Outpatient Clinics
HCM 540 – Operations
Management
Outline
Simulation primer and OP clinic example
Clinic flow, measures, issues
Open access
Mathematics of appointments
Information systems
Clinic operations analysis cases
Simulation for Managers
Many healthcare systems horribly complex
Difficult to estimate impact of changes to system on
performance
Much easier and less expensive to experiment with a model
instead of the real system
Discrete even simulation allows capture of variability and
complex interactions in systems
Handed out two nice introductions to computer simulation for
healthcare managers a few weeks ago:
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Benneyan, J.C., An introduction to using computer simulation in healthcare:
patient wait case study
Mahachek, A.R., An introduction to patient flow simulation for health-care
managers
Example: An outpatient
clinic simulation model
Simulation for Managers
Basic components of a simulation study:
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Study real system to understand problem and need for
simulation
Develop model of real system using simulation software
Concurrently collect data on key inputs to simulation model
(e.g. processing times, arrival rates) as well on on outputs
(wait times) if possible
Verify and validate model
Iterate through above 3 steps, with user involvement, until
everyone satisfied model is reasonable representation of
reality
Conduct controlled experiments with simulation model by
running it for various combinations of input values
Statistically analyze the output from the simulation
experiments to draw conclusions, gain insights, support
decision making
Software – MedModel (ServiceModel), ProcessModel,
Arena, Extend, GPSS, see http://www.informs-cs.org/
Generic Flow Modeled
Arrive
Front
Des k,
Lab
Wait
Clerk
Initial wait
Vital
Signs
Nurse
station
Medical
As sistant
LPN, RN,
charge
nurs e
Sub-wait
Lab
X Ray
Pharm
Exam
Depart
MD, NP
Med As sist
Wait for
Provider
Using Simulation to Support Capacity
Planning - Research
Ran set of simulation experiments for range of
volumes, exam times, staffing levels, rooms/doc,
prep location
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estimate initial wait time, wait time for provider, total time in
clinic, length of clinic session
Developed simple spreadsheet based model using
Pivot Tables to find max volume subject to
constraints on patient waiting and clinic length
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The data is output from the simulation experiements
Currently developing regression and neural network
based prediction models from the simulation
experimental output
Developing decision support tools
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FamPractice_v5.xls, ClinicWhatIfLookup-v4-Example.xls
if interested in collaboration, please contact me
Decision support tool
Charts to d isp lay:
0) Base: Rooms = 2,2,3 Staff=2,3,4
VS = not
Exam = 13
Method = random
VS time = 6
Wait Tim es
1) Set staff to 3
Room = 1,2,3
Scenario 1
System Input Parameters
N u m ber of room s p er p rovid er
N u m ber of su p p ort staff
Vital signs exam location
Mean exam tim e (m ins)
Sched u ling Method
Average vital signs exam d u ration
Scenario 2
2
2
N ot in Exam Room
13
Random
6
Scenario 3
2
3
N ot in Exam Room
13
Random
6
2) Set staff to 3, rooms to 2
VS = in, in, not
2
4
N ot in Exam Room
13
Random
6
Initial Wait
Wait for Provid er
5
35
4
30
4
25
Minu tes
Minu tes
3
3
2
2
20
15
1
10
1
5
0
16
18
20
22
24
26
28
30
32
0
34
16
18
20
22
N u m ber of p atient visits
24
26
28
30
32
34
28
30
32
34
N u m ber of p atient visits
Tim e in Clinic
Session Overru n
80
60
70
50
60
Minu tes
Minu tes
40
30
20
50
40
30
20
10
10
0
0
16
18
20
22
24
26
28
N u m ber of p atient visits
30
32
34
16
18
20
22
24
26
N u m ber of p atient visits
Interest in Clinic/Office
Operations & Management
http://www.ihi.org/idealized/idcop/
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IHIs initiative (started 1999) on the
“Idealized Clinic Office Practice”
Improving Chronic Illness Care
Higher level view
http://www.improvingchroniccare.org/change/index.html
A Robert Wood Johnson Foundation program
Bodenheimer, Wagner, & Grumbach (2002) Improving primary care for patients
with chronic illness, JAMA 288(14), 1775-79.
Bodenheimer, Wagner, & Grumbach (2002) Improving primary care for patients
with chronic illness: The chronic care model, Part 2, JAMA 288(15), 1909-1914.
Some Operational Inputs and Outputs
Performance Measures
Input/Decision Variables
 Volume by Patient Type
 Provider and Support
Staffing
 Appointment Scheduling
Policies
 Exam Room Allocation
Policies
 Patient Flow Patterns
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Quality of care
Appointment Lead Time
Patient Wait Time – initial, for
provider, repeat waits
Patient Time in Clinic
Length of clinic day
Exam Room Utilization
Support Space Utilization
Provider and Support Staff
Utilization
Patient satisfaction
Staff satisfaction
Profitability
A High Level Clinic Model Architecture
balk, renege
Appointment Scheduling
Model
Q
Beneficiary at Risk
Population
Daily Appt
Schedules
2
Appointment
Scheduling
Clerks Telephone
Access
Demand for Appt by
Patient type j
1
Day 1
Provider Appointment
Templates
Day 2
Day 3
Appointment Scheduling Policies
Day n
Walk-In Patients
Day i
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08:00
08:15
08:30
09:00
09:45
FU
FU
PP
1st
FU
Clinic Operations Model
Model Components
Scheduled
Patients
Arrival
Filter
No-Shows
Total Patient
Visits
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Exam Rooms
Support Space
Providers
Support Staff
Patient Flow Patterns
Exam Component Durations
Exam Component Resource
Requirements
 Patient Flow Rules (walk-ins,
late arrivals, no shows)
Performance Measures
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Exam Room Utilization
Provider Utilization
Support Space Utilization
Support Staff Utilization
Patient Wait to Begin Exam
Total Patient Time in Clinic
End of Clinic Day (overtime)
3
A Simple Patient Flow Model
multiple waits
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)
A myriad of questions – demand?
Who is the underlying population to serve?
What is the level of demand that can be
satisfied by a clinic?
How do you manage panels of patients for
providers?
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what is the expected workload generated by a given
panel of patients?
What is the “appropriate panel size”?
What are the basic types of patients served?
Appointments, walk-ins, both?
Demand for advance appt’s vs. same-day
appointments
The Front Desk?
How should the “front desk” be staffed?
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appointment scheduling
patient phone questions
patient check in/out
billing
How long do patients wait on the phone for
scheduling appts, medical questions, billing
questions?
What about information systems to support
patient records, appointment scheduling,
billing?
How is appointment capacity organized?
How much appointment vs. walk-in capacity is
needed?
appointment templates
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how many of each “type” of appointment to offer?
how to best sequence mix of appointments?
how to estimate length of time block for each type
of appt?
leave appt slots open for same day appointments?
 open access concept (Murray and Tantau)
 how many?
how many and how to schedule different
specialty “sub-clinics” within an OP Clinic
Appointment Templates
Template ID: Phys_Mon_AM_OB
Day / Time: Monday AM
Clinic:
Start
Slot
Appointment
Patients
Time
Length
Type
Per Slot
8:30
30
NEW
1
9:00
15
Postpartum
1
9:15
15
Follow Up
1
9:30
15
Follow Up
1
9:45
15
Follow Up
1
10:00
30
NEW
1
10:30
15
Follow Up
1
10:45
15
Follow Up
1
11:00
15
Follow Up
1
11:15
15
Follow Up
1
11:30
15
Follow Up
1
Provider Type:
OB
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2
Physician
How does one design
good templates?
how many each type?
 slot length?
 sequencing
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Template management
 Basis for generation of
daily appointment
schedules
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How is other resource capacity organized?
How many exam rooms per provider?
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are the rooms assigned?
Do patients get appointments with
specific providers?
How much support staff needed?
Where are various clinical interventions
done? Who does them?
How much waiting room capacity is
needed?
Appointment scheduling?
Do you overbook? By how much?
Performance measures for your overall
appointment scheduling process?
How do you measure how long your patients
are waiting for an appointment?
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do you know when they want the appointment
and whether their request was satisfied?
How do you most effectively use appointment
scheduling information systems?
Open Access
Premise – adjust capacity as needed to meet
customer demand
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One attempted response to chronic problem of
delays to see primary care physician
accommodate all appointment requests when
patient wants
developed by Kaiser Permanente (CA)
popularized by Murray and Tantau (MT)
 Developed in early 1990s
 Recent articles in JAMA
Three common models
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traditional access
1st generation open access
2nd generation open access
Learning More About Open Acces
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Must patients wait?
Author: Murray M; Tantau C Source: Jt Comm J Qual Improv (The Joint Commission journal on
quality improvement.) 1998 Aug; 24(8): 423-5 Libraries: 1015 (MEDLINE)
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Redefining open access to primary care.
Author: Murray M; Tantau C Source: Manag Care Q (Managed care quarterly.) 1999 Summer;
7(3): 45-55 Libraries: 158 (MEDLINE)
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Same-day appointments: exploding the access paradigm.
Author: Murray M; Tantau C Source: Fam Pract Manag (Family practice management.) 2000
Sep; 7(8): 45-50 Libraries: 119 (MEDLINE)
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Improving access to clinical offices.
Author: Kilo CM; Triffletti P; Tantau C, and others Source: J Med Pract Manage (The Journal of
medical practice management : MPM.) 2000 Nov-Dec; 16(3): 126-32 Libraries: 104
(MEDLINE)
Improving timely access to primary care: case studies of the advanced access model. Author:
Murray M; Bodenheimer T; Rittenhouse D, and others Source: JAMA 2003 Feb 26; 289(8):
1042-6: 3882
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Advanced access: reducing waiting and delays in primary care. Author: Murray M; Berwick DM
Source: JAMA (JAMA : the journal of the American Medical Association.) 2003 Feb 26; 289(8):
1035-40
Appointment Access Methods
st
Traditional
Capacity
Full/Reservoir
1 Generation
2
nd
Generation
Carve Out
Create
(partial reservoir)
(counter intuitive)
Primary Sorting/
Matching criteria
PCP/Clinical
Clinical/PCP
Provider presence
Holding
Appointments
Minimal
Maximal
Minimal
Overflow/full
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None
Future: Provider driven/
Member driven
Accountability
Appointment slots
Appointment slots
Panel
Unique Issues
 Access provider
driven and poor
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 Supply side variability
and flexibility
 Limited by panel size
Urgent Care
DEM
Future
Provider choice
Other providers
Urgent Care
DEM
Future
Evenings
Mismatches
Tension
Third appt type
Long queue for
routine appt
 Crunch
 “Black Market”
Traditional Access
Stratify demand into urgent and non-urgent
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See urgent now
See non-urgent later
Demand controlled by reservoir of supply
Appts booked to end of queue, schedules get saturated,
little holding of capacity for short-term demand
Often multiple appt types
Emphasis on matching demand to desired physician
Urgent demand “added on” or “worked in”
May lead to long appt lead times
MT argue it artificially increases demand
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Focus on urgent condition only necessitates additional visits
Diverted patients (e.g. different physician) end up coming back
anyway – 1 visit becomes 2 visits
st
1
Generation Open Access
A “carve out” approach
More “patient focused”
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I want to see my doc, and I want to see him/her now
Premise: demand can be forecasted with sufficient
accuracy to allow better matching of capacity to
demand
“Carve out” capacity each day for projected
SDA demand
Urgent vs. Routine appt stratification
Developed by Dr. Marvin Smoller of Kaiser Permanente
See Hawkins, S. “Creating Open Access to Clinic
Appointments in the Henry Ford Medical Group”
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passed out in class
Some Problems with 1st
Generation Open Access
Mismatches between patient and PCP
Definition of “urgent” is fuzzy and changes as day
goes on
Creation of new appt types to meet urgent needs of
patient who can’t come in today
Queues for routine tend to grow
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gets shifted to use urgent capacity
affects phone-in capacity and SDA capacity
Black market or “second appt book” which fills “held”
appts as they come available
2nd Generation Open Access
“Create capacity” by doing all today’s work today
Providers responsible for panel, not appt slots
No distinction between urgent and routine
Appts are taken for the day the patient wants
independent of capacity
Every effort to match patient with PCP
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argued that this reduces “unnecessary demand”
Challenges
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predict total demand
provider flexibility
panel management – how big?, how much work generated
by a given panel?
2nd Generation Open Access
What it is and what it is not…..
It is a theory designed to improve
appointment access and customer
satisfaction.
It is not a rigid formula(s)….each clinic will
implement the theory in the manner that
works best for them.
Demand is not insatiable. Staff is not in
the office until all hours of the day and
night.
How Clinic X tried to convey open access concepts to staff and mgt
Precursors to Open Access
Prospective demand measurement
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track actual demand for appts by patients (when they want slot, not when got slot)
track provider requests for follow-up demand
Panel sizes must be manageable and equitable
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no method can deal with demand>>capacity
tying panel size to workload can be challenging
Must estimate current supply
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# of providers, # of available appointment slots taking into account time each provider is
actually in clinic
Must eliminate backlog of appointments
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temporary increase in capacity through extended hours, weekends, etc.
Reduce # of appt types
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PCP vs other
short and long (e.g. long = 2xshort)
Develop contingency plans
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dealing with short term imbalances in supply or demand
Reduce and shape demand
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continuity of provider
multiple issues at a visit
group visits
non-visit care (education, reference, self-care)
Increase effective supply (especially of bottleneck resource)
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relieve providers of tasks that can be done by other
Review call center processes, staffing, etc. to assure telephone access
Myths and Rumors at Clinic X
Correct Concept
Appointment Scheduling
*
*
*
*
Myth/Rumor
Appointments are scheduled for when the patient
would like to be seen.
Appointment can be scheduled ahead of time (as far
in advance as patient would like)
Patient is driver of when to schedule appointment.
Scheduled with PCP if in the office
*
*
*
Cannot schedule return appointment until
day want to be seen.
PCP has to remain until patient is able to
get to the office.
Must add on as many patients as call to
be seen that day.
Insatiable Demand
*
Patients are added on within a reasonable limit
(contingency plans are developed).
*
Providers are remaining in the clinic until
all hours of the night.
Teaming
*
Providers are encouraged to form teams of 2-4
providers to care for patients.
Teammates are utilized when PCP is out of the office.
Patients still have PCP and see that individual as long
as they are in the clinic.
*
Must have only 2 people per team.
*
*
Panel Size
*
Panel size must be within reasonable limits. (Utilize
Smoller’s demand model to help determine
appropriate size).
*
Panel is allowed to continue to grow
without regard to demand.
Appointment Types
*
The pure theory dictates that there is no differentiation
in appt types.
Many clinics choose to continue with SDA (to maintain
holds in the schedule).
*
*
All appointments have to be 1 slot.
All appointments are considered “routine”
or same day.
*
Overtime
*
Support staff schedule is worked to decrease overtime
and allow for provider support.
*
People are staying late into the night with
little support staff for assistance.
Overall
*
Many clinics are already doing a modified 2nd
Generation Model and there are few changes.
*
Drastic change in the way we do
business.
Questions/Concerns about Open Access?
Under what conditions would OA seem to be most applicable?
When would it not be applicable and if so, are modifications
possible?
What is effect on care for chronic conditions? Will follow-up care
slip through the cracks?
Are we trading wait for an appointment for a wait at the clinic?
What will day to day variation actually look like? How often will
we be working until , say, 8pm?
Effect on staff morale?
How to actually implement?
How to sustain?
How pervasive and successful has it actually been?
Impact on patient satisfaction?
Impact on demand for visits?
More...?
Measurements related to OA
Patient satisfaction:
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Quarterly reports - all
levels of care
Annual access
satisfaction surveys
Provider and staff
satisfaction
Availability of
appointments compared
to model
Lead time for future
appointments and/or
“defect rate”
Percentage of patients
seeing own PCP and %
seeing team member
Telephone performance
compared to standards:
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Average speed to answer
Hold times
Call abandonment rates
Talk times
Panel Size
Visits per month
Resource Based Relative Value Units
Used as relative measure of clinical workload as well as basis for reimbursement by CMS
Developed in late 1980’s by researchers from Harvard in conjunction with HCFA and
physicians from numerous specialties
Adopted in 1992 by HCFA
RBRVUs also used to measure physician productivity
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performance monitoring
incentive plans
comparisons across departments
panel management
resource allocation
Shortcomings as a productivity measure
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medical care has changed since 1988 RBRVU development especially with respect to pre and
post-encounter work
don’t fully account for effort for coordination of care, on-call, supervision of allied health
professionals, remote communication with patients
CPT coding basis not very detailed for E+M (evaluation & management)
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99201-05 for OP visit for new patient, 99211-15 for OP visit for established patient
E+M codes cannot be combined to reflect multiple E+M tasks done at 1 visit
Limited reflection of complexity variation in patient populations, provider experience or quality of
care
See Johnson, S.E. and Newton, W.P. (2002) Resource-based Relative Value Units: A
Primer for Academic Family Physicians, Family Medicine, 34(3), pp. 172-176
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nice overview
references include the original research leading to RBRVU development
Measuring Work Effort – “Panels”
How to translate a panel of patients to workload (# of visits,
RVUs)?
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# of patients not a good measure of work
different patient types generate different numbers and types of
visits
Why might you want to be able to put a workload measure to a
panel of patients? How would you use it?
What are practical difficulties with measuring physician
workload?
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effect of FFS and HMO patients
substitution of specialist and/or ER care for primary care
covering for a colleague
HFMG built regression models based on patient age, sex, and
Ambulatory Diagnostic Group (ADG) to predict workload for a
panel
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Kachal, S.K., Bronken, T., McCarthy, B., Schramm, W., Isken, N. –
Performance measurement for primary care physicians, QQPHS
1996 Conference Proceedings (avail upon request)
Have been using for the last 10 years for a variety of purposes
The Mathematics of Appt Scheduling
tradeoffs between patient & provider wait, length of clinic
day, provider utilization
appt time
last patient
A2
A1
x
A3
A5
A4
x
x
idle
end of exam
patient
wait
x
x
clinic
run over
individual appointments or blocks of patients given same
appt time? (ex: 2 patients at start of day, then individual)
The Mathematics of Appt Scheduling
Decent amount of research on various simplified versions of the
appt scheduling problem
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single patient type usually considered
punctuality often assumed (patients and providers)
simple patient care path (one visit to provider)
Important variables
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mean exam time, coefficient of variation of exam time
number of appts scheduled in a session
punctuality, no-show rates
relative wait cost ratio between providers and patients
Some findings
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need good estimates of exam times
relatively simple rules like scheduling 2 patients at the start of the clinic
and then spacing appts out by mean exam time performed well in
simulation experiments
the “best” schedule depends on your objectives and parameter values
impact on practice has been limited (O’Keefe, Worthington, Vissers)
More about the math of appt scheduling
Handout – annotated bibliography of recent research in
appointment scheduling
Vissers, J. “Selecting a suitable appointment system in an
outpatient setting”, Medical Care, XVII, No. 12, Dec. 1979.
Ho and Lau, “Minimizing total cost in scheduling outpatient
appointments”, Management Science, 38, 12, Dec 1992.
Vanden Bosch, P.M. and D.C. Dietz, “Scheduling and sequencing
arrivals to an appointment system”,
http://www.e-optimization.com/resources/uploads/jsr.pdf
Bailey, N.T.J., “A study of queues and appointment systems in
hospital outpatient departments”, J. Roy. Stat. Soc. B, 14, 185,
1952
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first paper published about the topic of appt systems
Fetter, R.B. and J.D. Thompson, “Patients waiting time and
physicians’ idle time in the outpatient setting”, Health Services
Research, 1, 66, 1966.
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another early classic
Information Technology and Appointment
Scheduling/Practice Management
AppointmentsPro
One-Call (Per-Se Technologies)
Brickell Scheduler
e-MDs
Manage.md (ASP)
The Medical Office
Many more...
The open source movement...
http://www.linuxmednews.com/
Open source practice
management projects
MedPlexus – open source EHR
initiative with AAFP
OSCAR
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dev’d at McMaster in Canada
stand alone appt scheduling vs. integrated with practice management
single appointments vs. series of appointments
comprehensive resource scheduling?
enterprise wide vs. departmental?
integration with existing IS?
remote access?
capacity
price, vendor support, vendor viability
http://www.aafp.org/fpm.xml
Case 1: A Partially Successful OR
Engagement (Bennett and Worthington)
Ophthalmology clinic
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new and follow up patients
Routine, Soon, Urgent
Three ½ day clinic sessions per week
3 docs (11New, 33FollowUp for regular clinic)
Overbooked, overrun, excessive patient waits
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Mr. T suspected the appt system
Fundamental issue of matching capacity to
demand
“systems thinking” view
 User involvement
 Awareness of fit within broader organization
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)
Why might not the clinic be
running smoothly?
Patients late/early
Doctors late
No shows, cancellations
Excessive overbooking
Inappropriate appt
lengths
Highly variable
consultation times
Lack of data about
operations
Walk-ins
Staff absences
Understaffing
Not enough space
Not enough appt
capacity
Poor information
flow
Many more...
Vicious Circle of Insufficient
Capacity and Overbooking
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)
Analysis Highlights
Consideration of both process and organizational
issues
Patients were generally punctual

waited on avg 40 mins to see physician (51 mins
including repeat waits)
Simple model for “clinic appt build up”
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highlighted severity of demand>capacity
 If demand>capacity in long term, no appointment scheduling
magic is going to help
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vacation notice deadline for providers
Simple model to assess impact of lengthening
time between routine visits
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an attempt to decrease demand
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)
Analysis Highlights
Used specialized queueing model to explore
different appt scheduling patterns
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as expected, by spacing out appts further, wait to see
provider decreased but at increase in provider idleness
of course, less appts will also exacerbate the difficulty in
getting an appt
http://www.lums.lancs.ac.uk/staffProfiles/People/ManSci/00000163
Developed list of long term and shorter term operational
strategies
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some were implemented to various degrees
however, not much really changed over 2½ years
OP Clinics are messy, complex, and different constituencies have
different goals and objectives
Simple models and “applied common sense”
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)
Demand Management
Upstream
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population mgt
prevention and wellness
self-care
disease mgt
manage chronic conditions
Downstream
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education
telephone follow-up
lengthen visit intervals
change future point of
service entry
Midstream
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walk-in or call-in
coordinate with ancillary
providers
maximize visit efficiency
match patient to provider
group visits
Case 2: Simulation provides surprising staffing
and operation improvements at family practice
clinics (Allen, Ballash, and Kimball)
Simulation quite useful for exploring impact of
operational inputs on system performance
Intermountain Health Care
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integrated health system based in Utah
> 70 clinics, 840,000 enrollees, 2000 docs
clinics ranged in size, configuration, operating tactics
Developed generic clinic simulation model to explore
impact of different configurations/tactics on
performance
MedModel – healthcare specific simulation
development tool
Paper has very nice description of a typical simulation
analysis in healthcare
Proceedings of the 1997 HIMSS Conference – available upon request
A few highlights and things to note ( from Allen,
Ballash, and Kimball)
Started with “simple” model and added complexity as
needed
Obtained “patient treatment profiles” from healthcare
consulting firm
Fig 3,6 – “Low” MA utilization is “good”
MA team had dramatic positive effect over assigned MAs
– from 6 down to 4 MAs with only 4% ACLOS increase
3 rooms/doc not better than 2 per doc
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wait “moved” from waiting room to exam room
Dedicating exam rooms to docs did not adversely impact
performance – not the bottleneck
Patient scheduling matters at higher workloads
Overbooking had significant negative impact on patient
waits
Proceedings of the 1997 HIMSS Conference – available upon request
A few highlights and things to note ( from Allen,
Ballash, and Kimball)
Used results as springboard to look at IHC clinics
and how they operate
Assessed feasibility of implementing insights
gained from the modeling process
Noted that significant changes (“reengineering”)
of the patient care process will likely change the
results of the analysis
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so, rerun it, that’s the beauty of having a model.
Proceedings of the 1997 HIMSS Conference – available upon request
More Resources
http://www.ihi.org/idealized/idcop/
http://www.improvingchroniccare.org/change/index.html
http://www.aafp.org/x2471.xml
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American Academy of Family Practice
Family Practice Management
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http://www.aafp.org/fpm.xml
Journal of Medical Practice Management
Journal of the American Board of Family Practice
Managed Care Quarterly
Medical Group Management Journal
http://mpmnetwork.com/