Outpatient Clinics HCM 540 – Operations Management
Operational Inputs and Outputs - Clinics Performance Measures 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 • Volume by Patient Type • Provider and Support Staffing • Appointment Scheduling Policies • Exam Room Allocation Policies • Patient Flow Patterns
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
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
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 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
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?
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)
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 • 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
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)
Vicious Circle of Insufficient Capacity and Overbooking Interfaces 28:5 Sep-Oct 1998 (pp.56-69)
A Simple Patient Flow Model multiple waits 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” • 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)
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/MANSCI/Staff/worthing.htm • 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)
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... Why might not the clinic be running smoothly?
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 • 1st generation open access • 2nd generation open access
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) • 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) • 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) • 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)
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
1st 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
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
2nd 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?
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 • Downstream • education • telephone follow-up • lengthen visit intervals • change future point of service entry
Many different appt scheduling packages • AppointmentsPro • 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
Computer Simulation & OP Clinics Input/Decision Variables • Simulation quite useful for exploring impact of operational inputs on system performance Performance Measures • 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 • 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
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 • MedModel – 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 class
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 class
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 class
Input Parameters and Files 1-3 Input Files • Appointment book • Exam room assignments • No-show rates
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
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
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 • http://mpmnetwork.com/