Christopher Scaven, Renuka Sundaresan, David Sweeney, and Jordan Holland - Exploring an innovative access model that flips conventional scheduling theory on its side and sidesteps traditional access barriers.

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research technology consulting
Advanced Access &
Predictive Analytics
Christopher Scaven, D.O., Medical Director, Einstein Healthcare Network
Renuka Sundaresan, Director of Patient Access, Einstein Healthcare Network
David Sweeney and Jordan Holland, Advisory Board

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Road Map
Advanced Access Overview
Case Study: Einstein Primary Care Advanced Access
No-Show Predictive Analytics Tool

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Redefining Access to Care for Patients
Up-ending The Scheduling Status Quo Through Innovative Modeling
Source: Murray, M. & Berwick, D., “ Advanced Access: Reducing Waiting and Delays in Primary Care,” JAMA, February
26, 2003—Vol 289, No. 8
• Majority of appointments offered same day1
• Staggered work-down period of scheduled appointments
• Disciplined and proactive patient care management protocols
“Advanced access tries to close this gap in time between
supply and demand for all demand, routine as well as urgent”
—Murray & Berwick
“Doing Today’s Work, Today”
”
Process Steps
Develop strong patient care prompt
and follow-up management protocols
Work down current
appointment backlog
Educate & reset patient expectations
Develop process flow for PSRs and
providers
Align true provider demand & capacity
Advanced Access
• Schedule appointments no greater than
2 weeks from time of check-out
• Provide patients with appointment
reminder call to make appointments at
future designated date
• Patient appointment reminder created in
AeCIS pool (newly developed) for PSRs
to schedule for patients who do not call
the office as instructed

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Benefits of Advanced Access
Benefits of Adopting
Advanced Access
Improves
Satisfaction of
Stakeholders
Aligns true
demand and
provider
capacity
Reduces Re-work
and Overrides
Grows
Practice
Volume
Reduces 3rd
Next
Appointment
Improves
Scheduling
Performance
Streamlines Visit Types

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A Strategic Roadmap Lays the Path
Advanced Access Sidesteps Traditional Access Barriers & Drives
Dramatic Improvements
Key Components Value Proposition & Benefits
Advanced Access Timeframe
• No-show “cliff” identification
• No “pre-booking” until 10-14
days before to date of service
Significantly improves:
• 3rd next available
• No-show rate
• Staff workload
• Appointment flexibility
Care Team Appointment
• Shifting schedule up 10-15
minutes to build in care team
appointment (registration,
intake)
Significantly improves:
• Flow of clinic patient
• Sustainability of production
• Sustainable cadence of care
Access Standardization
• Appointment type
• Appointment duration
• Criteria mapping
Significantly reduces:
• Complexity and scheduling error rate
• Provider specific inefficiency
• Unusable schedule time “fragments”

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Road Map
Advanced Access Overview
Case Study: Einstein Primary Care Advanced Access
No-Show Predictive Analytics Tool

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Our Impetus for Change
Many of our providers function on a traditional scheduling model in which the physician sets up
his/her own protocol around appointment type and length
This creates four different challenges:
1. It results in non-standard work for our schedulers who must ascertain preferences depending
on the physician involved
2. It leaves unusable time gaps in the schedule, limiting patient throughput
3. It results in call center scheduling errors and suboptimal performance
4. It creates extra work for our clinicians to fit patients in for same-day access
 Non-standard appointment types have been developed as a workaround
43
Different
Appointment
Types Across
Primary Care
Days Average for
3rd Next Available
Appointment26
Traditional Scheduling Continues To Be An Obstacle For Patient
Experience and Timely Care

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Schedule Build Advanced Access
Prep Work
Advanced Access
Process Flow Training
1 2 3
~2 Weeks ~2 Weeks
Creation of timely access to both new and existing patients
~4 Hours
• Back End Build
 Slot types
 Link resource lists
 Build template
• Build Visit Type
 Associate slots, resources,
templates
• Push Schedule to Go Live
• Reduce patient back log
• Confirm patient visits
• Overbook when needed
• Additional clinic sessions
• Contact patients for
scheduling
• Patient education
• Creation and training on
appointment reminder pool
protocol
• Role-based training via
webinars
• Interactive CBT exercises
• Practice observations for
live-demo
Advanced Access Implementation Plan

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Primary Care (ECHA & EPM) Realizes Significant Improvements In
Several Metrics
Noticeable Changes Occur Upon Implementation
• 20 Primary Care
practices
(Dec ‘16 – Mar ’17)
• 5 Orthopedics practices
(July ’17)
• 6 Cardiology practices
(Aug ’17)
• 13 remaining
departments
(~60 – 70 practices)
Expanding Across
Einstein Health NetworkNo Show
Rate (%)
10% improvement
from Nov ‘16 – May ‘17
Requested Appt.
Unavailable
27.7% improvement
from Nov ‘16 – May ‘17
Staff Booking Overrides
(schedule inefficiency)
65.7% improvement
from Nov ‘16 – May ‘17
EHN Representative Outcomes (Primary Care)
1Annual Financial Impact of
10% No-Show Reduction
(Primary Care)
$1.1M 30+ to 10
Reduction of Days in 3rd
Next Available Appointment
(Primary Care)

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Road Map
Advanced Access Overview
Case Study: Einstein Primary Care Advanced Access
No-Show Predictive Analytics Tool

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Predictive Analytics Can Mitigate
No-Shows Before They Occur
Analytic Tool Provides Data-Driven Approach to Mitigating Impact of
Patient No-Shows
Increased
Appointment Utilization
Mitigate the impact of non-
utilized provider time without
the risk of practice overload
due to overbooking – down to a
specific appointment slot at the
provider level
Customized
Population Insight
Intuitive data analytics model
specific to EHN’s patient
population with the ability to
customize analytic criteria
(patient demographics and
behavior)
Individualized
Mitigation Strategy
Ability to drill-down to patient-
level detail to determine no-
show remediation strategies
best suited for individual
practices
Customizable, real-time reporting & workflow tool that
identifies likely-to-no-show appointments and recommends strategic over-booking
strategies based on a dynamic self-updating patient data algorithm
No-Show Predictive Analytics Tool

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Criteria Selection
OUTPUTINPUT
Direct feed of data pushed from
Cerner system to shared driven
accessible by all practice managers
1
Selection of desired department,
location, and appointment date
2
Aggregation of selected patient
data to quantify predicted patient
behavior by provider
3
Based on initial
assessment of significant
factors on patient
behavior – identify top 5:
1. Visit Type
2. Payer Class
3. Patient Age
4. Clinic Location
Density
5. Historical No-Show
Behavior of Patients
• Visualization of the
calculated likelihood
for patient no-show
behavior at a practice,
provider, and patient
view
• Strategic patient
outreach
recommendations to
mitigate lost access
and revenue through
precise overbooking
• Insight into patient
population behaviors
at provider level
Identified Einstein
Criteria
Predictive Analytics
PROCESSING
Automated data feed
from Cerner
Shared access drive Daily predictive tool output
Einstein Established an Automated Process for Tool

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4,500 Suggested Overbookings Across One Month
Initial Tool Results Utilize One Month of Data Post-Implementation
No-Show Predictive Tool
Source: Cerner Scheduling Data 6/16/2017 through 7/15/2017
Suggested Overbookings by Specialty
1,124
931
820
557
174 147 120 102 92 87
0
200
400
600
800
1,000
1,200
OB/GYN Primary Care Pediatrics Orthopedics Med. - CPC Cardiology Ophthalmology Urology Med. - Endo Med. - GI
Data from 6/16 – 7/15/2017
2,875 Suggested
overbookings across OB/GYN,
Primary Care and Pediatrics
4,509 potential overbooks across one month | 54,000 potential overbooks annually
Tool Accuracy Evaluation
Strong Correlation Low Error Rate Significant Predictability
95% correlation
between predictive and
actual no-show rates
<5% error rate – errors
occur when the tool
suggests a number of
overbooks in excess of
actual no-shows
75%+ of actual no-
shows were flagged as
potential no-show targets
within the tool