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Proactive Overbooked Routines Through Empiric Noshow Data (PORTEND)

Proactive Overbooked Routines Through Empiric Noshow Data (PORTEND). Ped Bunsongsikul MD 11/19/2013. Problem. Patient No-shows impair our ability to provide excellent access. Potential Solutions. Automatic Overbooking to compensate for anticipated No shows

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Proactive Overbooked Routines Through Empiric Noshow Data (PORTEND)

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  1. Proactive Overbooked Routines Through Empiric Noshow Data (PORTEND) Ped Bunsongsikul MD 11/19/2013

  2. Problem • Patient No-shows impair our ability to provide excellent access

  3. Potential Solutions • Automatic Overbooking to compensate for anticipated No shows • Change provider mindset to the thought that no-shows are undesirable.

  4. Overbooking Intelligently • Development of a method to calculate a number that is assigned to every W. This number is based on the historic no-show pattern for the members scheduled. (Done through a Terradata Query) • That number correlates to the probability that there will be a no-show for that W. If the number reaches a threshold, there is an 80% chance that there will be a no-show for the W. • For these Ws, one of the existing routine appointments is converted to an Overbook. This frees up a routine slot that can be booked by the call center as a routine.

  5. Calculation of the PORTEND output *The Terradata Query is found in the Notes of this slide

  6. Other methods Cumulative PPV vs Cumulative Addons (6 months West Covina) • Calculating Member Historic NoShow Rate For each member • # of prior PCP appointment No Shows/Total # of prior PCP appointments • For each given W, that W’s Historic NoShowRate is calculated • All of the patients prior NoShows / Total # of prior PCP appointments for all the patients • This number is multiplied by the number of routine appointments to get the AdjustedHistoricNoShowRate for the W • For each given W, that W’s Historic NoShowRate Sum is calculated • Sum of the members Historic NoShowRatefor that W • Assuming that the WHistoricNoShowRate predicts the no show rate for each member, the following equation is applied. • 1 – (1- WHNSR) ^n Where n = number of routine appointments. • This gives the exponential method.

  7. Data - 1 Year BPK Family Med July 2013-June 2014 Baldwin Park Family Medicine Physician Providers (23,564 Ws)

  8. 6 Month Family Medicine Data

  9. Variables • Threshold • The threshold level can be adjusted up or down to balance PPV vs Sensitivity • Timing of report • Reports can be run the week prior to allow for scheduling of routine appointments. • The earlier the report is run: • There will be fewer Ws that will meet threshold (decrease sensitivity) • there may be an increase in PPV as there will be more time for appointments to be scheduled. • Increased risk of cancellation which could disrupt the theoretical no-show probability

  10. Other Variables that are not considered • Timing of appointment (AM vs PM) – Not statistically significant • # of supply in the W (I would like to factor it in to help in automation)

  11. Prospective Predictions – Dry Run8/26-9/27 Baldwin Park Family Med

  12. Procedures Original Process • Report run on Thursday • Schedules are adjusted by Thursday Afternoon Current Process • Report run on Wednesday. • The schedules are adjusted by Thursday afternoon

  13. Template

  14. Rules • If you have travel time or other held/IW time in the W, you will not get the addon (IMPAAQT, Inbasket, and CSG IW do not count)

  15. PORTEND Started- 9/30WCO and Montebello Actual Data • Overall 126/159 (79.2%) shifts had a no show • 159 overbooked appointments to compensate for 939 noshows (17%) • Bad Day defined as everyone showing up and 12 patients are seen in the AM or 11 patients seen in the PM.

  16. Physician Impact • Increase in patients seen per half day • Low probability for a ‘bad day’. (7/159 shifts)

  17. Comments BPK Same day appointment protocol includes a Round Robin. This ensures adequate same day access. IMPAAQT is also being done in the BPK Family Medicine (Montebello and West Covina)

  18. Conclusion • It is possible to predict which shifts have a high likelihood of having a no-show. • By overbooking these shifts, it is possible to partially compensate for the anticipated no-shows with only a small chance of overscheduling the providers.

  19. SpecialtiesDermatology

  20. Specialty Cardiology • Not useful

  21. SpecialtiesNeurology

  22. Future Direction • Improvements • Join the Appointment Supply into the Terradata Query • Automation • The PORTEND output number is calculated automatically for each W • For any W that reaches the threshold, the overbook slot becomes bookable by the call center for routine appointments. • Would need a proper schedule template. • Factors • The Call Center software has been delayed until April 2014.

  23. Staff • Local Physician Lead: Ped Bunsongsikul, MD • Local Support Staff: Alma Gallardo, Lisa Ordaz • Schedulers: Gina Gallego, Iverica McDonough

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