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Moving From Hindsight to Foresight – Unlocking the 1% Challenge
Young Lee – Deloitte National Health Services
May 29, 2013
Copyright © 2012 Deloitte Development LLC. All rights reserved.2
Faculty/Presenter Disclosure
Presenter: Young Lee
No Conflicts to Disclose
3 Copyright © 2012 Deloitte Development LLC. All rights reserved.
Objectives of Today’s Session
• Setting the Context
• Hindsight – Insight – Foresight
• Enabling Improvement Through Advanced Analytics
4 Copyright © 2012 Deloitte Development LLC. All rights reserved.
A portrait of our healthcare system
• 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*
“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
* Deb Matthews – Ontario Minister of Health and Long-Term Care
5 Copyright © 2012 Deloitte Development LLC. All rights reserved.
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
6 Copyright © 2012 Deloitte Development LLC. All rights reserved.
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:
Group 1 – Infrequent users
Group 2 – Frequent users (the Top 5%)
Group 3 – the Top 1%
1
2
3
– Proactively manage this group to prevent patients from becoming a frequent user of the ED;
– Disrupt the cycle and transition these patients out of this group towards Group 1
– Manage the Top 1% by understanding who they are, what their needs are, and how to meet their needs
1
23
Leveraging advanced data analytics to better manage patient profiles
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
7 Copyright © 2012 Deloitte Development LLC. All rights reserved.
Examining the Top 1%
An illustrative example of patient emergency department (ED) consumption:
1
2
3
• 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
Group 3
8 Copyright © 2012 Deloitte Development LLC. All rights reserved.
Hindsight Insight Foresight
How do we unlock the Top 1%?
Broad historical reporting on key performance indicators.
What happened?
Statistical analyses (e.g. profiling and segmentation) help
organizations understand historical performance.
Why did it happen?
Advanced analysis, machine learning and modeling predict
future performance.
What could happen?
0%
5%
10%
15%
20%
25%
30%
35%
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
Rea
dmis
sion
Rat
e
Week
Readmission Rate by Facility
Alpha
Bravo
Charlie
Delta
0%10%20%30%40%50%60%70%
Rea
dmis
sion
R
ate Rx History
0%
10%
20%
30%
40%
50%
60%
70%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Rea
dmis
sion
Rat
e
Length of Stay
Length of Stay
0%
5%
10%
15%
20%
25%
30%
1 4 7 10131619222528313437404346495255586164
Rea
dmis
sion
Rat
e
Age
Age
0% 10% 20% 30% 40% 50%
Heart Failure & Shock
Psychoses
Esophagitis and Gastroentritis
PTCA
Joint Replacement
Chest Pain
Back & Neck Proc
Spinal Fusion
Readmission Rate
DRG
0%
20%
40%
60%
80%
100%
0 1 2 3 4 5 6 7 8 9 10 11 ≥12
Rea
dmis
sion
Rat
e
Number of Claims in Past 90 days
Transport Claims
0%
20%
40%
60%
80%
100%
0 1 2 3 4 5 6 ≥ 7
Rea
dmis
sion
Rat
e
Number of Claims in Past 90 days
CHF
Patient ID: X12345
Age: 29 Sex: Male
Primary DX: 996.12(Mechanical Complication of vascular device / implant)
ReadmissionPropensity
84%
History: Anemia, CHF, Hypertension
Rx History: G.I. Drugs, Beta blockers, Diuretics, Antihypertensives, Nitrates, Anticoagulants, Hypnotics
180 day horizon
DRG 144 – Other Circulatory System Diagnosis with CC
Readmission Rate48%
Advanced Data Analytics
Copyright © 2012 Deloitte Development LLC. All rights reserved.9
• Inter-hospital Readmission Rates
• Intra-hospital Readmission Rates
Hindsight
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HindsightInter-hospital Readmission Rates
All hospitals can be compared with all Disease Classes
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HindsightInter-Hospital Readmission Rates
Hospital ID 5 and 12 can be compared across time
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HindsightInter-Hospital Readmission Rates
Hospital ID 5 and 12 can be compared across time for Circulatory diseases
Copyright © 2012 Deloitte Development LLC. All rights reserved.13
Insight
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InsightReadmission Rates Analysis
Insight can be gained by looking into factors for readmissionWe show Age, CMG/DRG, Length of Stay, and Prescription History
Light blue bars have too few cases
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InsightReadmission Rates Analysis
Filters can be applied to specific Hospitals. Showing Hospital IDs #5 and #12
Copyright © 2012 Deloitte Development LLC. All rights reserved.16
• Case 1 – Heart failure
• Case 2 – Complications from prior treatment
• Case 3 – Psychosis
Foresight
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ForesightCase 1 – Heart Failure
Solution shows the reasons for readmission and their relative effect
Solution shows the history for this patient
83% propensity for readmission within 180 days
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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ForesightCase 2 – Complications from prior treatment
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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ForesightCase 3 – Psychosis
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.
20 Copyright © 2012 Deloitte Development LLC. All rights reserved.
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
* The New England Journal of Medicine
• Reduced patient odds of hospital admission by 24-40%
Care Management Plus (Oregon Health & Science University)
Guided Care (Johns Hopkins University)
• 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
Examples
21 Copyright © 2012 Deloitte Development LLC. All rights reserved.
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
• 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
Nanaimo Regional General Hospital (British Columbia) Saguenay-Lac-St-Jean (Quebec)
• To improve performance indicators and CTAS time at the emergency department
• 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
• Enabled the identification of various process bottlenecks, created fast track processes for CTAS 4 and 5, and improved overall patient flow
Advanced Data Analytics
22 Copyright © 2012 Deloitte Development LLC. All rights reserved.
How to leverage Advance Data Analytics to improve your healthcare organization
Hindsight Insight Foresight
• Broad historical reporting
Advanced Data Analytics
• 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
There is an Opportunity to Do More
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?Questions