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Outpatient Clinics HCM 540 – Operations Management.

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1 Outpatient Clinics HCM 540 – Operations Management

2 Operational Inputs and Outputs - Clinics  Volume by Patient Type  Provider and Support Staffing  Appointment Scheduling Policies  Exam Room Allocation Policies  Patient Flow Patterns Input/Decision Variables  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

3 A High Level Clinic Architecture 1 2 3

4 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 are the basic types of patients served? Appointments, walk-ins, both? Demand for advance appt’s vs. same-day appointments

5 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?

6 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

7 Appointment Templates StartSlotAppointmentPatients TimeLengthTypePer Slot 8:3030NEW1 9:0015Postpartum1 9:1515Follow Up1 9:3015Follow Up1 9:4515Follow Up1 10:0030NEW1 10:3015Follow Up1 10:4515Follow Up1 11:0015Follow Up1 11:1515Follow Up1 11:3015Follow Up1 Template ID: Phys_Mon_AM_OBProvider Type:Physician Day / Time: Monday AMClinic:OB  How does one design good templates?  how many each type?  slot length?  sequencing  Template management  Basis for generation of daily appointment schedules 2

8 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?

9 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?

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

11 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)

12 More about the math of appt 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”, 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

13 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 (11N, 33FU for regular clinic) Overbooked, overrun, excessive patient waits Mr. T suspected the appt system Fundamental issue of matching capacity to demand “systems thinking” view Interfaces 28:5 Sep-Oct 1998 (pp.56-69)

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

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

16 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 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)


18 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” (O’Keefe paper) Interfaces 28:5 Sep-Oct 1998 (pp.56-69)

19 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...

20 Open Access Premise – adjust capacity as needed to meet customer demand accommodate all appointment requests developed by Kaiser Permanente (CA) popularized by Murray and Tantau Three common models traditional access 1 st generation open access 2 nd generation open access

21 Learning More About Open Acces 1.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 access to clinical offices. 2.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)Same-day appointments: exploding the access paradigm. 3.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)Redefining open access to primary care. 4.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)Must patients wait?

22 Traditional Access 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

23 1 st Generation Open Access 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

24 Some Problems with 1 st 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 2 nd Generation Open Access “Create capacity” by doing all today’s work today 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 Challenges predict total demand provider flexibility panel management – how big?, how much work generated by a given panel?

26 Demand Management Upstream 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

27 Many different appt scheduling packages AppointmentsPro One-Call One-Call (Per-Se Technologies) Brickell Scheduler GBS HealthcareData Link 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

28 Computer Simulation & OP Clinics Simulation quite useful for exploring impact of operational inputs on system performance Volume by Patient Type clinical type – times for each step in care routine, soon, urgent Provider and Support Staffing MA-provider ratio pool or dedicated? Appointment Scheduling Policies template design slot length sequencing overbooking Exam Room Allocation Policies rooms per provider pool or dedicated Patient Flow Patterns what gets done where by whom Input/Decision Variables  Appointment Lead Time  Patient Wait Time – initial, for provider, total  Patient Time in Clinic  Length of Clinic Day  Exam Room Utilization  Support Space Utilization  Provider and Support Staff Utilization Performance Measures

29 Simulation provides surprising staffing and operation improvements at family practice clinics (Allen, Ballash, and Kimball) 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 MedModelMedModel – healthcare specific simulation development tool (like ProcessModel on steroids) Paper has very nice description of a typical simulation analysis in healthcare Proceedings of the 1997 HIMSS Conference – handed out in classHIMSS

30 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 – handed out in classHIMSS

31 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 – handed out in classHIMSS

32 Example of a Clinic Simulation Model

33 Visit Flow Diagram - 1

34 Visit Flow Diagram - 2

35 Visit Flow Diagram - 3

36 Visit Flow Diagram - 4

37 Input Parameters and Files  Appointment book  Exam room assignments  No-show rates Input Files 1-3

38 Experimenting with the Model Scenario 1 - Current patient prep done at Vitals Station one exam room per provider Scenario 2 - Prep in Exam Room patient prep done in Exam Room one exam room per provider Scenario 3 - Prep in Exam Room and Two Exam Rooms per Provider

39 Wait Time Components Scenario Comparisons

40 Another use of Simulation Ran set of simulation experiments for range of volumes, exam times, staffing levels, rooms/doc, prep location Developed simple spreadsheet based model using Pivot Tables to find max volume subject to constraints on patient waiting and clinic length Currently developing regression and neural network based prediction models from the simulation experimental output FamPractice_clinic.xls

41 Some Relevant Journals Journal of Medical Practice Management Journal of the American Board of Family Practice Managed Care Quarterly Family Practice Management Medical Group Management Journal

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