Lean Options for Walk-In, Open Access, and Traditional Appointment Scheduling in Outpatient Health Care Clinics

1. Lean Options for Walk-In, Open Access, and Traditional Appointment Scheduling in Outpatient Health Care Clinics Mayo Clinic Conference on Systems Engineering & Operations Research in Health Care Rochester, Minnesota – August 17, 2009 Additional information available at:http://Leeds.colorado.edu/ApptSched © 2008 – Linda LaGanga and Stephen Lawrence

2. Disclosure:Linda LaGanga, Ph.D.Director of Quality Systems & Operational ExcellenceMental Health Center of Denver • The Mental Health Center of Denver (MHCD) is a private, not-for-profit, 501 (c) (3), community mental health care organization providing comprehensive, recovery-focused services to more than 11,500 residents in the Denver metro area each year. Founded in 1989, MHCD is Colorado’s leading provider and key health care partner in the delivery of outcomes-based mental health services.

3. Agenda Background on Appointment Scheduling Lean Approaches Response to Overbooking Enhanced Models Computational Results Insights and Recommendations Contributions and Future Directions

4. Background on Appointment Scheduling

5. Motivation Healthcare Capacity Funding restrictions Demand exceeds supply Serve more people with limited resources Manufacturing Scheduling Resource utilization Maximize throughput Healthcare Scheduling as the point of access Maximize appointment yield

6. 2007 Consumer Reports survey of 39,000 patients and 335 primary care doctors (Hitti, 2007) • Top patient complaint was about time spent in the waiting room (24% of patients) • Followed by 19% of patients who complained that they couldn’t get an appointment within a week • Fifty-nine percent of doctors in the survey complained that patients did not follow prescribed treatment and 41% complained that patients waited too long to schedule appointments.

7. Literature: Appointment Scheduling and Yield Maximization • LaGanga & Lawrence (2007) • Clinic overbooking to improve patient access and increase provider productivity. Decision Sciences, 38(2). • Qu, Rardin, Williams, & Willis (2007) • Matching daily healthcare provider capacity to demand in advanced access scheduling systems. European Journal of Operational Research, 183. • LaGanga & Lawrence (2009) • Appointment Overbooking in Health Care Clinics to Improve Patient Service and Clinic Performance, working paper, Leeds School of Business, University of Colorado, Boulder CO (in review)

8. Literature: Access to Healthcare • Institute of Medicine (2001) • Crossing the quality chasm: A new health system for the 21st century. • Murray & Berwick (2003) • Advanced access: Reducing waiting and delays in primary care. Journal of the American Medical Association, 289(8). • Green, Savin, & Murray (2007) • Providing timely access to care: What is the right patient panel size? The Joint Commission Journal on Quality and Patient Safety, 33(4).

9. 2. Lean Approaches Rapid Improvement Capacity Expansion (RICE) TeamJanuary, 2008

10. Lean Approaches • Reducing Waste • Underutilization • Overtime • No-shows • Patient Wait time • Customer Service • Choice • Service Quality • Outcomes

11. Lean Process Improvement in Healthcare • Documented success in hospitals • ThedaCare, Wisconsin • Prairie Lakes, South Dakota • Virginia Mason, Seattle • University of Pittsburgh Medical Center • Denver Health Medical Center • Influences • Toyota Production System • Ritz Carleton • Disney • Hospitals to Outpatient • Clinics run by hospitals • Collaborating outpatient systems

12. Lean Process Improvement: One Year AfterRapid Improvement Capacity ExpansionRICE Results • Analysis of the1,726 intake appointments for the one year before and the full year after the lean project • 27% increase in service capacity • from 703 to 890 kept appointments) to intake new consumers • 12% reduction in the no-show rate • from 14% to 2% no-show • Capacity increase of 187 additional people who were able to access needed services, without increasing staff or other expenses for these services • 93 fewer no-shows for intake appointments during the first full year of RICE improved operations.

13. Lean Process Improvement:RICE Project System Transformation Year Before Lean Improvement Year After Lean Improvement

14. How was this shift accomplished? • Day of the week: shifted and added • Tuesdays and Thursdays • Welcome call the day before • Transportation and other information • Time lag eliminated • Orientation to Intake Assessment • Group intakes • Overbooking • Flexible capacity

15. Recovery Marker Inventory

16. Lean Scheduling Challenge • Choice versus Certainty • Variability versus Predictability • Sources of Uncertainty / Variability • No-shows • Service duration • Customer (patients’) Demand • Time is a significant factor • Airline booking models?

17. 3. Response to Overbooking

18. Reactions to Overbooking Article(LaGanga & Lawrence, 2007) Utility model to capture trade-offs Serving additional patients Costs of patient wait time and provider overtime Simulation model Compressed time between appointments More appointments without double-booking Allowed variable service times Contacted by Newspapers Radio American Medical Association Practitioners

19. Sample Responses • Campus reporter’s visit to student health center • “Not now and never will” • Patient waits 15 – 20 minutes • New administration, new interests • Morning News Radio • “Overbooking leading to increased patient satisfaction? That just doesn’t make any sense!” • Public Radio Interviewer • Benefits of increased access at lower cost

20. Instant Message Response to News Radio “Overbooking at medical providers is unconscionable.  Every provider I have gone to has a policy of charging a hefty fee to those who miss appointments.  Providers rarely, if ever, take into consideration the time and effort a patient must expend to attend an appointment.  Extended wait times mean that many patients have to use PTO time or risk losing their jobs in order to obtain adequate medical care.  An appointment should be considered a verbal contract.  If the patient is a no-show then the provider should be allowed to charge for the visit.  However, if the provider cannot see the patient within 30 minutes of the scheduled appointment then the patient should be commpensated [sic] for their time.  Providers seem to forget who is ultimately paying the bills.  When I get poor service at Macy's I have the option of shopping at Dillards.  It is not so easy when it comes to medical care.” 

21. Other Responses • Practitioners • Dentists • General medicine • Child advocacy • How should we overbook? • Other options • Lean Approaches • Open Access (Advanced Access) • Walk-ins 21

22. Which one of the following is true about Appointment Overbooking? 1. Airline overbooking models are very suitable. 2. Overbooking can be accomplished without double-booking. 3. It is the best choice for increasing service capacity. 4. It is not beneficial when service times are variable. 5. The utility of overbooking depends mostly on the cost of patient wait time.

23. Which one of the following is true about Appointment Overbooking? 1. Airline overbooking models are very suitable. 2. Overbooking can be accomplished without double-booking. 3. It is the best choice for increasing service capacity. 4. It is not beneficial when service times are variable. 5. The utility of overbooking depends mostly on the cost of patient wait time.

24. 4. Enhanced Appointment Scheduling Model

25. Objectives of Research • Optimize patient flow in health-care clinics • Traditionally scheduled (TS) clinic • Some patients do not “show” for scheduled appointments • TS clinic wishes to increase scheduling flexibility • Some capacity allocated to “open access” (OA) appointments, OR • Some capacity allocated to “walk-in” traffic • Balance needs of clinic, providers, and patients

26. Objectives of Research • Study impact of open access and walk-in traffic • When is open access or walk-in traffic beneficial? • What mix of TS, OA, and WI traffic is best? • What are trade-offs of TS, OA, and WI on clinic performance?

27. Assumptions • A clinic session has N treatment slots • Each slot is d time units long (deterministic) • A clinic session then is D=Nd time units in duration • One or multiple undifferentiated providers P • Clients serviced by any available provider • Patients can arrive in one of three ways • Binomial traditional appointments “show” with probability s • Poisson open access call-ins with mean  (per day) • Poisson walk-ins with mean l (per appointment slot) • Arrivals have equal service priority (undifferentiated)

28. Characteristics of Model • Model flexibility • Appt show rates sj can vary by treatment slot j (time of day) • Open access call-in rate  can vary by day. • Walk-in rate ljcan vary by treatment slot j • Number of providers Pjcan vary by slot j • Any arrival distribution can be accommodated • Patient arrivals • Patients are only seen at the start of a treatment slot (early arrivals wait for next slot without cost) • Patients are seen in order of arrival (FCFS)

29. Arrival of Scheduled Appointments Binomial distribution has no right tail sj = 4, s = 70% • Appointment arrivals are binomially distributed • sj patients scheduled for treatment slot j • Probability of a patient showing is s • aj ≤ sj actually arrivein slot j

30. Arrival of Walk-In Patients Poisson distribution has a long right tail l = 1 • Walk-ins arrive at some percentage of clinic capacity • Walk-in arrivals are Poisson distributed • Walk-ins arrive at rate lper slot • wj actually walk-in in slot j

31. Arrival of Open Access Patients Open access (OA) calls arrive at a mean rate equal to some fraction of clinic capacity (e.g., 50%) Patients call for a same-day appointment Number of OA patients calling on a particular day is Poisson distributed with mean  “Turned away” if no open slots remain that day Perhaps make an appointment on another day OA patients always show for appointments

32. Probability of k Clients Waiting Probability of k new arrivals in slot j Binomial TS appointment arrivals New WI or OA arrivals 32 Probability of k waiting at start of slot j None waiting plus k arrivals Waiting plus arrivals = k • ajk = probability of k clients arriving for service at the start of appointment slot j • qjk = probability of k clients waiting for service at start of appointment slot j Elements of (rj) can be calculated as

33. Relative Benefits and Penalties • p = Benefit of seeing additional client • w = Penalty for client waiting • t = Penalty for clinic overtime • Numéraire of p, w, and t doesn’t matter • Ratios (relative importance) are important • Allow linear, quadratic, and mixed (linear + quadratic) costs

34. Linear & Quadratic Objectives Linear Utility Function Quadratic Utility Function Benefit from patients served Patient waiting penalties during normal clinic ops Patient waiting penalties during clinic overtime Clinic overtime penalties

35. Heuristic Solution Methodology • Gradient search • Increment/decrement appts scheduled in each slot • Choose the single change with greatest utility • Iterate until no further improvement found • Pairwise interchange • Exchange appts scheduled in all slot pairs • Choose the single swap with greatest utility • Iterate until no further improvement found • Iterate (1) and (2) while utility improves • Prior research: Optimality not guaranteed, but almost always obtained

36. How does Open Access contribute to leaner scheduling? 1. It provides a more reliable method of overbooking. 2. It eliminates the uncertainty of demand for same-day appointments. 3. It guarantees that patients will be seen when they want. 4. It reduces uncertainty caused by no-shows. 5. It eliminates waste caused by unfilled appointments.

37. How does Open Access contribute to leaner scheduling? 1. It provides a more reliable method of overbooking. 2. It eliminates the uncertainty of demand for same-day appointments. 3. It guarantees that patients will be seen when they want. 4. It reduces uncertainty caused by no-shows. 5. It eliminates waste caused by unfilled appointments.

38. 5. Computational Results

39. Computational Trials • 44 sample problems solved • Session size N = 12 • Appointment show rate s= 70% • Number of providers P = {1, 2, 4, 8} • OA call-in rate l= {0%, 10%, …100%} capacity • With P = 4 and N = 12, then = 24 is 50% of capacity • Walk-in rate l= {0%, 10%, …100%} of capacity • With P = 4, then l= 2 is 50% of capacity • Quadratic costs • Parameters p =1.0, w =1.0, t =1.5

40. 50% Walk-Ins (l = 0.5) N=12, P=1, s=0.7, p=1.0, w =1.0, t =1.5 (quadratic)

41. Patients Seen N=12, P=1, s=0.7, p=1.0, a =1.0, w =1.0, t =1.5

42. Patient Waiting Time N=12, P=1, s=0.7, p=1.0, a =1.0, w =1.0, t =1.5

43. Clinic Overtime N=12, P=1, s=0.7, p=1.0, a =1.0, w =1.0, t =1.5

44. Provider Utilization N=12, P=1, s=0.7, p=1.0, a =1.0, w =1.0, t =1.5

45. Net Utility N=12, P=1, s=0.7, p=1.0, a =1.0, w =1.0, t =1.5

46. % of Best Utility N=12, P=1, s=0.7, p=1.0, a =1.0, w =1.0, t =1.5

47. 6. Insights and Recommendations

48. Managerial Implications • TS appointments provide better clinic utility than does WI traffic or OA call-ins • Any WI or OA traffic causes some decline in utility • An all-WI or all-OA clinic performs worse than any clinic with some TS appointments • Even a relatively small percentage of scheduled appointments can significantly improve clinic utility • Degree of improvement depends on number of providers • A mix of TS appointments with some OA or WI traffic does not greatly reduce clinic performance (utility)

49. Insights from the Model • Loss of utility with WI traffic is due to the long right-tail of Poisson distribution • Excessive patient waiting & clinic overtime • Loss of utility with OA traffic is due to uncertainty about number of OA call-ins • TS appts reduce patient waiting and clinic overtime • Binomial distribution has truncated right tail • Multiple providers improves clinic utility • Portfolio effect – variance reduction

50. Managerial Caveats • Results (to date) are for “reasonable” utility parameters • Sensitivity analysis currently under way • Attractiveness of WI and OA traffic may improve if they have a higher utility benefit than do scheduled appointments (pWI > pTS ; pOA > pTS ) • Currently under investigation