1. HCM 540 – Operations Management
Outpatient Clinics
HCM 540 – Operations Management

2. Outline Simulation primer and OP clinic example
Clinic flow, measures, issues
Open access
Mathematics of appointments
Information systems
Clinic operations analysis cases

3. 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:
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

4. Simulation for Managers
Basic components of a simulation study:
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

5. Generic Flow Modeled
Wait for Provider
Initial wait

6. Using Simulation to Support Capacity Planning - Research
Ran set of simulation experiments for range of volumes, exam times, staffing levels, rooms/doc, prep location
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
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
FamPractice_v5.xls, ClinicWhatIfLookup-v4-Example.xls
if interested in collaboration, please contact me

7. Decision support tool

8. Interest in Clinic/Office Operations & Management
IHIs initiative (started 1999) on the “Idealized Clinic Office Practice”

9. Improving Chronic Illness Care
Higher level view
A Robert Wood Johnson Foundation program
Bodenheimer, Wagner, & Grumbach (2002) Improving primary care for patients with chronic illness, JAMA 288(14),
Bodenheimer, Wagner, & Grumbach (2002) Improving primary care for patients with chronic illness: The chronic care model, Part 2, JAMA 288(15),

10. Some Operational Inputs and Outputs
Performance Measures
Input/Decision Variables
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
Volume by Patient Type
Provider and Support Staffing
Appointment Scheduling Policies
Exam Room Allocation Policies
Patient Flow Patterns

11. A High Level Clinic Model Architecture
balk, renege Q 2 1
Models again help to organize our thinking about complex systems 3

12. A Simple Patient Flow Model
multiple waits
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)

13. 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?
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

14. The Front Desk? How should the “front desk” be staffed?
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?

15. How is appointment capacity organized?
How much appointment vs. walk-in capacity is needed?
appointment templates
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

16. Appointment Templates2
Template ID: Phys_Mon_AM_OB Provider Type: Physician
Day / Time: Monday AM Clinic: OB
How does one design good templates?
how many each type?
slot length?
sequencing
Template management
Basis for generation of daily appointment schedules
Start
Slot
Appointment
Patients
Time
Length
Type
Per Slot
8:30 30
NEW 1
9:00 15
Postpartum
9:15
Follow Up
9:30
9:45
10:00
10:30
10:45
11:00
11:15
11:30

17. How is other resource capacity organized?
How many exam rooms per provider?
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?

18. 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?
do you know when they want the appointment and whether their request was satisfied?
How do you most effectively use appointment scheduling information systems?

19. Open Access
Premise – adjust capacity as needed to meet customer demand
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
traditional access
1st generation open access
2nd generation open access

20. Learning More About Open Acces

21. Appointment Access Methods

22. Traditional Access Stratify demand into urgent and non-urgent
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
Focus on urgent condition only necessitates additional visits
Diverted patients (e.g. different physician) end up coming back anyway – 1 visit becomes 2 visits

23. 1st Generation Open Access A “carve out” approach
More “patient focused”
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”
passed out in class

24. 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
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

25. 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
argued that this reduces “unnecessary demand”
Challenges
predict total demand
provider flexibility
panel management – how big?, how much work generated by a given panel?

26. 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

27. Precursors to Open Access
Prospective demand measurement
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
no method can deal with demand>>capacity
tying panel size to workload can be challenging
Must estimate current supply
# of providers, # of available appointment slots taking into account time each provider is actually in clinic
Must eliminate backlog of appointments
temporary increase in capacity through extended hours, weekends, etc.
Reduce # of appt types
PCP vs other
short and long (e.g. long = 2xshort)
Develop contingency plans
dealing with short term imbalances in supply or demand
Reduce and shape demand
continuity of provider
multiple issues at a visit
group visits
non-visit care (education, reference, self-care)
Increase effective supply (especially of bottleneck resource)
relieve providers of tasks that can be done by other
Review call center processes, staffing, etc. to assure telephone access

28. Myths and Rumors at Clinic X
Correct Concept
Myth/Rumor
Appointment Scheduling
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.

29. 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...?

30. Measurements related to OA
Patient satisfaction:
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:
Average speed to answer
Hold times
Call abandonment rates
Talk times
Panel Size
Visits per month

31. 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
performance monitoring
incentive plans
comparisons across departments
panel management
resource allocation
Shortcomings as a productivity measure
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)
for OP visit for new patient, 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
nice overview
references include the original research leading to RBRVU development

32. Measuring Work Effort – “Panels”
How to translate a panel of patients to workload (# of visits, RVUs)?
# 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?
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
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

33. The Mathematics of Appt Scheduling
tradeoffs between patient & provider wait, length of clinic day, provider utilization
appt time
last patient x x x x x
idle
clinic
run over
end of exam
patient
wait
individual appointments or blocks of patients given same appt time? (ex: 2 patients at start of day, then individual)

34. The Mathematics of Appt Scheduling
Decent amount of research on various simplified versions of the appt scheduling problem
single patient type usually considered
punctuality often assumed (patients and providers)
simple patient care path (one visit to provider)
Important variables
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
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)

35. 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
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”,
Bailey, N.T.J., “A study of queues and appointment systems in hospital outpatient departments”, J. Roy. Stat. Soc. B, 14, 185, 1952
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.
another early classic

36. 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...
Open source practice management projects
MedPlexus – open source EHR initiative with AAFP
OSCAR
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

37. Case 1: A Partially Successful OR Engagement (Bennett and Worthington)
Ophthalmology clinic
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
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)

38. 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...

39. Vicious Circle of Insufficient Capacity and Overbooking
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)

40. 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”
highlighted severity of demand>capacity
If demand>capacity in long term, no appointment scheduling magic is going to help
vacation notice deadline for providers
Simple model to assess impact of lengthening time between routine visits
an attempt to decrease demand
Interfaces 28:5 Sep-Oct 1998 (pp.56-69)

42. Analysis Highlights
Used specialized queueing model to explore different appt scheduling patterns
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
Developed list of long term and shorter term operational strategies
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)

43. Demand Management Upstream Midstream Downstream population mgt
prevention and wellness
self-care
disease mgt
manage chronic conditions
Midstream
walk-in or call-in
coordinate with ancillary providers
maximize visit efficiency
match patient to provider
group visits
Downstream
education
telephone follow-up
lengthen visit intervals
change future point of service entry

44. 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
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

45. 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
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

46. 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
so, rerun it, that’s the beauty of having a model.
Proceedings of the 1997 HIMSS Conference – available upon request

47. More Resources http://www.ihi.org/idealized/idcop/
American Academy of Family Practice
Family Practice Management
Journal of Medical Practice Management
Journal of the American Board of Family Practice
Managed Care Quarterly
Medical Group Management Journal