Outpatient Clinics HCM 540 – Operations Management.

Outpatient Clinics

HCM 540 – Operations Management

Outline

Simulation primer and OP clinic exampleClinic flow, measures, issuesOpen accessMathematics of appointmentsInformation systemsClinic operations analysis cases

Simulation for ManagersMany healthcare systems horribly complexDifficult to estimate impact of changes to system on performanceMuch easier and less expensive to experiment with a model instead of the real systemDiscrete even simulation allows capture of variability and complex interactions in systemsHanded 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 outpatientclinic simulation model

Simulation for ManagersBasic 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/

Generic Flow Modeled

ExamFront Desk,

Sub-waitWait

Arrive

Vital Signs

Nurse station

Clerk Medical Assistant

LPN, RN, charge nurse

MD, NPMed Assist

Depart

LabX Ray

Lab Pharm

Initial wait 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

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

Charts to display: Wait Times

System Input Parameters Scenario 1 Scenario 2 Scenario 3Number of rooms per provider 2 2 2Number of support staff 2 3 4Vital signs exam location Not in Exam Room Not in Exam Room Not in Exam RoomMean exam time (mins) 13 13 13Scheduling Method Random Random RandomAverage vital signs exam duration 6 6 6

3Staff

3Staff

3Staff

3Staff

3Staff

3Staff

3Staff

3Staff

3

Scenario 3Staff

4

Initial Wait

0

1

1

2

2

3

3

4

4

5

16 18 20 22 24 26 28 30 32 34

Number of patient visits

Min

ute

s

Time in Clinic

0

10

20

30

40

50

60

16 18 20 22 24 26 28 30 32 34


Min

ute

s

Wait for Provider

0

5

10

15

20

25

30

35

16 18 20 22 24 26 28 30 32 34


Min

ute

s

Session Overrun

0

10

20

30

40

50

60

70

80

16 18 20 22 24 26 28 30 32 34


Min

ute

s

0) Base: Rooms = 2,2,3 Staff=2,3,4VS = notExam = 13Method = randomVS time = 6

1) Set staff to 3Room = 1,2,3

2) Set staff to 3, rooms to 2VS = in, in, not

Decision support tool

Interest in Clinic/Office Operations & Management

http://www.ihi.org/idealized/idcop/ IHIs initiative (started 1999) on the

“Idealized Clinic Office Practice”

http://www.ihi.org/idealized/idcop/

Improving Chronic Illness Care

http://www.improvingchroniccare.org/change/index.htmlA Robert Wood Johnson Foundation programBodenheimer, 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.

Higher level view

Some Operational Inputs and Outputs

Volume by Patient Type Provider and Support

Staffing Appointment Scheduling

Policies Exam Room Allocation

Policies Patient Flow Patterns

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

Performance Measures

A High Level Clinic Model Architecture

Beneficiary at RiskPopulation

Appointment SchedulingModel

Provider AppointmentTemplates

Appointment Scheduling Policies

AppointmentScheduling

Clerks -Telephone

Access

Demand for Appt byPatient type j

Day 1

Day 2

Day 3

Daily ApptSchedules

Day n

Day i 08:00 FU 08:15 FU 08:30 PP 09:00 1st 09:45 FU Arrival

FilterScheduled

Patients

Walk-In Patients

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)

Clinic Operations Model

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)

Model Components Performance Measures

Total PatientVisitsNo-Shows

1

2

3

Q

balk, renege

A Simple Patient Flow Model

Interfaces 28:5 Sep-Oct 1998 (pp.56-69)

multiple waits

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

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?

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

Appointment Templates

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





Template ID: Phys_Mon_AM_OB Provider Type: PhysicianDay / 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

2

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?

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?

Open AccessPremise – 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

Learning More About Open Acces

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)

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)

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)

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

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

Traditional 1st Generation 2nd Generation

Capacity Full/Reservoir Carve Out(partial reservoir)

Create(counter intuitive)

Primary Sorting/Matching criteria

PCP/Clinical Clinical/PCP Provider presence

HoldingAppointments

Minimal Maximal Minimal

Overflow/full Urgent Care DEM Future Provider choice

Other providers Urgent Care DEM Future Evenings

NoneFuture: Provider driven/Member driven

Accountability Appointment slots Appointment slots Panel

Unique Issues Access providerdriven and poor

Mismatches Tension Third appt type Long queue for

routine appt Crunch “Black Market”

Supply side variabilityand flexibility

Limited by panel size

Traditional AccessStratify demand into urgent and non-urgent

See urgent now See non-urgent later

Demand controlled by reservoir of supplyAppts booked to end of queue, schedules get saturated, little holding of capacity for short-term demandOften multiple appt typesEmphasis on matching demand to desired physicianUrgent demand “added on” or “worked in”May lead to long appt lead timesMT 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

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 demandUrgent vs. Routine appt stratificationDeveloped by Dr. Marvin Smoller of Kaiser PermanenteSee Hawkins, S. “Creating Open Access to Clinic Appointments in the Henry Ford Medical Group”

passed out in class

Some Problems with 1st Generation Open Access

Mismatches between patient and PCPDefinition of “urgent” is fuzzy and changes as day goes onCreation of new appt types to meet urgent needs of patient who can’t come in todayQueues 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

2nd Generation Open Access

“Create capacity” by doing all today’s work todayProviders responsible for panel, not appt slotsNo distinction between urgent and routineAppts are taken for the day the patient wants independent of capacityEvery 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?

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

Precursors to Open AccessProspective 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 clinicMust 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

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.

Myths and Rumors at Clinic X

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 OAPatient satisfaction:

Quarterly reports - all levels of care

Annual access satisfaction surveys

Provider and staff satisfactionAvailability of appointments compared to model Lead time for future appointments and/or “defect rate”

Percentage of patients seeing own PCP and % seeing team memberTelephone performance compared to standards:

Average speed to answer Hold times Call abandonment rates Talk times

Panel SizeVisits per month

Resource Based Relative Value UnitsUsed as relative measure of clinical workload as well as basis for reimbursement by CMSDeveloped in late 1980’s by researchers from Harvard in conjunction with HCFA and physicians from numerous specialtiesAdopted in 1992 by HCFARBRVUs 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)

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

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)?

# of patients not a good measure of work different patient types generate different numbers and

types of visitsWhy 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

The Mathematics of Appt Schedulingtradeoffs between patient & provider wait, length of clinic day, provider utilization

1A 2A 3A

x x

4A 5A

x x x

idle

end of exam

appt time

patientwait

clinicrun over

last patient

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

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)

More about the math of appt scheduling

Handout – annotated bibliography of recent research in appointment schedulingVissers, 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.pdfBailey, 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 systemsFetter, 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

Information Technology and Appointment Scheduling/Practice Management

AppointmentsProOne-Call (Per-Se Technologies)Brickell Schedulere-MDsManage.md (ASP)The Medical OfficeMany more...

stand alone appt scheduling vs. integrated with practice managementsingle appointments vs. series of appointmentscomprehensive resource scheduling?enterprise wide vs. departmental?integration with existing IS?remote access?capacityprice, vendor support, vendor viability

The open source movement...http://www.linuxmednews.com/Open source practice management projectsMedPlexus – open source EHR initiative with AAFPOSCAR

dev’d at McMaster in Canada

http://www.aafp.org/fpm.xml

http://www.brickellresearch.com/appt.htm

http://www.brickellresearch.com/appt.htm

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


Why might not the clinic be running smoothly?

Patients late/earlyDoctors lateNo shows, cancellationsExcessive overbookingInappropriate appt lengthsHighly variable consultation timesLack of data about operations

Walk-insStaff absencesUnderstaffingNot enough spaceNot enough appt capacityPoor information flowMany more...

Vicious Circle of Insufficient Capacity and Overbooking


Analysis HighlightsConsideration of both process and organizational issuesPatients 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 providersSimple model to assess impact of lengthening time between routine visits an attempt to decrease demand


Analysis HighlightsUsed 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/ManSci/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”


Demand ManagementUpstream

population mgt prevention and wellness self-care disease mgt manage chronic

conditions

Downstream education telephone follow-up lengthen visit intervals change future point of

service entry

Midstream 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 performanceIntermountain 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 performanceMedModel – 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

http://www.medmodel.com/

http://www.himss.org/templates/index.asp

A few highlights and things to note ( from Allen, Ballash, and Kimball)Started with “simple” model and added complexity as neededObtained “patient treatment profiles” from healthcare consulting firmFig 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 increase3 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 bottleneckPatient scheduling matters at higher workloadsOverbooking had significant negative impact on patient waits



A few highlights and things to note ( from Allen, Ballash, and Kimball)

Used results as springboard to look at IHC clinics and how they operateAssessed feasibility of implementing insights gained from the modeling processNoted 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.



More Resources

http://www.ihi.org/idealized/idcop/http://www.improvingchroniccare.org/change/index.htmlhttp://www.aafp.org/x2471.xml

American Academy of Family PracticeFamily Practice Management

http://www.aafp.org/fpm.xmlJournal of Medical Practice ManagementJournal of the American Board of Family PracticeManaged Care QuarterlyMedical Group Management Journalhttp://mpmnetwork.com/

http://www.ihi.org/idealized/idcop/

http://mpmnetwork.com/



Outpatient Clinics HCM 540 – Operations Management.

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Transcript of Outpatient Clinics HCM 540 – Operations Management.