Staying Ahead of Your Competitors in Evidence Based World- Models for Success

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Staying Ahead of Your Competitors in Evidence Based World – Models for Success

Seng C Tan

Regional Director, HEOR and RWE

IMS Health Asia, Singapore

5th Aug 2015

1

• What is RWE

• Why RWE

• Data Supply Issues

• Approach Overview

• Examples of Datasets in Asia

IMS Health Asia - RWE Stay Ahead

Agenda

2

What is RWE?


3

Intelligence is scattered with considerable efforts being required to optimally integrate insights across functions


Separate, disparate activities

We need a system instead

• A foundation of real-world data (RWD) from ever expanding sources

• Data that can be used for multiple purposes, consistently across the globe

• Innovative technology and analytic advances that quickly generate new insights

• Optimal organizational performance by tapping into uniquely rich insights

HEOR,

Medical

Drug

Safety Brand,

commercial

teams

OR

studies Pricing

and

market

access

DUS

study

Data-

base

subs

Data-

base

subs

Ad

boards PMR

Pricing

research

Switch

and

repeat

PV

study

Epi

studies

Patient

journey

Patient

journey

Data-

base

subs

Registry

EC

OS

YS

TE

M B

AC

KG

RO

UN

D

4

Vision for the Future: The RWE Ecosystem


An environment for building deeper insights to benefit the entire enterprise

Trial

optimization

Claims LRx

data Hospitals

Social

media

EMR

Survey Enriched

datasets

ePRO

Registries

pRCTs

EMR=

eCRF

HEOR/

Safety

R&D Commercial

EC

OS

YS

TE

M B

AC

KG

RO

UN

D

5 IMS Health Asia - RWE Stay Ahead

Broader Definition of RWE

ISPOR*

“…data used for decision-making

that are not collected in conventional

randomized

controlled trials (RCTs)”

Connected Healthcare

Many Stakeholders Asking Same

Questions Around Efficacy &

Value

Simply stated, real world

evidence is the application of real

world data to derive insights that

can be generalized to usual

settings


Evidence based journey of each patient could be mapped

The patient journeys in real life could be identified, linked and investigated to

answer the research questions


Definition of Real World Evidence (RWE)

RWE uses patient-level data to better assess the value of treatments and

services based on actual health outcomes and the total cost of care

Electronic Medical Records (EMR)

Claims Databases,

Healthcare

Registries/PROs

Fast-cycle Datasets

Identify Unmet Patient Needs

Comparative Effectiveness Studies

Deep-patient Segmentation

Better Understanding of Disease Dynamics

Meet Payer Needs for Proof of

Relative Value

Improved Drug Safety &

Monitoring

Data

Analysis

Insight

Clinical Commercial

8

Why RWE?



The importance of RWE

‘RWD is becoming

crucial to decision

making when used in

conjunction with

clinical trials’


RWE has been widely used in Western countries for different decision-making purposes

Therapy Area Brand Notes

Oncology Tysabri Tysabri was initially withdrawn from market due to serious adverse events but

was then re-introduced under CED as real world studies contributed to

demonstrating that benefits outweigh risks.

USA

CV Crestor AZ prevented the generic reference pricing of Crestor with a series of real

world studies demonstrating that Crestor was able to get more patients to

their LDL goal compared to generic simvastatin.

Italy

Parkinson Levodopa TLV reimbursed Levodopa at a premium price and granted provisional

reimbursement, conditional on the collection of RWE.

Sweden

Asthma Xolair MoH negotiated reimbursement only for patients who show improvement with

Xolair. Novartis will rebate full cost of treatment for all other patients.

Netherlands

Diabetes Byetta Payers agreed to provide provisional market access on the basis that Lilly

would monitor Byetta’s real life use, collect epidemiological and safety data

for P&R.

Italy

Oncology Avastin Full or partial reimbursement for patients in which the Avastin and Taxol

combination exceeded a specific total dosage in a study designed to test

whether the combination of both medicines could extend patient survival in

mBC and mRCC.

Germany

Schizophrenia Risperdal The full price of Risperdal funds were held in escrow until Janssen provided

proof of lower hospitalization costs from a 12-month real world study.

France

BPH Finasteride Full cost is reimbursed if patients prescribed finasteride subsequently

required surgery for benign prostatic hyperplasia after one full year of medical

therapy.

Canada

LEMS Firdapse PCTs refused access to the first licensed treatment for LEMS because of real

world use of an unlicensed therapy.

UK


Creating patient centered evidence based value

RWE generated from ‘Big Data’ is the healthcare’s most powerful currency via

objective understanding and robust analyses on health outcomes, costs and

quality

Payer Patient Provider

Pharma


RWE could answer the questions of different stakeholders

Meet commitments

Add to the safety profile

Evaluate efficacy to

improve patient outcomes

Prove value

Secure reimbursement

Enhance understanding of

unmet patient needs

Explore new indications

Generate publications

Industry Regulators Payers Providers Patients

Drug

candidates Market

Access

Detect safety signals

Ensure long-term

effectiveness

Determine value and

coverage

Monitor usage within

criteria

Cost-effectiveness

Obtain locally relevant

evidence

Advance science

Improve care

Ensure continued

reimbursement

Generate publications

My own health- what

choices do I have?

What are the

risks/benefits?

Which treatment will

improve my quality of

life?

Which treatment is

safer, more convenient

and affordable?

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Data Supply Issues



Data Supply Issues: availability, completeness, quality and gaining trust of data owners are the main challenges in Asia

Data availability and completeness

• Only few providers have established RWD system

• Certain datasets lack the clinical richness required for certain evidence generation

• Variables such as costing for each resource use are not part of the database

Data confidentiality and security

• Variation in privacy laws and definition of patient identifiable information

• Lack of awareness on appropriate de-identification techniques

• Inadequate data governance standards

Trust

• Unwilling to allow or may improve access restrictions

• Data owners are not ready to build a trust with non-academic 3rd parties to enable optimal data use

Quality

• ‘Dirty Data’ – a mix of structured, semi-structure and unstructured information

• Large volume of free-text physician notes

• Data might be stored in multiple systems using different standards and formats even within the same care setting

Data Supply Issues

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Approach Overview



Real World Evidence Beyond Real World Data

REAL-WORLD DATA (RWD)

Mortality,

other registries

Hospital visits,

service details Test results,

lab values,

pathology results

Pharmacy

data

Electronic medical and

health records

Claims

databases

(government and

payer)

Social

media

Consumer

information

Pharmaceuticals data

Meaningful questions

Fit-for-purpose data &

analytics

Externally validated findings

REAL-WORLD DATA

REAL-WORLD EVIDENCE (RWE)

Real-World Evidence as a capability—data,

tools, processes, organization—underpinning several

functions to drive business intelligence


A number of key research questions could be answered in well designed and executed RWE project

Commercial should lead the direction of the research with expertise inputs from other key

stakeholders such as medical, HEOR and MA

RWE Q1: Epidemiology and characteristics of

Disease X population

1.1 Incidence and

prevalence

1.2 Demographics

1.3 Characteristics of

patient frequently re-

admitted for

condition Y

1.4 Standard of care

in Disease X and

impact on outcomes

RWE Q2: Treatment Patterns

and Compliance

2.1 Treatment

algorithm and switch

analysis

2.2 Compliance with

treatment guidelines

2.3 Patient

adherence and

persistence with

existing treatments

2.4 Dose escalation

and de-escalation

analysis

RWE Q4: Effectiveness and

Safety

4.1 Comparative

effectiveness of

different treatments

4.2 Mortality

outcomes by

treatments

4.3 AE comparisons

across different

treatments

4.4 Resource use,

LOS and costs

associated with

different treatments

4.5 Use of rescue

therapy

RWE Q3: Predictors of

Outcomes

3.1 Prognostic

factors for in-hospital

cases

3.2 Biomarker as a

predictor of

outcomes and

resource use

3.3 Relationship

between LOS;

compliance, re-

admission and

mortality

3.4 Adverse events

and other

complications

RWE Q5: Burden of Disease X

5.1 Cost of treating

Disease X

5.2 Life years lost

5.3 Patient reported

outcomes with

Disease X

5.4 Productivity loss

5.5 Burden to

caregivers


Multiple data sources will be linked into a Data Platform

A cross-disciplinary matrix team is always led by CoE RWE with local and external expertise

inputs to design and plan a RWE project


Illustration: Unique possibilities in oncology linked data


IMS Health has identified the potential retrospective datasets available in the region for RWE projects

A team in IMS Health conducted a comprehensive systematic literature review to identify

and investigate datasets used published retrospective studies across Asia Pacific region

• The number of publications varies different across the countries with Australia, Korea, China and Taiwan having the largest number of publications

• For example, in the case of Taiwan, more than 550 publications using longitudinal patient data retrospectively

• A large number of publications available shows that the healthcare data of the country in the APAC region are increasingly analyzed and potentially utilized in policy making and P&R decision

• In certain countries such as South Korea and

Taiwan, claims data has been widely used and analyzed leading to high number of publications

• Nearly 50 databases have been identified for further investigation of their potential use to generate real world evidence in the region

Publications found on Pubmed. Avg: 470 publications

Publications found after deduplication Avg: 404 publications

Included papers Avg: 183 publications

No. of databases Avg: 48 databases

Duplicated publications

Excluded publications

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Examples of Datasets in Asia (Applied in IMS Health Asia Projects)


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Using the gov’t medical insurance database, we could obtain inpatient costs to analyze various hospitalization costs


How the data is captured

Scope of data

Hospital

Patients Medical

Insurance Account*

Provincial BoHRSS

China Health Insurance

Research Association

(CHIRA)

Employer

Contributions

Out-of-pocket

Portion under coverage cap

Sampling and data-tracking

Description:

• In-patient data that cover the costs associated with diagnostics, surgery, hospitalization fees and drugs

• It also includes patient age, sex, geography and length of hospital stay

• It does not include out-patient data

Coverage:

• 147 hospitals are sampled, which are distributed across 20 provinces and 4 autonomous regions

Data mix:

• The data specifies both reimbursed and out-of-pocket payment amount

Funding

Illustrative

Sit

e o

f C

are

Fu

nd

ing

Sou

rce

1 2

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In CHIRA, the cost items, organized by ICD-10 diagnostic codes, for inpatient treatment in China


Key cost items for oncology…

A. Diagnosis

B. Inpatient

C. Prescription drugs

• X rays

• Clinical tests, including urine and blood tests, liver and renal function, FISH, IHC etc.

• Surgery

• Hospitalization

• Surgical materials

• Surgical drugs

• Chemo therapies

• Target therapy drugs

Cost area Cost items

…such cost items are organized by ICD-10 diagnostic codes

ICD-10 Oncology Type

Sample database

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Overview of MDV Database in Japan


Panel

hospitals

Data

Category

1. Approx 130 DPC hospitals

2. Annual 2.5 million net patients

3. Both in-patients and outpatients

4. DPC E and F file, Form #1 (discharge summary), claim, laboratory data

5. Since April 2008, monthly update

Data Items

Details Caveats

• No University hospitals in the panel

• Typically approx half of the panels to be filtered out due to longitudinal incompleteness or lack of outpatient data

• Outpatient data not available with some panels

• Lab data available with 10% of the panel

• Lab data is only available for biochemical tests, i.e. blood and urine test (blood pressure not available)

6. Patient: Annonymized patient ID, age, gender

7. Institution: Specialty

8. Drug: Brand, form and strength based on drug code, daily dosage and duration (only for oral drugs)

9. Treatment: Treatment including test/check based on treatment code

10. Diagnosis: DPC diagnosis code (more category than ICD-10), ICD-10, first diagnosed date, treatment month

11. Hospitalization: Hospitalization date, discharge date, discharge summary

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MDV Database capture in-patient level data of a total of 135 key providers in Japan


急性期病院（DPC参加病院） 135 施設

項目データ取り込み完了（外来データ使用可能）

病院数 135HP （122HP）

病床数 47,402床（42,543床）

平均病床数 351床（349床）

①0-14歳 13.6%

②15-64歳 53.1%

③65歳以上 33.3%

合計 642万人

年代別実患者総母数（2008年4月～2013年11月）

*がん拠点病院45病院含む（国指定29 都道府県指定16）

病床数病院数

199以下 25

200-499 85

500以上 25

合計 135HP

急性期医療機関（DPC病院）約9％をカバー

（データ提供開始時期は病院によって異なります）

29

8

6

18

31 13

5 25

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Public health insurance data is now open and shared by HIRA for research and analyses


• HIRA is a government agency and stands for Health Insurance Review and Assessment Service

• Aligns with the bigger agenda called ‘Government 3.0’ which aims to achieve a common goal by sharing government data where appropriate

− The two main objectives are ‘Providing customized services to individual citizens’ and ‘Creation of new national growth model through job creation’ through opening and sharing information

• All the government departments are to be evaluated by the degree of openness

• There are three data types

available from HIRA

− Treatment information data

− Patient sample data

• HIRA-NPS (National Patient Sample)

• HIRA-NIS (National Inpatient

Sample)

• HIRA-APS (Aged Patient Sample

with age≥65)

• HIRA-PPS (Child & Young people

Patient Sample with age<20)

− Pharmaceutical products distribution

information data (Sales data)

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Patient level real-world data provides reliable inputs for disease burden and economic analyses


Treatment information data

Before

▶Provided only for public

organizations and

academia

▶Treatment details,

Prescription details,

diagnosis info, etc

Now*

▶Open to public except for

sales force management

purpose

▶Data release time: now

▶Cost: minimum $200 ▶Delivery time: 1~3

months

Patient sample data

Before

▶Provided only for public

organizations and

academia

▶1.4 mil patients sample

data with treatment &

prescription details

Now*

▶Open to public

▶Data release time: now

▶Cost: about $300 ▶Delivery time: minimum

2 weeks

Pharmaceutical products

distribution information data

Current

▶Provided for academia

and pharmaceutical

companies

▶ Rx count, value,

counting units by

regions, ATC, bed size,

etc

Future*

▶Level of data openness is

to be determined

▶Data release time: TBD

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Patient level sample data is made available annually from NHIRD for research use


• Taiwan launched a single-payer

National Health Insurance Program on

March 1, 1995

• As of today, more than 23 million of

Taiwan’s 23.4 million population were

enrolled in this program

• National Health Insurance Research

Database (NHIRD) captured through

the electronic recording system set up

by the Bureau of National Health

Insurance, Taiwan (BNHI)

• The data derived from NHIRD has been increasingly used to provide real world evidence in drug

reimbursement listing as well as other policy decision-making

• Local economic evidence such as budget impact and cost-effectiveness analyses primarily rely

on the inputs reported and derived from the raw patient level NHRID data

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Different data files and datasets have been created over the years for various research goals


Data Files

Monthly claim summary for inpatient claims (DT)

Monthly claim summary for ambulatory care claims (CT)

Inpatient expenditures by admissions (DD)

Details of inpatient orders (DO)

Ambulatory care expenditures by visits (CD)

Details of ambulatory care orders (OO)

Expenditures for prescriptions dispensed at contracted pharmacies (GD)

Details of prescriptions dispensed at contracted pharmacies (GO)

Major Datasets

Longitudinal Health Insurance Database 2010 (LHID2010)

- 1,000,000 beneficiaries, randomly sampled from the year 2010 Registry for Beneficiaries (ID) of the NHIRD

- everyone who was a beneficiary of the National Health Insurance Program during any period in 2010 could be randomly sampled

Specific subject datasets

- Based on a survey of the research community, specific research subjects were selected

- Examples include Traditional Chinese medicine dataset (CM), Cancer dataset (CN), Diabetes dataset (DB) etc


Contacts Seng Chuen Tan Director, HEOR IMS Health Asia Pacific Email: [email protected]

mailto:[email protected]

