never mind the data, show me the outcome
DESCRIPTION
Presented by Dr Penelope Jane O'Hara, Managing Director, Accenture Analytics, Accenture at ISS Seminar: Outcome-based Analytics on 23 August 2013TRANSCRIPT
Copyright © 2013 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Copyright © 2013 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
Insight Driven Health
Never mind the data – show me the outcomes Dr Penny O’Hara August 2013
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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Impr
oved
pat
ient
out
com
es
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
PMPSuppliers
Public &Private Payers
Providers
Supply Chain DataIndustry Intelligence DataBenchmarking DataMarket Research Data
Treatment & Rx Claims & Payment DataClinical Outcomes DataLeading Practices DataProgram Effectiveness DataPopulation/ Disease Data
Drug Safety DataDrug Efficacy DataMedical Device EfficacyClinical Trial DataLeading Practices DataMarket Research Data
Prescription DataLab DataRadiological DataProduct Utilization Data Treatment Protocol Data
Admissions DataPhysician Profile DataBenchmarking DataEBM DataClinical Research Data
Epidemiological DataPatient Profile DataMarket Research DataGenomics DataClinical Trial DataOther 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, ...)
Data
clea
nsin
g &
Agg
rega
tion
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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14 New Documents sent to the PCEHR since the Shared Health Summary
Medication Summary
08-Nov-2008 Consumer Entered 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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