Moving From Hindsight to Foresight –
Unlocking the 1% Challenge
Young Lee – Deloitte National Health Services
May 29, 2013
Faculty/Presenter Disclosure
Presenter: Young Lee
No Conflicts to Disclose
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Objectives of Today’s Session
• Setting the Context
• Hindsight – Insight – Foresight
• Enabling Improvement Through Advanced Analytics
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A portrait of our healthcare system
“Ontarians regard health care as the single most important public policy issue;
and they will not tolerate anything that causes deterioration in access and
quality of care” – Drummond Report
• There is no shortage of literature and studies that suggest our health system
is not meeting the needs of those who need it most
• Investments have been made to implement a variety of strategies and
programs to improve both the quality and efficiency of service delivery
• However, the solutions implemented have not addressed the overall health
system pressures and dynamics at play in managing patient flow and care
transitions
As a result, the top 1% consumes 33% of all health-care dollars and the top
5% consumes two-thirds*
* Deb Matthews – Ontario Minister of Health and Long-Term Care
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Understanding who is using our health system
• As a health system, a wealth of data exists to inform our improvement
strategies
• Analysis typically has focused on examining service utilization data points
such as patient volumes, patient demographics, diagnostic segmentation, and
process metrics (e.g., wait times), instead of solutions that meet the
specific requirements of high-needs patients
• The limitation of this traditional approach lies in the fact that those who use
the health system the most frequently and have the greatest need are
relatively small in number
• Our traditional approaches and lessons learned inform us that we need to
leverage our hindsight to better manage the top 1% and 5% users
Advanced data analytics can enable us to seek out these patient profiles to
better understand how to manage these users of the health system
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Segmenting patients and their healthcare consumption
through the use of Advanced Data Analytics
An illustrative example of patient emergency department (ED) consumption:
• There are 3 important
segments of patient
profiles that need to be
properly managed:
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Group 1 – Infrequent
users
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2
Group 2 – Frequent
users (the Top 5%)
Group 3 – the Top 1%
Patient flux – patient profiles are not static, which means patients easily move from group-to-group, and
thus need to be actively managed, to prevent conversion from Group 1 to either Groups 2 or 3
Leveraging advanced data analytics to better manage patient profiles
1 – Proactively manage this group to prevent patients from becoming a frequent user of the
ED;
2 – Disrupt the cycle and transition these patients out of this group towards Group 1
3 – Manage the Top 1% by understanding who they are, what their needs are, and how to
meet their needs
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Examining the Top 1%
An illustrative example of patient emergency department (ED) consumption:
1
3
2
Group 3
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•
A small proportion of patients (6.5% or 4,280 patients) are seen 3 or more times within
a 1-year period by a hospital’s ED, consuming 25% of all ED time
•
An even smaller proportion of patients (0.6% or 395 patients) visit the ED 6 times or
more per year
•
The patient profile of the frequent users are not merely represented by older patients;
as such, patient needs should be assessed and commitment should be made to better
manage these patients to shift them out of Group 3 and into Group 1
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How do we unlock the Top 1%?
Hindsight
Insight
Foresight
Broad historical reporting on key
performance indicators.
Statistical analyses (e.g.
profiling and segmentation) help
organizations understand
historical performance.
Advanced analysis, machine
learning and modeling predict
future performance.
What happened?
Why did it happen?
What could happen?
30%
Readmission Rate
30%
20%
Chest Pain
15%
Joint Replacement
10%
PTCA
Alpha
Primary DX: 996.12
(Mechanical Complication of
vascular device / implant)
Readmission
Propensity
84%
180 day horizon
History: Anemia, CHF, Hypertension
Psychoses
Heart Failure & Shock
1 4 7 10131619222528313437404346495255586164
Age
Patient ID: X12345
Age: 29 Sex: Male
Esophagitis and Gastroentritis
5%
20%
DRG
Spinal Fusion
Back & Neck Proc
0%
0%
10% 20% 30% 40% 50%
Readmission Rate
Transport Claims
100%
80%
60%
40%
20%
0%
0
Rx History: G.I. Drugs, Beta blockers,
Diuretics, Antihypertensives, Nitrates,
Anticoagulants, Hypnotics
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2 3 4 5 6 7 8 9 10 11 ≥12
Number of Claims in Past 90 days
5%
70%
Delta
60%
Length of Stay
50%
40%
30%
20%
10%
0%
0%
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Week
70%
60%
50%
40%
30%
20%
10%
0%
CHF
100%
Rx History
Readmission Rate
10%
Charlie
Readmission Rate
Bravo
15%
Readmission Rate
Readmission Rate
25%
Age
25%
Readmission Rate
Readmission Rate by Facility
35%
80%
DRG 144 – Other Circulatory
System Diagnosis with CC
60%
40%
20%
0%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Length of Stay
0
1
2
3
4
5
6
Number of Claims in Past 90 days
≥7
Readmission Rate
48%
Advanced Data Analytics
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Hindsight
• Inter-hospital Readmission Rates
• Intra-hospital Readmission Rates
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Inter-hospital Readmission Rates
Hindsight
All hospitals can be compared with all Disease Classes
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Inter-Hospital Readmission Rates
Hindsight
Hospital ID 5 and 12 can be compared across time
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Inter-Hospital Readmission Rates
Hindsight
Hospital ID 5 and 12 can be compared across time for Circulatory diseases
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Insight
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Readmission Rates Analysis
Insight
Insight can be gained by looking into factors for readmission
We show Age, CMG/DRG, Length of Stay, and Prescription History
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Light blue bars have too few cases
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Readmission Rates Analysis
Insight
Filters can be applied to specific Hospitals. Showing Hospital IDs #5 and #12
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Foresight
• Case 1 – Heart failure
• Case 2 – Complications from prior
treatment
• Case 3 – Psychosis
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Case 1 – Heart Failure
Foresight
83% propensity for
readmission within
180 days
Solution shows the
reasons for readmission
and their relative effect
Solution shows the
history for this patient
Suggested intervention – Patient should be coached about their condition and management of their disease. Their family
members should also be coached on how to take care of the patient. Active care management may be considered.
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Case 2 – Complications from prior treatment
Foresight
Suggested intervention – Patient is at high risk of readmission due to complexity of illness. We suggest
enrolling the patient into a care management program before discharge.
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Case 3 – Psychosis
Foresight
Suggested intervention – Patient has liver disease and electrolytic imbalance complicating psychosis.
Given the young age of the patient, a care coordination program should be considered along with
coaching the patient’s parents on specific care strategies.
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Advanced Data Analytics combined with Care
Management has improved the American health system
•
Similar to the Canadian health system, in the United States, 10% of patients account
for 70% of total health care expenditures*
•
Medicare beneficiaries with 5 or more chronic conditions accounted for 76% of all
Medicare expenditures
•
Care management is a healthcare innovation that can reduce costs while enhancing
quality for patients with complex health care needs
Examples
Care Management Plus
(Oregon Health & Science
University)
Guided Care (Johns
Hopkins University)
•
•
Reduced patient odds
of hospital admission
by 24-40%
Reduced the number of
hospital days by 24%
and insurers’ net costs
by 11%
Frequent Users of
Health Services
Initiatives (The California
Endowment and California
HealthCare Foundation)
•
61% reduction in ED visits
and 62 % decrease in
inpatient days over two
years
* The New England Journal of Medicine
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Advanced Data Analytics has enabled much improved
patient transitions in BC and QC
Data analytics focused on identifying the frequent users of care has enabled more
efficient patient transition, thereby reducing costs to the health system
Nanaimo Regional General Hospital
(British Columbia)
•
To improve performance indicators
and CTAS time at the emergency
department
Saguenay-Lac-St-Jean (Quebec)
• Following analysis of high needs
patients in the Ste-Agathe region of
Quebec; care models that were
targeted to support the needs of the
top 200 healthcare users were
implemented
Advanced Data Analytics
• Enabled the identification of various
process bottlenecks, created fast
track processes for CTAS 4 and 5,
and improved overall patient flow
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• Over a 3-year period, ED visits have
been reduced from 760 to 212 visits;
inpatient days by an equivalent of 9.4
beds; and hospital admissions from
514 to 88
Copyright © 2012 Deloitte Development LLC. All rights reserved.
How to leverage Advance Data Analytics to improve your
healthcare organization
There is an Opportunity to Do More
Hindsight
Insight
Foresight
Advanced Data Analytics
• Broad historical
reporting
• Statistical analyses
(e.g. profiling and
segmentation)
• Advanced analysis
and modeling
• Understand
patient needs and
historical
behaviour
• Understand
patient profiles
and patient
segments (e.g.,
top 1% and top
5%)
• Predict the future
to prevent
unnecessary use
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Questions
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