Outpatient Clinics

1
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

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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
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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 http//www.informs-cs.org
  /

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Generic Flow Modeled
Wait for Provider
Initial wait
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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

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Decision support tool
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Interest in Clinic/Office Operations Management

• http//www.ihi.org/idealized/idcop/
• IHIs initiative (started 1999) on the Idealized
  Clinic Office Practice

9
Improving Chronic Illness Care
Higher level view

• http//www.improvingchroniccare.org/change/index.h
  tml
• 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.

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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

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A High Level Clinic Model Architecture
balk, renege
2
Q
1
3
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A Simple Patient Flow Model
multiple waits
Interfaces 285 Sep-Oct 1998 (pp.56-69)
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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 appts vs. same-day
  appointments

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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?

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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

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Appointment Templates
2
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

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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?

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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?

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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

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Learning More About Open Acces
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Appointment Access Methods
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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

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1st Generation Open AccessA 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

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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 cant 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

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2nd Generation Open Access

• Create capacity by doing all todays 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?

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2nd Generation Open AccessWhat 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
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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 demandgtgtcapacity
• 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

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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 Smollers 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.
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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...?

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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

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Resource Based Relative Value Units

• Used as relative measure of clinical workload as
  well as basis for reimbursement by CMS
• Developed in late 1980s 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
• dont 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 EM
  (evaluation management)
• 99201-05 for OP visit for new patient, 99211-15
  for OP visit for established patient
• EM codes cannot be combined to reflect multiple
  EM 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
• nice overview
• references include the original research leading
  to RBRVU development

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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

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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)

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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 (OKeefe,
  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. 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
• 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

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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
• devd 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
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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 285 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...

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Vicious Circle of Insufficient Capacity and
Overbooking
Interfaces 285 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 demandgtcapacity
• If demandgtcapacity 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 285 Sep-Oct 1998 (pp.56-69)
41
(No Transcript)
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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
• http//www.lums.lancs.ac.uk/staffProfiles/People/M
  anSci/00000163
• 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 285 Sep-Oct 1998 (pp.56-69)
43
Demand Management

• Upstream
• 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

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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
• gt 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, thats the beauty of having a model.

Proceedings of the 1997 HIMSS Conference
available upon request
47
More Resources

• http//www.ihi.org/idealized/idcop/
• http//www.improvingchroniccare.org/change/index.h
  tml
• http//www.aafp.org/x2471.xml
• American Academy of Family Practice
• Family Practice Management
• 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/