This document summarizes research analyzing patient scheduling, no-shows, and cancellations at a specialty care weight management clinic. The researchers built a temporal database to capture appointment dynamics over time. They analyzed booking rates, cancellation rates by provider and patient, and the refill rate of cancelled slots. Over 65% of cancellations occurred within 5 days of appointments. The researchers aim to use the database to further investigate clinic issues and develop scheduling and waitlist tools.

1. Analyzing Patient Scheduling, No Shows, and
Cancellations in a Specialty-Care Clinic
Pranjal Singh
Center for Healthcare Engineering and Patient Safety

2. Analyzing Patient Scheduling,
No-Shows, and Cancellations in a
Specialty – Care Clinic
Pranjal Singh
Amy Cohn, PhD
Amy Rothberg, MD

3. Goals of the Talk
• Investigate access in a specialty care setting
– Michigan Weight Management Program
(WMP)
• Utilize a longitudinal database to capture
dynamics of WMP
– Understand how the database is built
• Analyze issues relevant to the program
– Booking rates
– Cancellations
– Refill of emptied slots due to cancellations
3

4. Research Team
4
Amy Cohn, PhD1,2 Amy Rothberg, MD3 William Herman, MD, MPH3
Students1
Anna Learis
Alexander Mize
Yiming Qiang
Joseph Sorenson4
1 Center for Healthcare Engineering and Patient Safety, University of Michigan, Ann
Arbor
2 Department of Industrial and Operations Engineering, University of Michigan, Ann
Arbor
3 Department of Internal Medicine, University of Michigan Health System
4 Department of Computer Science and Engineering, University of Michigan, Ann
Arbor

5. The WMP Program at U of M
5
[1]. Rothberg et al. BMC Obesity (2015) 2:11 DOI 10.1186/s40608-015-0041-9
• The University of Michigan Weight Management
Program (WMP) is a two-year program
o Intense caloric restriction & behavioral change
• Geared towards individuals with critical health risk
• Patients must attend ≥80% of scheduled
appointments
• First month of program requires more
frequent visits
0 6 12 18 24months

6. Problem Statement
6
• Clinic booked at or near capacity, so patients must
schedule far into the future, where personal
schedules are less certain
• Last-minute cancellations leave appointments slots
unused
• Insufficient capacity for patients to adhere to
program timeline

7. Database Approach
7
• Build a temporal MySQL database to evaluate the
dynamic clinic schedule
• Data: 2 spreadsheets received every working day
o Prospective appointment schedules
o Provider availability data
• Store information on a rolling horizon basis in the form
of a snapshot
• Compare consecutive appointment snapshots
o Evaluate and capture the changes in clinic
dynamics

8. Conceptualizing the Database
1. Slot
2. Appointment Opportunity
3. Provider Template
4. Appointment Schedule
5. Appointment Schedule Snapshots
8

9. 1. Slot
• Basic building block of the database
• Represents single 15 minute time period
• Corresponds to a single record in the database
• Defined by:
– Provider Name
– Slot Date
– Slot Begin Time
9

10. 2. Appointment Opportunity
• Appointment Opportunity can be represented by single
Slot or group of consecutive Slots
• Represents single placeholder for a patient appointment
• Appointment Opportunity is typically created expecting:
– New Patient (multiple slots)
– Return Visit (single slot)
• Defined by:
– Appointment Length
– Appointment Type
– Slot Number in Appointment
– Total Number of Slots in Appointment
8:00
8:00
8:15
8:30
Slot
Appointment
Opportunity
1/1
1/3
2/3
3/3
Appointment
Opportunity
OR
Slot
10

11. 3. Provider Template
• Represents all possible Appointment Opportunities over a
given timeframe
• This template is a compilated schedule of each provider’s
general availability to see patients
• This excludes information about intermittent unavailability,
e.g.
– Out of office for conference
– Vacation leave
– Administrative duty
11

12. 4. Appointment Schedule
• Represents Provider Template overlaid with:
1. Each provider’s intermittent unavailability
a. Out of office for conference
b. Vacation leave
c. Administrative duty
2. Patient appointment data
• Some Appointment Opportunities are occupied by
patient appointments
• Rest of Appointment Opportunities remain either:
– Available for scheduling appointments
– Unavailable due to provider unavailability to see patients
12

13. 5. Appointment Schedule
Snapshots
• On each day (M-F), we view the Appointment Schedule
• Each day’s view represents an Appointment Schedule
Snapshot
• We compare two consecutive Appointment Schedule
Snapshots and capture changes from one to another,
e.g.
– Creation of appointment
– Cancellation of appointment
– Rescheduling
– Provider availability
14

14. A Sample View of the Database
14
Appointment Snapshot Date
Date Time 7/26 7/27 7/28
7/28
10:00
A
Cancelled
C
Created
10:15
10:30
10:45 B
B
Cancelled
11:00 Buffer
11:15
B
Created

15. Our Approach vs. Traditional
Approach
15
Traditional View of Appointment Schedules
• Often looks at a static view of the calendar.
• No visibility about information between two time points
– Suppose we look at two static views of an
appointment calendar (e.g. July 1st and July
8th)
• What happens in between
these two time points?
Our View of Appointment Schedules
• Temporal database offers many advantages:
– Capture and quantify information at
multiple levels (e.g. appointment type,
provider, day of week, etc.)
– See how multiple opportunities used for
one appointment
– Aggregating snapshots allows us to solve
problem seen in traditional approach

16. Patient Case Study
Sept 24 25 26 27 28
July
2
Appointment
July
23
Appointment
August
6
Appointment
August
26
September
24
September
28
Key highlights:
1. Multiple opportunities were used for one appointment
2. Late cancellations hurt opportunities for rescheduling
Provider A cancels
Reschedules for Provider B
Provider A cancels
Reschedules for Provider B
Provider A cancels
Reschedules for Provider B
Patient cancels Reschedules for Provider B
Provider A cancels
Reschedules for Provider B
Patient cancels
Provider A cancels
Reschedules for Provider B
Patient cancels
Refilled Reschedules for Provider B
Reschedules for Provider B
Patient cancels
Provider A cancels
Reschedules for Provider B
Patient cancels
Refilled
Reschedules for Provider B
Patient cancels
17
Snapshot Date

17. How Booked is the Clinic?
17
Booked Rate =
Number of Appointments Scheduled
Number of Appointment Opportunities Available
***

18. Who is Cancelling?
18
• Provider
Cancellations
account for over 28%
of all cancellations
• Consider Automated
Reminder System
and MyUofMHealth
Portal to be ”Patient”
Cancellations

19. When do Cancellations Occur?
19
Over 65% of all cancellations occur
within 5 days of the scheduled
appointment

20. Are Cancelled Slots Getting
Refilled?
20

21. Are Cancelled Slots Getting
Refilled?
21

22. What’s Next?
22
• Use the database to investigate other issues
related to the clinic
• Create a scheduling dashboard for short term
booking
• Develop a waitlist simulation tool that provides
easier appointment access

23. Acknowledgements
23
• The Metabolism, Endocrinology & Diabetes
Clinic
• University of Michigan College of Engineering
• University of Michigan Medical School

24. 24
Thank you!
Pranjal Singh
pranjsin@umich.edu
Amy Cohn
amycohn@umich.edu