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DATA-DRIVEN APPOINTMENT SCHEDULING IN THE PRESENCE OF NO-SHOWS Michele Samorani Linda LaGanga October 16 2012.

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Presentation on theme: "DATA-DRIVEN APPOINTMENT SCHEDULING IN THE PRESENCE OF NO-SHOWS Michele Samorani Linda LaGanga October 16 2012."— Presentation transcript:

1 DATA-DRIVEN APPOINTMENT SCHEDULING IN THE PRESENCE OF NO-SHOWS Michele Samorani Linda LaGanga October 16 2012

2 The No-Show Problem 0 Mental Health Center of Denver (MHCD) 0 Large nonprofit organization 0 36% of the appointments were no-shows! 0 MHCD can’t charge for not showing 0 MHCD already uses reminder calls 0 Progress in reducing no-shows for psychiatrist appointments 0 About 25% on average, and varies between doctors 0 What can they do?

3 Solution 1: Open Access 0 Show rate decreases if “lead time” increases 0 Give only same-day or next-day appointments 0 If too many patients call in for same-day appointments, defer them to tomorrow (Robinson and Chen 2010) Same-dayNext-day2 days3 days4 days Show rate.87.74.75.72.71

4 Solution 2: Overbooking 0 Compress slots (LaGanga and Lawrence 2007) p ppp 12:0011:30 11:0010:30 10:00 9:309:00 Regular Scheduling Overbooking pp p p p 9:009:20 9:40 10:00 10:2010:40 11:00 11:20 11:40 p p pp 12:00 pp p p p 9:009:20 9:40 10:00 10:2010:40 11:00 11:20 11:40 p p pp 12:00 Lucky Case Low waiting time Low overtime Unlucky Case High waiting time High overtime Overbooking

5 Data-Driven Appointment Scheduling

6 Solve Scheduling Problem Appointment Request Classification Rule Day & Slot Day2:00 PM2:20 PM2:40 PM 0 1 2 Current Schedule Current day SSN S S S S N Use Analytics to Schedule Appointments Scheduling Horizon (h) Show in day 0 Show in day 1 No-Show in day 2 Day-dependent show outcomes! Lead time Personal schedule Day of week Weather Minimize overtime and waiting time

7 Goals 0 Understand the causes of no-shows (descriptive analytics) 0 Accurately predict show outcomes (predictive analytics) 0 Optimally schedule appointments (prescriptive analytics) 0 The scheduling policy must be practical (descr. + prescr. anlyt.) 0 Provide guidelines on clinic design (prescriptive analytics)

8 Classification Rule Solve Scheduling Problem Appointment Request Day & Slot Classification Rule Understand the causes of no-shows (descriptive analytics) Accurately predict show outcomes (predictive analytics) Optimally schedule appointments (prescriptive analytics) The scheduling algorithm must be interpretable (descr. + prescr. anlyt.) Provide guidelines on clinic design (prescriptive analytics)

9 0 Any Classification algorithm requires a mining table 0 Typically, the mining table is built manually 0 We build it automatically 0 Through Propositionalization (Samorani et al. 2011) Classification Rule AppointmentAttribute 1…Attribute nShow? 1D12…12.3Yes …………… 56,000Q21…0.0No

10 Appointment Service Type Day of week Location Lead time Show outcome Client Gender Diagnosis Age Recovery Markers Employment Housing … Staff Discipline Code 0..N 1 1 1 Propositionalization 1. Pick a path starting from Appointment 2. “Roll-up” attributes 3. Add a new attribute to the table Appointment  More than 3,000 attributes built in 3 hours! Age of the client

11 0 What attributes are most discriminant? 0 Lead time 0 Location 0 Previous no-show rate 0 Service type 0 Staff characteristics: 0 Number of times they performed group therapy 0 Number of times they performed case management 0 Number of times at a certain location New Knowledge! Expected Unexpected

12 Performance frontier Prediction Quality 0 Sensitivity = accuracy among the non-showing appointment requests 0 Specificity = accuracy among the showing appointment requests 0 Cost-sensitive classification to shift quality towards sensitivity or specificity UB LB

13 Classification Rule Solve the Scheduling Problem Appointment Request Day & Slot Solve Scheduling Problem Understand the causes of no-shows (descriptive analytics) Accurately predict show outcomes (predictive analytics) Optimally schedule appointments (prescriptive analytics) The scheduling algorithm must be interpretable (descr. + prescr. anlyt.) Provide guidelines on clinic design (prescriptive analytics)

14 Mathematical Model -Patient categories for waiting time -Day- and patient- dependent revenues -Solved via column generation

15 Interpret the output of the scheduling algorithm Solve Scheduling Problem Appointment Request Day & Slot Classification Rule Understand the causes of no-shows (descriptive analytics) Accurately predict show outcomes (predictive analytics) Optimally schedule appointments (prescriptive analytics) The scheduling algorithm must be interpretable (descr. + prescr. anlyt.) Provide guidelines on clinic design (prescriptive analytics)

16 Develop a Heuristic 0 Target sequence: SSNSSN 0 A further analysis reveals that 0 No-shows tend to be scheduled far in advance 0 Shows tend to be scheduled in the near future 0 Heuristic:  Schedule predicted shows soon in S-slots  Schedule predicted no-shows far in the future in N-slots -2.1% profit compared to optimal procedure 0 Let’s analyze the output of the scheduling algorithm:

17 Guidelines on clinic design Solve Scheduling Problem Appointment Request Day & Slot Classification Rule Understand the causes of no-shows (descriptive analytics) Accurately predict show outcomes (predictive analytics) Optimally schedule appointments (prescriptive analytics) The scheduling algorithm must be interpretable (descr. + prescr. anlyt.) Provide guidelines on clinic design (prescriptive analytics)

18 Sensitivity and Specificiy *Bars and labels = profit Same-day scheduling is worst if prediction quality is high Sensitivity and Specificity regulate the trade-off between Patients seen and Wait. / Over times

19 Comparison to Open Access PolicyProfitWait time (minutes)Overtime (minutes) Open Access6.050.0048.79 HP (.9,.5)7.1414.7415.22 HP (.7,.7)6.9215.5820.47 HP (.6,.8)6.8215.4222.97 Lower Bound6.4418.1431.11 Upper Bound7.895.180.37 +18.0% +10.9% (benefit of analytics) 0 It can be shown that analytics is less beneficial for shorter scheduling horizons

20 At MHCD

21 0 Low show rate  Shift prediction quality to high sensitivity 0 High show rate  Shift prediction quality to high specificity

22 *In selected MHCD clinics with low show rate, assuming capacity = 8, slots = 12 Benefits of Analytics Heuristic L. Bound

23 Benefits of Analytics *In selected MHCD clinics with low show rate, assuming capacity = 8, slots = 12 Heuristic L. Bound

24 Conclusions

25 Contributions and Managerial Insights 0 Find causes of no-shows 0 Develop a dynamic scheduling algorithm that uses individual day- dependent no-show predictions 0 Develop an effective heuristic procedure that is interpretable and easy to implement 0 Find that same-day appointment is the worst policy if predictive analytics is used 0 Outperform open access by 18% at MHCD 0 Reduce system variability

26 Innovation in Analytics 0 Descriptive Analytics: 0 Propositionalization to find new knowledge 0 Predictive Analytics: 0 Cost-sensitive classification to favor one of the two conflicting objectives 0 Prescriptive Analytics: 0 Suggest when to lean towards sensitivity or specificity 0 Study the output of optimization through analytics

27 Implementation at MHCD 0 The implementation of the scheduling system at a MHCD clinic is currently in progress 0 First phase (DONE): implement an “observer” 0 Second phase: implement it in a real clinic

28 Thank you for your attention

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