Improving Patient Access and Adherence to an Endocrine Program€¦ · Pranjal Singh Amy Cohn, PhD...
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Improving Patient Access and Adherence to an Endocrine Program Pranjal Singh Amy Cohn, PhD Amy Rothberg, MD
Transcript of Improving Patient Access and Adherence to an Endocrine Program€¦ · Pranjal Singh Amy Cohn, PhD...
Improving Patient Access and
Adherence to an Endocrine Program
Pranjal Singh
Amy Cohn, PhD
Amy Rothberg, MD
Collaborators of the Project
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William Herman, MD3 Amy Rothberg, MD3
Student
Cara Cheshire1
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
Staff
Yu-Hei Moses Chan, MSE1
Amy Cohn, PhD1,2
Background on the WMP Program
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[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 energy restriction o Behavioral change
• 26 total visits required over two years • Patients must attend ≥80% of scheduled appointments
• First month of program requires more frequent visits
0 6 12 18 24 months
Problem Statement
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Patients waiting too long to get into program Access
Patients are not able to adhere to the program’s structured visit timeline
Adherence
Approaching the Problem
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• We designed a temporal database to analyze:
o Capacity Dynamics o Patient Behavior
• The stages of the database are: 1. Slot 2. Appointment Opportunity 3. Provider Template 4. Appointment Schedule 5. Appointment Schedule Snapshots
Slot
• Basic building block of the database
• Represents single 15 minute time period
• Corresponds to a single record in the database
• Defined by:
o Provider Name
o Slot Date
o Slot Time
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Slot Provider “A” 7/17/2014 9:15 AM
Appointment Opportunity
• Appointment Opportunity represents single placeholder for a patient appointment
• Appointment Opportunities are created expecting:
o Return Visit (Single Slot)
o New Patient (Multiple Slots)
• Defined by:
o Appointment Length
o Appointment Type
o Slot Number in Appointment
o Total Number of Slots in Appointment
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8:00
8:00
8:15
8:30
Slot Appointment Opportunity
1/1
1/3
2/3
3/3
Appointment Opportunity
OR
Slot
Provider Template
• Represents all possible Appointment Opportunities over a given timeframe
• This template is an aggregated schedule of each provider’s general availability to see patients
• This excludes information about intermittent unavailability, e.g.
o Out of office for conference
o Vacation leave
o Administrative duty
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9/14/2015 9/15/2015 9/16/2015
Appointment Schedule
• Represents Provider Template overlaid with:
o Each provider’s intermittent unavailability
o Patient appointment data
• Some Appointment Opportunities are occupied by patient appointments
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9/14/2015 9/15/2015 9/16/2015
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.
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What can we learn from the database?
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From 7/15/2014 – 4/29/2016: New Patients • 25% of scheduled appointments were cancelled • Of cancelled appointments, 90% were rescheduled Return Visits • 34% of scheduled appointments were cancelled • Of cancelled appointments, 76% were rescheduled
Major Questions for Analysis
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Capacity Dynamics
Patient
Behavior
1. How long is an appointment held before it gets cancelled?
1. How easy is it to schedule an appointment “X” weeks into the future?
2. How far into the future are “X” slots available for scheduling?
How easy is it to schedule an appointment “X” weeks into
the future?
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How far into the future are “X” slots available for
scheduling?
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0 5 10 15 20 25
Day
s
Number of Empty Slots
How far into the future are “X” slots available for scheduling? (New Patients)
Mean
Median
Analysis (cont’d)
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How long are people holding their appointments before they cancel? Appointment Create
Date Appointment Cancellation
Date Appointment Date
Appointment Holding Time
Appointment Lifetime
Ratio = Appointment Holding Time
Appointment Lifetime
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N = 56
Analysis (cont’d)
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Freq
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Appointment Holding Time Ratio
Appointment Lifetime ≤ 1 Week
Analysis (cont’d)
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N = 765
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Appointment Holding Ratio
1 Week < Appointment Lifetime ≤ 1 Month
Analysis (cont’d)
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N = 2522
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Appointment Holding Ratio
1 Month < Appointment Lifetime ≤ 3 Months
Analysis (cont’d)
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N = 1306
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Appointment Holding Ratio
3 Months < Appointment Lifetime ≤ 6 Months
Analysis (cont’d)
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N = 541
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Appointment Holding Ratio
Appointment Lifetime > 6 Months
Analysis Summary
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• Difficult to see new patients within the first month of the clinic’s schedule o Variability reduced in long term
• Utilization impacted by appointment holding times
Ongoing Work
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• Look at the impact of seasonality on utilization metrics
• Track utilization of a particular slot over a specified duration of time o How is that slot changing?
• Look at priority of waitlists
Acknowledgements
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• The Seth Bonder Foundation • The Metabolism, Endocrinology & Diabetes Clinic • Center for Healthcare Engineering and Patient Safety • University of Michigan College of Engineering • University of Michigan Medical School
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Thank you!
Pranjal Singh [email protected]
Amy Cohn [email protected]
Amy Rothberg [email protected]
