This document discusses using analytics and metrics to improve patient access and decision making. It provides an overview of the challenges of selecting the right metrics from the many options available. It then proposes 14 key metrics to measure different aspects of access like timeliness, capacity, and patient experience. The document discusses building dashboards with these metrics tailored for different audiences. It also provides two case studies, with questions, about using metrics and data to diagnose issues at specific healthcare organizations around appointment availability, call center performance, and declining patient volumes.

1. Decisions By Analytics
Duane Reynolds, Senior Engagement Manager, Advisory Board

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Pre-Session Prep
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The Access Metric Quandary
Growth Metrics
Financial
Metrics
Scheduling
Metrics
Patient Experience
Metrics
Contact Center
Metrics
Productivity
Metrics
CFOCEO/COO
CSO
CXO
COOCOO
?
What do I
measure?
Where do I
take action
first?
What are my
data sources?
What is my
measurement
period?
Who should
see what
data?

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Proposing Fourteen Key Metrics of Access
Access Executive Dashboard Metrics
% of Patients
Booked/Scheduled
Average Time to Appt.
(New patients)
3rd Next Available
Appointment
Realized Utilization
(Patient Arriving)
wRVUs/clinical FTE
Capacity
Profile
Timeliness
Profile
Appointment
Yield Profile
Cancellations (Rate)
No Shows (Rate)
Percentage of
Abandoned Calls
Unique Patients
(Panel Size - Primary
Care only)
Demand
Profile
Total Scheduling
Volume
Patient Satisfaction
Telephony systemBilling
system
Scheduling systemPt. Sat.
Vendor
Legend
% of Rescheduled
Appointments
% of Established
Patients
% of New Visits

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METRICSHEREMETRICSHEREMETRICSHERE
METRICSHEREMETRICSHEREMETRICSHERE
METRICSHEREMETRICSHEREMETRICSHERE
Capacity
Profile
METRICSHEREMETRICSHEREMETRICSHERE
Focusing a Spotlight on the Right Metrics for the Right Audience
Envisioning (Representative) Best Practice Dashboards
Executive
Dashboard
Organizational
Aggregate
Service Line
Dashboard
Regional
Drill-down
Regional-
Dashboard
Service Line
Drill-down
Organizational &
Regional Drill-Down
• Panel Size
(Primary Care)
• Total Scheduling
Volume
• Abandoned Calls
Timeliness
Profile
Appointment
Yield Profile
Demand
Profile
Regional Drill-Down
• Panel Size
(Primary Care)
• Total Scheduling
Volume
• Abandoned Calls
Service Line Drill-
down
• Panel Size
(Primary Care)
• Total Scheduling
Volume
• Abandoned Calls
Regional Drill-Down
• Appt. Utilization
(Booking)
• % New Visits
• % of Established
Patients
Regional Drill-Down
• Avg. Time to Appt.
• 3rd Next Avail. Appt.
• Pat. Sat.: Ability to
Get Desired
Appointment
Regional Drill-Down
• Realized Utilization
• wRVUs/cFTE
• Rescheduled Appts.
• Cancellations
• No-Shows
Organizational &
Regional Drill-Down
• Appt. Utilization
(Booking)
• % New Visits
Service Line Drill-
down
• Appt. Utilization
(Booking)
• % New Visits
• % of Established
Patients
Service Line Drill-
down
• Realized Utilization
• wRVUs/cFTE
• Rescheduled Appts.
• Cancellations
• No-Shows
Organizational &
Regional Drill-Down
• Realized Utilization
• No-Shows
Organizational &
Regional Drill-Down
• Avg. Time to Appt.
• 3rd Next Avail. Appt.
• Pat. Sat.: Ability to
Get Desired
Appointment
Service Line Drill-
down
• Avg. Time to Appt.
• 3rd Next Avail. Appt.
• Pat. Sat.: Ability to
Get Desired
Appointment
Red Yellow Green Thresholds & Trend lines MTD/YTD/Target Delineation (service line specific as appropriate)

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Breakout Case Studies
Patient Access Analytics in Action

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Case #1 – Background
Case in Brief: Elite Physician’s Network
• A comprehensive, not-for-profit health system that provides care to residents of Whitby, NH
• Newly established medical group with 215 MDs and 10 APPs led by administrative COO
charged with improving access and financial performance
Clinic schedules are defined and
managed by the practices
wRVUs per cFTE are at the
60th% tile of MGMA
benchmarks
Call management is de-centralized but
it is estimated that the organization
receives ~10,000 calls per day;
Abandonment rate 9%
Medical groups invests an
average of $175,000 per MD
FTE, before downstream
contribution. The majority of
providers are in primary care
Population expected to grow by 5% over
the next five years but your volume has
declined the last three years
Average time to appointment is
30 days for primary care and
70 for specialty care

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Case #1 – Questions
1 2 3You’re the new VP of
access. What are the first
metrics you should
evaluate to understand
key drivers of
performance?
The CSO suggests that
you need more
physicians to grow
volume. What is your
response and what
metrics should be
evaluated?
What data is missing
that would help you
further solve for the root
cause of low patient
volume?
% Booked or
Scheduled, patients
per hour per provider,
cancellation rate and
type
My vote would be
“No, not yet.”
However, she might
consider augmenting
MD providers with
lower cost APPs.
Volume, % of new
visits, wRVU per
cFTE, no show rate,
fill rate
A virtual bottle of
wine to get you
through this exercise!

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Opportunity to Rethink our View of Demand
Segmenting Demand to Inform & Right-Size Capacity (Growth) Requirements
Market-Level Demand
Organizational / Service-Level Demand
Provider-Level Demand
• Risk-Adjusted Panel Size
• New Case Volumes (Proceduralist)
• Referral & Patient Management
• Patient Demographic Trending
• Ambulatory Utilization/Volume Forecasting
• Market Share Analysis (by Service)
• Telephone Scheduling Volumes vs. Attempt/Abandon
• Online Scheduling/Appointment Request Volumes
• Payer/Employer Network Demand & Risk
Arrangements
Met vs. Unmet
Demand
Productivity
Expectations
Growth
Targets
versus
True Capacity by
Service & Region

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Case #2 – Background
Case in Brief: Centralized Call Center Woes for Gotham Clinic
• Gotham Health System is not-for-profit, faith-based institution that provides care to a mostly
urban population
• The medical group is well established with approximately 1,500 MDs providing care across
the network
Alignment on the definition of
1.0 cFTE and session length
wRVUs per cFTE are at the
75th% tile of MGMA
benchmarks
A centralized call center was established
in 2016 but they continue to struggle with
appointment availability and scheduling
errors. The providers despise the call
center and want to go back to
decentralization
Patient satisfaction is in the
bottom 10th %tile nationally for
their peer group. Ability to
speak to the clinic is their
lowest score
Gotham’s no show rate has continued
to climb from 10% to 19% in a two
year period
The average 3rd next available
appointment is 55 Days across
the network

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Case #2 – Call Center Statistics
Metric Target Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17
Average Speed of Answer 45 Seconds 0:00:24 0:00:27 :20 0:00:34 0:00:43 :50 :50 0:00:47
Abandon Rate 3% 2.55% 3.16% 5.76% 4.50% 4.00% 7.00% 10.00% 6.82%
Handles 27,499 32,105 29,265 33,344 29,561 35,697 41,550 49,799
Arrivals 28,857 33,999 31,872 34,568 30,647 37,641 43,342 54,689
QA Accuracy Rate
Simple 99% 98.91% 92.00% 87.00% 99.12% 97.59% 99.12% 97.00% 93.44%
Intermediate 98% 97.71% 99.35% 98.51% 96.72% 98.56% 98.08% 87.00% 91.00%
Complex 97% 97.73% 96.00% 85.00% 98.36% 96.84% 88.00% 88.00% 94.58%
% Appointments Scheduled 34.18% 36.38% 44.40% 44.70% 43.51% 44.25% 38.86% 37.69%
% Non-Scheduling Calls 43.00% 34.00% 51.00% 33.00% 26.57% 40.00% 27.37% 32.00%
Call Metrics
Accuracy and
Quality
Key Disposition
Data Points
Pediatrics Family Med Cardiology Urology Totals
Non-Scheduling 329 616 350 139 1434
General Office Information 132 149 64 42 387
Speak to provider 69 151 79 24 323
Wrong Queue 15 80 66 27 188
Referral 9 58 30 11 108
Med Renewal 14 51 15 4 84
Returning Call from office (non-scheduling) 14 34 40 10 98
Medical Records and Patient History Requests 25 20 10 2 57
No Return call from office/haven’t heard back from office 14 26 3 3 46
Patient Form Inquiry 16 13 5 2 36
Patient Late for Appointment 12 8 17 6 43
Test Result 6 17 5 3 31
Wrong Number – Non-Einstein 2 8 13 4 27
Unable to Verify HIPAA 1 1 3 1 6
Non Scheduling Dispositions - Rollup

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Case #2 – Questions
1 2 3Assuming a full
complement of staff,
what do you suppose
is driving their call
center performance
problems?
What other types of
data might you need to
understand the root
cause of the issue?
What role does the
clinic play in terms of
call center
performance, if any?
Alignment on
management of general
information calls, task
management
expectations, work
queue design, etc.
ACD structure
Avg. Handle time &
after call work time
Process flows
Agent skilling level
Outsized call
volume dependent
on flawed workflow
infrastructure
Staff performance?

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