Presented by Dr Penelope Jane O'Hara, Managing Director, Accenture Analytics, Accenture at ISS Seminar: Outcome-based Analytics on 23 August 2013

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Insight Driven Health
Never mind the data –
show me the outcomes
Dr Penny O’Hara
August 2013

2. Copyright © 2013 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
The cost of healthcare is being driven globally by an
aging, less fit population, affected by chronic and more
complex disease states.
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Lifestyle
Diseases
• Osteoporosis
• Stroke
• Depresssion
• Obesity
• Chronic Kidney
Disease
• Atherosclerosis
• Chronic liver
disease
• Cirrhosis
• Type 2 diabetes
Aging
Populations
Increased Incidence
of Chronic Disease
Decreasing Fitness
of Populations
Increasing Patient
Complexity
Increasing
Costs

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Limited /Silo’ed Data & Analytics
Current Situation
Niche Analytic Models
Cost + Clinical Triggers
Mostly Claims Based Data
Short-Term Patient Focus
Complex Benefit Plans
Contentious Outcomes
Repeatable ROI
Proof
Economic
Incentive
Mismatch
Health Behavior
Change
Measurement
Traditionalism &
Inertia
Road Blocks Target State
EHR/Integrated Analytics
Integrated Impact & ROI Prediction
Behavioral Triggers
Integrated Claims & Clinical Data
Longitudinal Patient Focus (3–5 year)
Consumer Optimized with Incentives
Evidence Base Medicine (EBM/CER)
Analytic insights are needed help overcome inertia and
drive meaningful change
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Readmission Prediction, Safety Risk
Actuarial, Financial Forecasts
Genomics; Fraud & Abuse
Treatment Option Evaluation, Resource Utilization
Predictive Analytics
(the “so what”…and the
“now what”)
Future orientated and
source of competitive
advantage
Biosurveillance, Adverse Drug Warning
Disease Management, Daily staffing
Clinical Analysis, Top Performers
Patient Satisfaction, Quality Metrics
Descriptive Analytics
(the “what”)
“Rearview mirror” –
provides foundation and
insight
Sources: Davenport, Thomas H and Jeanne G. Harris, Competing on Analytics; The New Science of Winning, Harvard Business School Publishing Corporation 2007.
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The power of shifting healthcare analytics from
describing what happened to creating accurate
predictions will not only deliver new insights but improve
patient outcomes

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Improvedpatientoutcomes
Optimization
Predictive Modeling
Forecasting/
extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard Reports
“What’s the best that can happen?”
“What will happen next?”
“What if these trends continue?”
“Why is this happening?”
“What actions are needed?”
“What exactly is the problem?”
“How many, how often,
where?”
“What happened?”
Descriptive
Analytics
(the “what”)
Sophistication of Intelligence
Predictive
Analytics
(the “so what”)
…..and cover both descriptive as well as predictive
analytics to drive improved patient outcomes

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The data needed to empower robust health analytics is
distributed throughout the ecosystem
Patients
PMP
Suppliers
Public &
Private Payers
Providers
Supply Chain Data
Industry Intelligence Data
Benchmarking Data
Market Research Data
Treatment & Rx Claims & Payment Data
Clinical Outcomes Data
Leading Practices Data
Program Effectiveness Data
Population/ Disease Data
Drug Safety Data
Drug Efficacy Data
Medical Device
Efficacy
Clinical Trial Data
Leading Practices Data
Market Research Data
Prescription Data
Lab Data
Radiological Data
Product Utilization Data
Treatment Protocol Data
Admissions Data
Physician Profile Data
Benchmarking Data
EBM Data
Clinical Research Data
Epidemiological Data
Patient Profile Data
Market Research Data
Genomics Data
Clinical Trial Data
Other basic research
Optimize
Revenue
Control
Cost
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Statistical models trained on a broad set of profile
parameters for each individual allow us to derive per-
patient risk scores
Very high
High –
Medium
0
20
80
100
Claims & Clinical
data history
Patient profiles Predictive
Models Model-specific risk-score
Low
Member Data
Inpatient/Hospital
data
Outpatient/GP Data
Data on care
procedures
Rx/Pharma data
Others (e.g. rehab,
...)
Datacleansing&Aggregation
250-1000 predictive
parameters form per-patient
profile
Hospital
admission
risk (LOH)
Financial
risk
Disease
specific
risks:
Diabetes,
CHF/CAD,
COPD/
Asthma,
etc.
 Socio-demographic
 Chronic conditions &
ICD-groups
 Medication
 Inpatient/Outpatient
visits
 Costs
 Med. Procedures
 Coaching Feedback
Identified
target
population for
intervention
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In La Fe, Valencia, 17% of patients drive around 60% of the total
health expenditure. A Health Management system reduced hospital
contacts considerably
Proactive patient care: In La Fe, Spain, a Health
Management program helped to reduce the strain on health
system from patients with chronic diseases
Case Management
High complexity patients
Case Management /
Disease Management
High risk patients
Selt-treatment
Patients with moderate risk
Healthy Living &
Prevention
Patients with low risk
7% Hospital stays (days)
8% Hospital Admissions
33% Visits to Emergency
7% Hospital stays (days)
24% Hospital Admissions
27% Visits to Emergency
64% Hospital stays (days)
53% Hospital Admissions
16% Visits to Emergency
22.5% Hospital stays (days)
15% Hospital Admissions
24% Visits to Emergency
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La Fe
Activity Results

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A large proportion of the increased expenditure is in the
inpatient setting
Source: Basque Country Health Department 2009
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1.0%
3.5%
31.4%
23.0%
13,6%
27,4%
35%
25%
15%
12%
8%
5%
23.383
80.186
720.644
527.441
311.862
628.303
6,3%
12,4%
27%
36%
35%
2,5%6,7%7,6%
4%11,7%11,4%
20,6% 17,7% 4% 30,7%
19% 20,6% 7% 17,5%
24% 27,5% 11% 2,6%

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Something had to change – we developed an approach
to identify patients AND to operationally improve
outcomes
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We predicted which patients were approaching their
‘Intense Years’ of health resource consumption
(*) Source: Roger Halliday, Department of Health for England
Number of Hospital Days
Before Integrated Case Management
After Integrated Case Management
-4
Years
-3
Years
-2
Years
-1
Year
Intense
Year
+1
Year
+2
Years
+3
Years
+4
Years
+5
Years
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The Case Management Model is a capability that
coordinates health services provisioning and helps
patients navigate the system
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Australia: Convergence of PCEHR & mHealth
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The National eHealth Record System and Mobile Gateway (e.g.
consumer-entered medications via an NPS mobile application)
eHealth Record Functions
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Copyright © 2011 Accenture All Rights Reserved.
14 New Documents sent to the PCEHR since the Shared Health Summary
Medication Summary
08-Nov-2008ConsumerEntered Medication
Case Study – National Prescribing
Service:
• Consumer based application to
track medicines (brand, strength
,dosage)
• Keep photos of medicines,
packaging and dispense labels
• Track schedule of medicines
taken /not taken
• Set medicine alerts and
reminders
• Track changes to medicines using
a change log
Value Proposition:
• Identification &
authentication
• Ability to view and upload
medicines directly to their
eHealth record using a
mobile device.
• A more complete picture
of current medications–
hence better care.

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Whole Community Health:
The North West London Integrated Care Model
Local Multi-Disciplinary Groups… …working in a Multi-Disciplinary System
Patient
registry
Risk
stratification
Clinical
protocols &
care packages
Case
conference
Performance
review



Care plans
Care
delivery
Improve the quality of patient
care for patients with diabetes and the elderly
Group
Mental
Health
Specialist
Sub-Group
Social care
Specialist
Acute
Specialist
Aligned Incentives through an innovative financial model
Information sharing to access and analyse data in a timely fashion
Joint Governance through IMB with a shared performance and evaluation framework
Organisation and culture development
Patient, user and carer engagement and involvement
Community
matron
Practice
Social
care
worker
District
nurse
Community
Mental
Health
Practice
nurse
GP
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Source: :NHS North West London ICP business-case-amended-final

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Thank You