (deterministic) A clinic session then is D=Nd time units in duration
One or multiple undifferentiated providers P Clients serviced by any available provider
Patients can arrive in one of three ways Binomial traditional appointments “show” with probability Poisson open access call-ins with mean (per day) Poisson walk-ins with mean (per appointment slot) Arrivals have equal service priority (undifferentiated)
28© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Characteristics of Model Model flexibility
Appt show rates j can vary by treatment slot j (time of day)
Open access call-in rate can vary by day. Walk-in rate j can vary by treatment slot j Number of providers Pj can vary by slot j Any arrival distribution can be accommodated
Patient arrivals Patients are only seen at the start of a treatment slot
(early arrivals wait for next slot without cost) Patients are seen in order of arrival (FCFS)
29© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Arrival of Scheduled Appointments Appointment arrivals are binomially distributed
sj patients scheduled for treatment slot j Probability of a patient showing is s aj ≤ sj actually arrive
in slot j
; , 1 j js aj kj j
j
sb a s
a
sj = 4, = 70%
0.00
0.10
0.20
0.30
0.40
0.50
0 1 2 3 4 5 6 7 8 9 10
Number of Patients
Den
sity
f(x
)
Binomial distribution has no
right tail
30© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Arrival of Walk-In Patients Walk-ins arrive at some percentage of clinic
capacity Walk-in arrivals are Poisson distributed
Walk-ins arrive at rate per slot wj actually walk-in in slot j
;!
k
jj
ep w
w
0.00
0.10
0.20
0.30
0.40
0.50
0 1 2 3 4 5 6 7 8 9 10
Number of Patients
Den
sity
f(x)
= 1
Poisson distribution has a
long right tail
31© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Arrival of Open Access Patients Open access (OA) calls arrive at a mean rate
equal to some fraction of clinic capacity (e.g., 50%)
Patients call for a same-day appointment Number of OA patients calling on a particular day
is Poisson distributed with mean “Turned away” if no open slots remain that day
Perhaps make an appointment on another day OA patients always show for appointments
32© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Probability of k Clients Waiting
Elements of (rj) can be calculated as
1, ,0 1, , 1 1,0
k
j k j j k j i j k ii
10
,k
jk ij ki
b s p
jk = probability of k clients arriving for service at the start of appointment slot j
jk = probability of k clients waiting for service at start of appointment slot j
Probability of k new arrivals in
slot j
Binomial TS appointment
arrivals
New WI or OA arrivals
None waiting plus k arrivals
Waiting plus arrivals = k
Probability of k waiting at start of
slot j
32
33© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Relative Benefits and Penalties = Benefit of seeing additional client = Penalty for client waiting = Penalty for clinic overtime Numéraire of , , and doesn’t matter
Ratios (relative importance) are important Allow linear, quadratic, and mixed (linear +
quadratic) costs
34© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Linear & Quadratic Objectives
1, 1,1 1
ˆˆ 1ˆ
N k
jk N k N kj k k i k
U A k i kA
S
Linear Utility Function
Quadratic Utility Function
2 21, 1,
1 1
ˆˆ 2 1 1ˆ
N k
jk N k N kj k k i k
U A k i kA
S
Benefit from patients served
Patient waiting penalties during normal clinic ops
Patient waiting penalties during clinic overtime
Clinic overtime penalties
35© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Heuristic Solution Methodology
1. Gradient search Increment/decrement appts scheduled in each slot Choose the single change with greatest utility Iterate until no further improvement found
2. Pairwise interchange Exchange appts scheduled in all slot pairs Choose the single swap with greatest utility Iterate until no further improvement found
3. Iterate (1) and (2) while utility improves4. Prior research: Optimality not guaranteed, but
almost always obtained
36© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
How does Open Access contribute to leaner scheduling?1. It provides a more reliable method of
overbooking.
2. It eliminates the uncertainty of demand for same-day appointments.
3. It guarantees that patients will be seen when they want.
4. It reduces uncertainty caused by no-shows.
5. It eliminates waste caused by unfilled appointments.
37© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
How does Open Access contribute to leaner scheduling?1. It provides a more reliable method of
overbooking.
2. It eliminates the uncertainty of demand for same-day appointments.
3. It guarantees that patients will be seen when they want.
4. It reduces uncertainty caused by no-shows.
5. It eliminates waste caused by unfilled appointments.
38© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
5. Computational Results
0
1
2
3
4
5
6
7
8
9
10
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Net
Util
ity p
er P
rovi
der
Open Access (OA) Traffic (% of capacity)
Walk-ins
Open Access
-6.190
1
2
3
4
5
6
7
8
9
10
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Net
Util
ity p
er P
rovi
der
Open Access (OA) Traffic (% of capacity)
Walk-ins
Open Access
-6.19
39© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Computational Trials 44 sample problems solved Session size N = 12 Appointment show rate = 70% Number of providers P = {1, 2, 4, 8} OA call-in rate = {0%, 10%, …100%} capacity
With P = 4 and N = 12, then = 24 is 50% of capacity Walk-in rate = {0%, 10%, …100%} of capacity
With P = 4, then = 2 is 50% of capacity Quadratic costs
Parameters =1.0, =1.0, =1.5
40© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
50% Walk-Ins (= 0.5)N=12, P=1, =0.7, =1.0, =1.0, =1.5 (quadratic)
0
1
2
1 2 3 4 5 6 7 8 9 10 11 12
Num
ber
of A
ppoi
ntm
ents
Appointment Slot
0
1
2
1 2 3 4 5 6 7 8 9 10 11 12
Num
ber
of A
ppoi
ntm
ents
Appointment Slot
41© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Patients Seen
10
11
12
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Pat
ien
ts S
een
per
Pro
vid
er
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
2 Providers (P=2)10
11
12
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Pat
ien
ts S
een
per
Pro
vid
er
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
2 Providers (P=2)
N=12, P=1, =0.7, =1.0, =1.0, =1.0, =1.5
42© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Patient Waiting Time
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Exp
ecte
d W
aitin
g T
ime
/ Pat
ien
t
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Exp
ecte
d W
aitin
g T
ime
/ Pat
ien
t
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
N=12, P=1, =0.7, =1.0, =1.0, =1.0, =1.5
43© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Clinic Overtime
0.0
0.5
1.0
1.5
2.0
2.5
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Exp
ecte
d P
rovi
der
Ove
rtim
e (d
time
un
its)
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
0.0
0.5
1.0
1.5
2.0
2.5
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Exp
ecte
d P
rovi
der
Ove
rtim
e (d
time
un
its)
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
N=12, P=1, =0.7, =1.0, =1.0, =1.0, =1.5
44© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Provider Utilization
60%
65%
70%
75%
80%
85%
90%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Exp
ecte
d P
rovi
der
Util
izat
ion
OA or WI Traffic (% of capacity)
Walk-Ins
Open Acess
60%
65%
70%
75%
80%
85%
90%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Exp
ecte
d P
rovi
der
Util
izat
ion
OA or WI Traffic (% of capacity)
Walk-Ins
Open Acess
N=12, P=1, =0.7, =1.0, =1.0, =1.0, =1.5
45© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Net Utility
N=12, P=1, =0.7, =1.0, =1.0, =1.0, =1.5
0
1
2
3
4
5
6
7
8
9
10
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Net
Util
ity p
er P
rovi
der
Open Access (OA) Traffic (% of capacity)
Walk-ins
Open Access
-6.190
1
2
3
4
5
6
7
8
9
10
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Net
Util
ity p
er P
rovi
der
Open Access (OA) Traffic (% of capacity)
Walk-ins
Open Access
-6.19
46© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
% of Best Utility
N=12, P=1, =0.7, =1.0, =1.0, =1.0, =1.5
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Util
ity (%
of m
axim
um)
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Util
ity (%
of m
axim
um)
OA or WI Traffic (% of capacity)
Walk-ins
Open Access
47© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
6. Insights and Recommendations
48© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Managerial Implications TS appointments provide better clinic utility
than does WI traffic or OA call-ins Any WI or OA traffic causes some decline in utility
An all-WI or all-OA clinic performs worse than any clinic with some TS appointments Even a relatively small percentage of scheduled
appointments can significantly improve clinic utility Degree of improvement depends on number of providers
A mix of TS appointments with some OA or WI traffic does not greatly reduce clinic performance (utility)
49© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Insights from the Model Loss of utility with WI traffic is due to the long
right-tail of Poisson distribution Excessive patient waiting & clinic overtime
Loss of utility with OA traffic is due to uncertainty about number of OA call-ins
TS appts reduce patient waiting and clinic overtime Binomial distribution has truncated right tail
Multiple providers improves clinic utility Portfolio effect – variance reduction
50© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Managerial Caveats Results (to date) are for “reasonable” utility
parameters Sensitivity analysis currently under way
Attractiveness of WI and OA traffic may improve if they have a higher utility benefit than do scheduled appointments (WI > TS ; OA > TS ) Currently under investigation
51© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
7. Contributions & Future Directions
52© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Contributions of Research Analytic yield management model for health care
clinics with OA traffic First to examine analytically examine combinations
of TS and OA Fast and effective near-optimal solutions Demonstrate the trade-offs of OA traffic
Scheduled appointments provide higher utility Even some appointments improve utility of an all
OA clinic
53© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Future Work Determine sensitivity of results
Utility parameters, number of slots, show rates, linear costs
Show rates, walk-in rates, and providers vary by time of day
Extend model Different utility parameters for appointments and
walk-ins Walk-ins seen before appointments and vice versa Stochastic service times
54© 2008 – Linda LaGanga and Stephen Lawrence© 2009 – Linda LaGanga and Stephen LawrenceMayo Clinic SE/OR 2009
Questions? Comments?Questions? Comments?Lean Options for Walk-In,
Open Access, and Traditional Appointment Scheduling in Outpatient
Health Care Clinics
© 2008 – Linda LaGanga and Stephen Lawrence
Linda R. LaGanga, Ph.D.Director of Quality Systems
Mental Health Center of Denver
Denver, CO USA
Stephen R. Lawrence, Ph.D.Leeds School of Business
University of Colorado
Boulder, CO USA
Mayo Clinic Conference on Systems Engineering & Operations
Research in Health CareRochester, Minnesota – August 17, 2009
Additional information available at: http://Leeds.colorado.edu/ApptSched