Together Wersquoll Make

Breakthroughs

Herman Roelandts ndash Himss 2015

Uw data kapitaal levert nog steeds rente op

InterSystems at a Glance

1978 2014 0

100

200

300

400

500 Technology company

Indirect channel

87 of rev is from ww healthcare market

Epic Philips hellip

80 US HC providers use ISC

Hospitals networksSweden SA Nl WalesScotland Brazil Chile Saudi hellip

Healthcare Transformation Journey

Capture Share Understand Act

Weaving technology into care delivery

Uniting the care team

Driving efficiency

Focus

Wersquoll Make Breakthroughs

InterSystems Technology

Facilitate communication and coordination everywhere for everyone

Provide complete view of patients and populations at the point of action

Added value for HC

HealthShare

But hellip

HealthShare

Data gaps

Missing data elements (eg

outcomes)

RTCrsquos require details that may

not be routinely collected

Coding often only at first level

(eg ICD-9) therefore missing

granularity

80 of info stored as

unstructured data

Which market generated this list of challenges

Data quality

bull ldquoLongitudinalityrdquo

bull Coding for administrative

reasons (up-down

coding)

bull Coding often months

after patient encounter

bull Data provenance ndash who

entered the data

ldquoSemanticsrdquo

bull Many standards ndash many

versions

bull Complex care ndash many

HCPrsquos involved ndash many

hand-overs

bull Need to pool data cross

sites and cross different

countries

bull Pharma focused on CDISC

Privacy

bull Clearly a top priority

bull Different interpretations by

country by region-complex

bull Trust

Challenges with re-use

of patient level data

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

InterSystems at a Glance

1978 2014 0

100

200

300

400

500 Technology company

Indirect channel

87 of rev is from ww healthcare market

Epic Philips hellip

80 US HC providers use ISC

Hospitals networksSweden SA Nl WalesScotland Brazil Chile Saudi hellip

Healthcare Transformation Journey

Capture Share Understand Act

Weaving technology into care delivery

Uniting the care team

Driving efficiency

Focus

Wersquoll Make Breakthroughs

InterSystems Technology

Facilitate communication and coordination everywhere for everyone

Provide complete view of patients and populations at the point of action

Added value for HC

HealthShare

But hellip

HealthShare

Data gaps

Missing data elements (eg

outcomes)

RTCrsquos require details that may

not be routinely collected

Coding often only at first level

(eg ICD-9) therefore missing

granularity

80 of info stored as

unstructured data

Which market generated this list of challenges

Data quality

bull ldquoLongitudinalityrdquo

bull Coding for administrative

reasons (up-down

coding)

bull Coding often months

after patient encounter

bull Data provenance ndash who

entered the data

ldquoSemanticsrdquo

bull Many standards ndash many

versions

bull Complex care ndash many

HCPrsquos involved ndash many

hand-overs

bull Need to pool data cross

sites and cross different

countries

bull Pharma focused on CDISC

Privacy

bull Clearly a top priority

bull Different interpretations by

country by region-complex

bull Trust

Challenges with re-use

of patient level data

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Healthcare Transformation Journey

Capture Share Understand Act

Weaving technology into care delivery

Uniting the care team

Driving efficiency

Focus

Wersquoll Make Breakthroughs

InterSystems Technology

Facilitate communication and coordination everywhere for everyone

Provide complete view of patients and populations at the point of action

Added value for HC

HealthShare

But hellip

HealthShare

Data gaps

Missing data elements (eg

outcomes)

RTCrsquos require details that may

not be routinely collected

Coding often only at first level

(eg ICD-9) therefore missing

granularity

80 of info stored as

unstructured data

Which market generated this list of challenges

Data quality

bull ldquoLongitudinalityrdquo

bull Coding for administrative

reasons (up-down

coding)

bull Coding often months

after patient encounter

bull Data provenance ndash who

entered the data

ldquoSemanticsrdquo

bull Many standards ndash many

versions

bull Complex care ndash many

HCPrsquos involved ndash many

hand-overs

bull Need to pool data cross

sites and cross different

countries

bull Pharma focused on CDISC

Privacy

bull Clearly a top priority

bull Different interpretations by

country by region-complex

bull Trust

Challenges with re-use

of patient level data

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Wersquoll Make Breakthroughs

InterSystems Technology

Facilitate communication and coordination everywhere for everyone

Provide complete view of patients and populations at the point of action

Added value for HC

HealthShare

But hellip

HealthShare

Data gaps

Missing data elements (eg

outcomes)

RTCrsquos require details that may

not be routinely collected

Coding often only at first level

(eg ICD-9) therefore missing

granularity

80 of info stored as

unstructured data

Which market generated this list of challenges

Data quality

bull ldquoLongitudinalityrdquo

bull Coding for administrative

reasons (up-down

coding)

bull Coding often months

after patient encounter

bull Data provenance ndash who

entered the data

ldquoSemanticsrdquo

bull Many standards ndash many

versions

bull Complex care ndash many

HCPrsquos involved ndash many

hand-overs

bull Need to pool data cross

sites and cross different

countries

bull Pharma focused on CDISC

Privacy

bull Clearly a top priority

bull Different interpretations by

country by region-complex

bull Trust

Challenges with re-use

of patient level data

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Added value for HC

HealthShare

But hellip

HealthShare

Data gaps

Missing data elements (eg

outcomes)

RTCrsquos require details that may

not be routinely collected

Coding often only at first level

(eg ICD-9) therefore missing

granularity

80 of info stored as

unstructured data

Which market generated this list of challenges

Data quality

bull ldquoLongitudinalityrdquo

bull Coding for administrative

reasons (up-down

coding)

bull Coding often months

after patient encounter

bull Data provenance ndash who

entered the data

ldquoSemanticsrdquo

bull Many standards ndash many

versions

bull Complex care ndash many

HCPrsquos involved ndash many

hand-overs

bull Need to pool data cross

sites and cross different

countries

bull Pharma focused on CDISC

Privacy

bull Clearly a top priority

bull Different interpretations by

country by region-complex

bull Trust

Challenges with re-use

of patient level data

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

But hellip

HealthShare

Data gaps

Missing data elements (eg

outcomes)

RTCrsquos require details that may

not be routinely collected

Coding often only at first level

(eg ICD-9) therefore missing

granularity

80 of info stored as

unstructured data

Which market generated this list of challenges

Data quality

bull ldquoLongitudinalityrdquo

bull Coding for administrative

reasons (up-down

coding)

bull Coding often months

after patient encounter

bull Data provenance ndash who

entered the data

ldquoSemanticsrdquo

bull Many standards ndash many

versions

bull Complex care ndash many

HCPrsquos involved ndash many

hand-overs

bull Need to pool data cross

sites and cross different

countries

bull Pharma focused on CDISC

Privacy

bull Clearly a top priority

bull Different interpretations by

country by region-complex

bull Trust

Challenges with re-use

of patient level data

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Data gaps

Missing data elements (eg

outcomes)

RTCrsquos require details that may

not be routinely collected

Coding often only at first level

(eg ICD-9) therefore missing

granularity

80 of info stored as

unstructured data

Which market generated this list of challenges

Data quality

bull ldquoLongitudinalityrdquo

bull Coding for administrative

reasons (up-down

coding)

bull Coding often months

after patient encounter

bull Data provenance ndash who

entered the data

ldquoSemanticsrdquo

bull Many standards ndash many

versions

bull Complex care ndash many

HCPrsquos involved ndash many

hand-overs

bull Need to pool data cross

sites and cross different

countries

bull Pharma focused on CDISC

Privacy

bull Clearly a top priority

bull Different interpretations by

country by region-complex

bull Trust

Challenges with re-use

of patient level data

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Parallel industry-centric growth in ICT

The inefficiencies become obvious at the clinical trial interface

Physician

Investigator

57 of RampD

investment is

within Clinical

Development1

In some

countries nearly

90 of all

healthcare

records are

digital Patient health records Clinical trial

research data

Electronic data capture

of Clinical Trial data

Patient Care Data

Over 40 of

clinical trial

data are

entered into

health record

and EDC1

1 Integrating Electronic Health Records and Clinical Trials An Examination of Pragmatic Issues Michael Kahn University of Colorado

2 Slide originated by Custodix and used with their approval

Letrsquos find a market with the same needs so we can mirror our capabilities

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

RampD cost ever increasing RampD output ever decreasing

The Pharmaceutical Industry in figures Key data 2012 ndash efpia report

Strong incentives to make RampD more effective and efficient

857 million research $ are used for the Clinical trial phase

per new chemical or Biological entity

A typical clinical trial take approx 5 years what if we can

shorten this period by providing more complete and better

quality clinical data

What with the gain of bringing the medicine quicker

to the market

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

10

Interoperability EHRs generated by single institutions (the

doctor has a set of information for each

patient if the patient goes to another

doctor there is another set of information)

Separate and disparate systems

Incompatible EHR systems

Different models

Variable quality uniformity and

organisation of the data

Different coding and content standards

Structured (eg prescriptions) versus

unstructured (eg clinical narrative)

Different languages across Europe

Data security

privacy amp ethics

Complying with ethical legal

and privacy requirements that

differ from country to country is

critical to gain acceptance with

the general public patients and

medical professionals

Scalability and

sustainability

Solutions need to be adaptable

and reusable and governed within

a sustainable ecosystem

To address key challenges to enable the re-use of EHR for clinical research

Confidence in data

All data has to be complete and

accurate at the point of capture

A single error presents risk that

can be magnified as data

transmits downstream

Custodix slide

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Added value for HC

HealthShare

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Even EC is supporting the re-use of clinical data

EHR4CR mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2011-2015)

Budget of euro gt16m

For further information see

wwwehr4creu or contact

Geert Thienpont (EuroRec)

geertthienpointramitbe

Custodix slide

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

The EHR4CR platform

Local

Applica

tions

ET

L

Site

dependent

process (Virtual)

appliance

Modified Custodix slide

Semanti

c

interop

Securit

y

AuthN

AuthZ

Audit Workflow

Messaging Platform

Management

Terminology

Services

Mapping

Applicati

on

Services

Centrally deployed

(SaasPaas)

TTP

EDC

CD

W CTMS

eCRF

Clinical

trial

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

A 2ND unique initiative = a second real project EMIF

Mandated by IMI

One of the largest European publicprivate partnership projects in this area

4-year project (2014-2018)

Budget of euro gt16m

Difference with EHR4CR= data

Not only coming from EHRrsquos For further information see

wwwemifeu or contact

Bart VanNieuwenhuysse

(Janssen Pharma)

Custodix slide

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

This creates value for hospitals

Better patient care

Improved route to

inclusion in clinical

trials Enhances

treatment options

giving patients

access to trial drugs

and care pathways

with no cost to the

Trust

Improved clinical

research

Improved efficiencies

and interconnectivity

with other hospitals

facilitates

streamlines and

enriches clinical

research

Income stream

Better placed to

generate income

from clinical research

At a time of squeezed

budgets income from

research can help

drive innovation and

efficiency with better

outcomes for patients

Better quality EHR data

Improved monitoring

performance

benchmarking reporting

and management (eg

reimbursement coding)

Drives optimisation of

patient care and

improved efficiencies

Enhanced

reputation

Greater visibility of

hospitalclinicians in

scientific community

Improved ability to

participate inconduct

clinical trials

Custodix slide

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

And pharmaceutical companies Improved access to health record datahellip

PA

TIE

NT

S P

RO

TE

CT

ED

BY

LE

GA

L

AN

D P

RIV

AC

Y P

RO

TE

CT

ION

STA

ND

AR

DS

amp R

EG

UL

AT

ION

S

10

01

011

01

001010010

10010100101110

Patient

health

records

hellipwill speed up protocol design patient recruitment data capture amp safety reporting

0100

01

De-identified

data for Clinical

Research

What is the impact of protocol criteria on the size of the patient population for the trial

Which countries and sites offer the best chance of success

Where are patient candidates and who is the treating physician

What patient data can be pre-populated into the clinical trial records

What are the safety issues and have they been reported

Custodix slide

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

A win for all stakeholders is critical

Pharma

academia CROs Clinical trial

development will

become more efficient

by reducing the time it

takes to bring new

drugs to market thus

generating substantial

value

Hospitals Able to participate in

more clinical research

programmes

benefiting their

patients

Health

authorities Access to new and

better evidence to

underpin health

policy strategy and

resource planning

Health

community

governments Able to offer improved

quality of healthcare

with reduced

healthcare costs

EU More attractive for

RampD investment

Patients Faster access to safe

and effective

medicines improving

health outcomes

across Europe

Custodix slide

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Added value for HC

HealthShare

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Upwards of 80 ndash 95 of Healthcare Data Is Unstructured

bull Patient complaints

bull Notes observations

bull Theories opinions

bull Conclusions

Unstructured Data

bull Coded fields

bull Values

bull Lab results Structured

Data

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Traditional Knowledge Discovery Top Down

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

iKnow Approach Bottom up Concept Level

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

The iKnow approach

Smart Indexing (concepts and relations)

Tw o patients are suffering from congestive heart failure

relation detection concept detection

Tw o patients Are suffering from Congestive heart failure

Smart Index Concept

Are suffering from

Tw o patients

Relation

Concept Congestive heart failure

Tw o patients are suffering from congestive heart failure

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Examples of the iKnow Breakthrough in Action

Intelligent EHR Navigation

Cohort ID Driving Actions with Real Word Predictive Models

Breakthrough Population Screening

Finding Elusive Unknowns

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Intelligent EHR Navigation

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Intelligent EHR Navigation

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Intelligent EHR Navigation

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

iKnow Breakthroughs in Action Retur

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Identify Patients with a Certain Condition

structured unstructured

Clinical Protocol

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Acta Oncol 2012 Sep 5

Metformin use and improved

response to therapy in esophageal

adenocarcinoma

Skinner HD McCurdy MR Echeverria AE Lin SH Welsh JW OReilly

MS Hofstetter WL Ajani JA Komaki R Cox JD Sandulache VC Myers

JN Guerrero TM

Department of Radiation Oncology The University of Texas MD

Anderson Cancer Center Houston Texas USA

32

Identify Patients With A Certain Condition

Identify Patients With A Certain Condition

Head amp Neck Cancer Metformin Diabetic

Identify Patients With A Certain Condition

Metformin Diabetic Head amp Neck Cancer

0 In metadata Forms in notes

Partially in metadata Forms in notes

All in metadata

Identify Patients With A Certain Condition

Identify Patients With A Certain Condition

iKnow Breakthroughs in Action

Parnassia Mental Health

Chaotic claimend conflict

manisch psychotisch toestandsbeeld

motorisch onrustig oninvoelbare

indruk psychotische indruk

dysfoor discussie eisend dwingend

verzet manie agressief

Parnassia Mental Health

Chaotic claimend

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Identify Patients With A Certain Condition

Metformin Diabetic Head amp Neck Cancer

0 In metadata Forms in notes

Partially in metadata Forms in notes

All in metadata

Identify Patients With A Certain Condition

Identify Patients With A Certain Condition

iKnow Breakthroughs in Action

Parnassia Mental Health

Chaotic claimend conflict

manisch psychotisch toestandsbeeld

motorisch onrustig oninvoelbare

indruk psychotische indruk

dysfoor discussie eisend dwingend

verzet manie agressief

Parnassia Mental Health

Chaotic claimend

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Identify Patients With A Certain Condition

Identify Patients With A Certain Condition

iKnow Breakthroughs in Action

Parnassia Mental Health

Chaotic claimend conflict

manisch psychotisch toestandsbeeld

motorisch onrustig oninvoelbare

indruk psychotische indruk

dysfoor discussie eisend dwingend

verzet manie agressief

Parnassia Mental Health

Chaotic claimend

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Identify Patients With A Certain Condition

iKnow Breakthroughs in Action

Parnassia Mental Health

Chaotic claimend conflict

manisch psychotisch toestandsbeeld

motorisch onrustig oninvoelbare

indruk psychotische indruk

dysfoor discussie eisend dwingend

verzet manie agressief

Parnassia Mental Health

Chaotic claimend

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

iKnow Breakthroughs in Action

Parnassia Mental Health

Chaotic claimend conflict

manisch psychotisch toestandsbeeld

motorisch onrustig oninvoelbare

indruk psychotische indruk

dysfoor discussie eisend dwingend

verzet manie agressief

Parnassia Mental Health

Chaotic claimend

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Parnassia Mental Health

Chaotic claimend conflict

manisch psychotisch toestandsbeeld

motorisch onrustig oninvoelbare

indruk psychotische indruk

dysfoor discussie eisend dwingend

verzet manie agressief

Parnassia Mental Health

Chaotic claimend

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Chaotic claimend conflict

manisch psychotisch toestandsbeeld

motorisch onrustig oninvoelbare

indruk psychotische indruk

dysfoor discussie eisend dwingend

verzet manie agressief

Parnassia Mental Health

Chaotic claimend

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Parnassia Time Before Seclusion

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

iKnow Breakthroughs in Action

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Pharma Company

Hepatitis C is a serious global health

concern due to it high prevalence

rate and low rate of diagnosis bull This pharma organisation developed a drug to treat HepC

patients and was looking for a way to identify patients at risk to

develop HepC

bull Most of the risk factors (like prison stay tatoos ) are only

available in clinical notes

bull By teaming up with InterSystems a screening platform was

developed that used an evidence based algorithm to score

patients on HepC high risk factors

bull This results in reports for clinicians where they can act based on

the scores and evidence found

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Injecting Drug User (IDU)

HIV

Country of origin HCV prevalence gt 2

High ethnic mix area

LFT ALT more the

Transfusion before 1992

Piercing

Acupuncture

Tattoo

Men having sex with men (MSM)

Household amp sex partners of Hep carriers

Prison stay

Identifying Hepatitis C ldquoAt Riskrdquo Patients

HCV Risk Group Drivers

bull Alert and outcome ldquoscorerdquo

bull Clinical support

ndash Guidance on additional questions

ndash Testing recommendation

bull Links to knowledge base

Positive Risk Alert

Algorithm Criteria Patient Database Risk Indicator Flag Hep C Test

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

iKnow Breakthroughs in Action

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

UZ Brussel

iKnow i2b2 Lucene

SQL access layer

InHouse Application

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

UZ Antwerpen (POC)

SetBuilder

iKnow i2b2

Data Access

REST

Pastel

Text Structured

Data

Together Wersquoll Make

Breakthroughs

HermanRoelandtsIntersystemscom