Never mind the data, show me the outcome

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.

2

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

http://content.nejm.org/cgi/content/full/356/3/209�

http://www.nature.com/nature/journal/v450/n7169/full/450494a.html�


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

3


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.

4

The power of shifting healthcare analytics from describing what happened to creating accurate predictions will not only deliver new insights but improve patient outcomes


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


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

6


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

7


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



22.5% Hospital stays (days) 15% Hospital Admissions 24% Visits to Emergency

8

La Fe

Activity Results


A large proportion of the increased expenditure is in the inpatient setting

Source: Basque Country Health Department 2009

9

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%


Something had to change – we developed an approach to identify patients AND to operationally improve outcomes

10


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

11


The Case Management Model is a capability that coordinates health services provisioning and helps patients navigate the system

12


Australia: Convergence of PCEHR & mHealth

13

The National eHealth Record System and Mobile Gateway (e.g. consumer-entered medications via an NPS mobile application)

eHealth Record Functions

8Copyright © 2011 Accenture All Rights Reserved.

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.


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

1

2

3

4

5

6

7

Source: :NHS North West London ICP business-case-amended-final


Thank You

Technology

Never mind the data, show me the outcome