Translational & Interoperable Health Infostructure - The Servant of Three Masters

© Amnon Shabo (Shvo)

Translational & Interoperable Health Infostructure -

The Servant of Three Masters

Amnon Shabo (Shvo), PhD

Chair, EFMI Translational Health Informatics Work Group

Chair, IMIA Health Record Banking Work Group

Co-chair, HL7 Clinical Genomics Work Group

Research Fellow, University of Haifa

Towards a universal health information language

Revolutionizing healthcare through independent lifetime health records


“The Patient-centric translational health record”

This lecture is partially based on my recent publication:

2


Agenda

Translational Medicine and informatics

Universal Exchange Language?

Translational Health Information Language!

Semantic Warehousing of Health Information

The vision - Independent Health Record Banks

3


Translational Medicine Basic Concepts

T = Translational Barrier

Each T is tough but when Tn succeeds and Tn+1 fails… it‟s frustrating!

4

Source: Sarkar, IN. Biomedical informatics and translational medicine. Journal of Translational Medicine 2010, 8:22.


Translational Health Informatics

Translational Medicine involves data-driven approaches

CBR; machine learning; simulation, etc.

Analyze observational data found in operational health information

systems and use them for both -

discovery of new insights and suggesting hypotheses to be

checked in controlled trials

refinement of established evidence and clinical guidelines

Translational Research is about translating results of studies

in all relevant disciplines:

Biology

Analytics

Technology (IT, systems, equipment, modalities, devices, etc.)

Socio-economic, bio-ethical and medico-legal considerations

5

Common Informatics

infrastructure (language)

is needed across

disciplines !


Informaticians as Translators

6

Enable a

feedback loop

Source: Sarkar, IN. Biomedical informatics and translational medicine. Journal of Translational Medicine 2010, 8:22.


Agenda




Semantic warehousing of health information


7


USA President‟s Report on How Best to Use HIT

Key recommendations from the US PCAST Report:

“The initial approach to meaningful use* has focused on

driving physicians to adopt EHR systems that perform

important quality-improving functions within the practice

and, to a lesser extent, on developing capabilities for

broader sharing”

“Creation and dissemination of a

universal exchange language for healthcare information”

“An infrastructure for locating patient records”

“Rigorously protecting privacy and security”

* US Federal Meaningful Use of HIT – Incentives criteria set in the US for reimbursement

8


Flat representations are flat tires!

Health data semantics and context

cannot be faithfully represented

using flat structures (e.g., a list of

entries), rather, it requires a

compositional language that

associates data entries into a

meaningful statement


Universal Exchange Language? Start with Statements!

10

Code

Participant

Object

Code

Code

Insert into basic

health objects

Clinical Statement

Observation

Object

Medication

Object

Procedure

Object

Gra

mm

ar

Example: Observation O1

(consisting of Observations

O11 and O12 and related to

Subject S1), is the reason for

Procedure P1 (performed by

Clinician C1) which is the

cause of Observation O2… OthersDocsPharmaLab

SNOMED, LOINC, ICD, etc.

It’s available through the new generation of standards!


Examples of Data Sets in a Hypertension Study

Blood Pressure:

Systolic and Diastolic measurements are components

Mean is derived from the above components

Heart rate (HR) measurement is timed with

blood pressure (BP)

Anti-hypertension drug

Is taken with the indication of

Hypertension

Microalbuminuria

HR + BP were measured…

before and after taking a drug

Dose increased if Sys.BP>x

11

BP, HR -8

BP, HR -4

BP, HR 0

BP, HR +4

BP, HR +8

BP, HR +12

BP, HR +16

BP, HR +24

BP, HR +48Taken from different

columns

in the source data…

Losartan 50 mg/day (T=0)

Losartan 100 mg/day (T=+4)

Semantics is

often implicit!

So

urc

e: H

yp

erg

en

es

Co

ho

rts


Putting Detached Data into a Clinical Statement

12

Observation

Blood Pressure

SNOMED [BP code]R

ela

tion:

[time

d]

SubstanceAdministration

DrugTherapy

[Losartan intake details: dose, time, etc. ]

Entity / Role

ManufacturedMaterial

[Losartan ]

Relation:

[participation]

Observation

Heart Rate

LOINC [HR code]

Relation:

[timed]

Observation [Organizer]

Vital Signs

Rela

tion:

[co

mp

]

Rela

tion:

[co

mp

]


Clinical Genomics Statement

e.g., an OMIM Entry:

Despite the dramatic responses to EGFR inhibitors in

patients with non-small cell lung cancer, most patients

ultimately have a relapse. {12:Kobayashi et al. (2005)}

reported a patient with EGFR-mutant, Gefitinib-responsive,

advanced non-small cell lung cancer who had a relapse

after 2 years of complete remission during treatment with

Gefitinib. The DNA sequence of the EGFR gene in his

tumor biopsy specimen at relapse revealed the presence

of a second mutation ({131550.0006}). Structural modeling

and biochemical studies showed that this second mutation

led to the Gefitinib resistance.

13


Example: Clinical Genomic Statement

14

Observation

SequenceVariation

[EGFR Variant id

131550.0001]

Relation:

[cause]

Observation

ClinicalPhenotype

[responsive]

Rela

tion:

[su

bje

ct]

SubstanceAdministration

DrugTherapy

[Gefitinib intake details: dose, time, etc. ]

Entity / Role

ManufacturedMaterial

[Gefitinib ]

Relation:

[participation]

Observation

SequenceVariation

[EGFR Variant id

131550.0006]

Relation:

[cause]

Observation

ClinicalPhenotype

[resistant]

Relation:

[SAS]

OBSERVED

INTERPRETIVEGoal is to

provide it

ON TIME!!


Clinical Genomics Statement Model

15

Indications PhenotypesOmics

Observation

PerformersSpecimen

Genomic

Source

Cli

nic

al G

en

om

ic S

tate

me

nt Associated

Observationsencapsulation

Key omics

datareference Raw omics

data

ObservedInterpreted

* GTR is based on constraining the HL7 Clinical Document Architecture (CDA) base standard

* CDA: Clinical Document Architecture –

An HL7 standard describing generic structure of clinical documents with narrative along with structured data following a clinical statement model.

Specializes the HL7 Clinical Statement model

Aligned with HL7 Clinical Genomics specs

Subset is used by the Genetic Testing Report (GTR)*


Narrative Structured „Reconciliation‟

Health information language needs to

accommodate unstructured data (e.g.,

clinician's narrative or patient’s story),

while maintaining interlinks to

structured data entries corresponding

to contents that have been structured

16


HL7/ISO CDA (Clinical Document Architecture)

17

CDA

Human-to-Human

Machine-to-Machine

Printed

Bedside

…

EMR

Transcription

…

Medical Records

Transformation

…

Clinical Decision Support

Patient held-records alerts

…


CDA Overview

CDA – a generic specification

Could be used to representvarious types of documents:

Consultation note

Visit / progress note

Referral letter

Discharge summary

Operative note

…

A document type is alsocalled ‘template’ or‘implementation guide’

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Body

Header

Body

SectionNarrative

Clinical Statement

CDA

Entry

CDA

Entry

CDA

Entry…

CDA Document

code

code

code


CDA IG: Genetic Testing Report (GTR)

Define an implementation guide for a genetic testing

report that is human readable and machine-processable

Target at all types of GTR producers, e.g., genetic labs, clin.

geneticists

Readable content is larger in scope, e.g., detailed description of

the tests performed along with references

Machine-processable should be limited, e.g., exclude raw data

Ballot a Universal IG; then derive specific types of GTR:

Healthcare & Research

Realm-specific guides

Omic-specific guides

Developed using the MDHT* open source tool

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* MDHT - Model Driven Health Tool


GTR Overall Layout

20

Sections

order

constraint

Section

titles

constraint

Document

code

constraint


GTR Rendered – The Header

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Draft that has not been clinically validated


GTR Rendered – Summary Section

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Draft that has not been clinically validated


Genomic Observations Organizer

Clinical Genomic Statement - Overall Interpretation

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Overall

Interpretation

Performers

Wit

hin

th

e

Ove

rall

In

terp

reta

tio

n S

ec

tio

n

CG

S-O

I

CGS Reference

CGS Reference

CGS Reference

Test Details Section

CGS Test Details Section

CGS Test Details Section

CGS

GTR

Sp

ec

ific

ge

ne

tic

tes

tin

g's


GTR UML Model - Model Driven Health Tool

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Sections multiplicity:

Summary SHALL appear exactly once

TestDetails SHALL appear at least once

TestInformation MAY appear once


Key data out of raw/mass data sets

pertaining to an individual should

be encapsulated in its native

format into clinical data structures,

where 'bubbled-up' items could be

associated with phenotypic data

(using clinical data standards)

The Challenge of Raw and Mass Data

25


HL7 Clinical Genomics: Encapsulate & Bubble-up

26

Clinical PracticesGenomic Data

Sources

EHR

System

Bubble up the most clinically-significant raw

genomic data into specialized HL7 objects and

link them with clinical data from the patient EHR

Decision Support Applications

Knowledge(KBs, Ontologies, registries,

reference DBs, Papers, etc.)

the challenge…

Encapsulation by

predefined &

constrained

bioinformatics

schemas

Bubbling-up is

done continuously

by specialized CDS

applications

re-analysis


XML Fusion: Encapsulation of Raw Genomic Data

27

Raw

gen

om

ic d

ata

rep

resen

ted

in

Bio

info

rmati

cs m

ark

up

HL

7 v

3 X

ML

© Amnon Shabo (Shvo)28

EHR PHR

Genomics

Enable

Decision Support

e.g., risk analysis

algorithms

Family Health History – A Convergence Test Case


Proof of Concept

29

PHR:

HHS Surgeon General FHH tool Patient enters data

Data exported as HL7 Pedigree instance

CDS:

HughesRiskApps Patient data from Surgeon General tool is imported

Pedigree is constructed

Risk assessment algorithms run


Family Health History – HITSP Recommendations

Now

Recommended

also by MU


Agenda






31


ResearchMetadata:

ISA

Scientific Knowledge:

Nano-publication

Omics Data:iPOP

Bridge Standards:

(e.g., GTR, DIR, PHMR)

Towards a Translational Health Information Language

Key DataEncapsulatedor referenced

Decision Support:Health eDecisions

(HeD)

Compositional Syntax:HL7 Clinical Statement & CDA,

openEHR

Constraining Syntax:ADL (AML)UML+OCL

Clinical Data:SemanticHealthNet

KN

OW

LED

GE

Raw

& m

ass

or

rese

arch

DAT

APo

int

of

Car

e D

ATA

Image Data:DICOM

Device Data:Continua & IEEE

Medical Terminologies:UMLS, epSOS TAS & SemanticHealthNet

Profiling:IHE

openEHR

provenance

findings

reasoning

utilization

Bubble-up

reasoning


Nano-publication – structuring the „narrative articles‟ using a nanopublication format, which is the smallest

unit of publication: a single assertion, associating two concepts by means of a predicate in machine-readable

format with proper metadata on provenance and context (http://nanopub.org/wordpress/)

HeD = The US ONC S&I Framework Health eDecisions Initiative (HeD), developing CDS Services standards

and CDS content for use as “knowledge artifacts”

(http://wiki.siframework.org/Health+eDecisions+Homepage)

UMLS = Unified Medical Language System (http://www.nlm.nih.gov/research/umls/)

epSOS TAS = Terminology Access Service (http://www.epsos.eu/)

ISA = Investigation – Study – Assay (http://isa-tools.org/)

iPOP = Integrative Personal Omics Profile (http://www.ncbi.nlm.nih.gov/pubmed/22424236)

GTR = Genetic Testing Report, developed by HL7 Clinical Genomics

(http://www.hl7.org/dstucomments/showdetail.cfm?dstuid=95)

DIR = Diagnostic Imaging Report (http://www.hl7.org/implement/standards/product_brief.cfm?product_id=13)

PHMR = Personal Healthcare Monitoring Report

(http://www.hl7.org/implement/standards/product_brief.cfm?product_id=33)

SemanticHealthNet = EC FP7 project, harmonizing major standards in health

(http://www.semantichealthnet.eu/)

CDA = Clinical Document Architecture

(http://www.hl7.org/implement/standards/product_brief.cfm?product_id=7)

openEHR = Open source for EHR, based on CEN EN13606 spec for EHR (http://www.openehr.org/)

ADL = Archetype Definition Language (http://www.openehr.org/downloads/ADLworkbench/learning_about)

UML = Unified Modeling Language (http://www.uml.org/)

OCL = Object Constraining Language (http://www.omg.org/spec/OCL/)

IHE = Integrating the Healthcare Enterprise (http://www.ihe.net/)

* THIL Acronym Glossary

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http://nanopub.org/wordpress/

http://wiki.siframework.org/Health+eDecisions+Homepage

http://www.nlm.nih.gov/research/umls/

http://www.epsos.eu/

http://isa-tools.org/



http://www.ncbi.nlm.nih.gov/pubmed/22424236

http://www.ncbi.nlm.nih.gov/pubmed/22424236

http://www.hl7.org/dstucomments/showdetail.cfm?dstuid=95




http://www.hl7.org/implement/standards/product_brief.cfm?product_id=13








http://www.semantichealthnet.eu/





http://www.openehr.org/

http://www.openehr.org/downloads/ADLworkbench/learning_about

http://www.uml.org/

http://www.omg.org/spec/OCL/

http://www.ihe.net/


Nanopublication

“A nanopublication is the smallest unit of publishable information: an assertion about

anything that can be uniquely identified and attributed to its author. Nanopublications

support fine-grained attribution to authors and institutions, with the intention of

incentivising the reuse of knowledge. These assertions are organized using (a) the

domain semantics drawn from community ontologies and information models, and (b)

nanopublication representation model permitting provenance, annotation, attribution

and citation.” *

Nano & Micro-publication may

be important to machine learning

technologies as it surfaces up

the essence of a publication

while you could also crunch the

full text for possible nuances

* Source: Barend Mons et.al, The Open PHACTS RDF/Nanopublication Working Group V1.81 26-03-2012

34


The ISA Format for Assay‟s Metadata

ISA:

-Investigation

-Study

-Assay

ISA captures and

communicates the

complex metadata

required to interpret

experiments employing

combinations of

technologies, and

the associated data files

35

Source: Sansone et. al.

Toward interoperable

bioscience data.

Nature 2012.


HeD* CDS Guidance Service Use Case

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Source: USA ONC Standards & Interoperability Framework - Use Case Development and Functional Requirements for Interoperability

CDS Guidance Service; HeD = ONC Health eDecisions initiative


HeD Lifecycle of a CDS Knowledge Artifact

37

Source: USA ONC Standards & Interoperability Framework - CDS Knowledge Artifact Schema Implementation Guide, October 2012

Such flagging could help machine learning systems recognize

faster what knowledge artifacts become obsolete


The rise of the 'narciss-ome„…

Transformative paper in the Cell Journal

Reviewed in Nature

(http://www.nature.com/news/the-rise-of-the-narciss-

ome-1.10240)

iPOP – Integrative Personal Omics Profile Our personal omics change over time!

Longitudinal examinations of genome, proteome, metabolome,

autoantibodies, etc. of an individual (one of the authors)

Monitor healthy and disease states

Predict and act accordingly (the data predicted diabetes and diabetes

was diagnosed; life style changes make it manageable!)

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http://www.nature.com/news/the-rise-of-the-narciss-ome-1.10240
















Leverage a landmark

paper* that presented

the benefits of a variety

of omics „tests‟ done on

a healthy individual

Results were packaged

as an iPOP – Integrative

Personal Omics Profile

Should be

standardized and be

part of the

translational EHR !!

• iPOP paper can be found here:

https://register.mssm.edu/seminar/CLR9011/downloads/2013JUN20/8.15.13-dudley1.pdf

Packaging Omics „Testing‟ on Individual Level


















• Rethinking Domain Specific Standards• Wouldn‟t it be better if we strive to a universal health

information language?!

• Domain standards will then be just different usages of that

language

• Current situation is that various domain standards are

semantically inconsistent

• Are standards for exchange only?!• Natural languages are used for both communication and self

writing

• Similarly, standards could be used beyond exchange for

internal representation

• Differences between representations for exchange and

internal needs often make the exchange of data not fully

semantic interoperable

Rethinking Standards and Languages…


Agenda






41


The HyperGenes Project

Integrated demographic, clinical and environmental data with

genomic data on Essential Hypertension (EH) from well-

established historical cohorts in Europe (~25)

Genome-wide Association Study (1m SNPs for each subject;

~12,000 subjects)

Created disease models that incorporated the new findings

Created a common infostructure to both research and CDS!

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Translational effort!


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Translational Health Info-structure

43

Health Data Warehouse

DataStaging Repository

(Curation, normalization, de-identification,

standardization and integration)

KnowledgeAnalytics Hub

(rules, guidelines, functions, algorithms,

literature, evidences, insights, etc. )

Annotate

HEALTHCARE

iEHR*

Biomedical Analytics

En

rich

& E

xp

ort

BI AnalysisStudy Analysis

Data mining

New

kn

ow

led

ge

, p

refe

rab

ly s

tan

da

rdiz

ed

Pa

tien

t/su

bje

ct d

ata

, pre

fera

bly

sta

nd

ard

ize

d

CGS*-based

Clinical Data

Information Marts(e.g., transMart)

Standards-based

Data

Lo

ad

* iEHR Interoperable electronic health record

* C-CDA Consolidated CDA – common templates of clinical documents

* DtC Direct-to-Consumer testings and monitoring, etc.

* CGS „Clinical Genomics Statement’ standard representation

* HPO Human Phenotype Ontology

* THIL Translational Health Information Language

Images

(e.g., DICOM)

Templates

(e.g., C-CDA*)

Existing sources

of knowledge

Patient generated data

and DtC* output data

Transform

Encapsulate

reference

THIL*

Ontologies

(e.g., HPO*)

Mass & Raw DataCommon formats in optimized storage (cloud-enabled)

Omics

(e.g., VCF)

Sensors

(e.g., IEEE)

P

r

i

v

a

c

y

&

S

e

c

u

r

i

t

y


Warehouse Information Models

A BII warehouse can have multiple information

models, each dedicated to a specific solution/project

A warehouse information model is created based on

selection of generic standards such as HL7 CDA and-

Constraining the standards

Interrelating the standards

The Hypergenes Information model is based on HL7

CDA, Pedigree and Genetic Variation standards that

were (1) constrained and (2) interrelated in a way that

there is a single CDA & Pedigree per subject and

multiple Genetic Variations (see next slide)44


Hypergenes Warehouse Information Model

45

CDA TemplateHeader

subject id

….

Body

reference1 to GV

reference2 to GV

reference2 to PD

….

clinical &

environmental

observations

GV Templatesubject id

Genomic

Observations

Phenotypes

Raw Genomic Data

subject id

HapMap / BSML / MAGE

Relational schemas

optimized for persistency

Encapsulation or

referencing

Pedigree Template

On

e in

sta

nce

pe

r su

bje

ct

subject id

Genotype

Phenotype

Observed Interpretive

or

*Template is a set of constraints specific to a project/solution

Health Records Disease Model


BII Ontology of Essential Hypertension

46


ETL Processes into the BII

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Terminology

Servers

Data Warehouse

Normalization,

Standardization

& Validation

Build CDA Store

HL7 Persistors

ONTOLOGY

Cohort

Data

Harmonization Data Extraction


Rich Expressiveness vs. Interoperability

There‟s currently a tension between the two goals:

The more expressive it is -

the less interoperate it is

Expressive structures lead to optionality

Possible solution: Constraints

Archetypes with EHR 13606 (European/ISO Standard)

HL7 Templates (no formalism has been agreed upon)

OCL is examined

GELLO (OCL-based) for clinical decision support

Public registries of templates

Need dedicated IT to provide registry services!

In research settings

Granularity, specificity and heterogeneity of data is higher

The same constraining technologies allows for

capturing similarities while preserving disparities

Cohorts harmonization could be reassessed depending on analysis results,

creating new computed fields and aggregates

48


Hypergenes Data Standardization

49

OWL Ontology

Standard-based

Instances

(e.g., CDA)

Instance

Generation

Engine

Data Source

Mapping local Vocabularies

Template Model

Conform to the

Template Model

Representing constraints

Java API

Ad

ap

ter

CTS

Using MDHT (UML+OCL) to

represent and validate

constraints


Hypergenes BII Features for Mart Creation

50

User

Schema

RDF Store

RIM-based

XML Database

Mass Datae.g., Genomic; Images;

Sensor... Non-XML format

Promotion

SPARQL API

Data Mart

Relational

Data Mart

Semantic Web Tools &

Applications


Agenda






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Motivation and Passion…

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KNOWLEDGE

We don‟t know much

more than we know

Case-based

reasoning

The case is the

lifetime EHRHealth

Record Banking

DATA

Individual‟s Data

Fragmentation

Decision making

Is hard!

Humans

Machines

Case-based

(tacit) knowledgeTrial & error

Sustainability


From Medical Records to the EHR…

53

Medical

records timeco

nte

nt

From medicine to health…

Longitu-

dinal,

possibly

life long

Cross-institutional

Medical recordEvery authenticated

recording of medical

care (e.g., clinical

documents, patient

chart, lab results,

medical imaging,

personal genetics, etc.)

Health recordAny data items related to the

individual’s health (including

data such as genetic, self-

documentation, preferences,

occupational, environmental,

life style, nutrition, exercise,

risk assessment data,

physiologic and biochemical

parameter tracking, etc.)

Longitudinal (possibly lifetime) EHRA single computerized entity that continuously aggregates and summarizes the medical and health records of individuals throughout their lifetime

Should Also

include

genetic data


EHR – layers of temporal and summative data

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Temporal Data

Summative Info E H

R

Evidence

Sensitivities | Diagnoses | Medications | etc.

Medical records: charts, documents, lab results, imaging, etc.

Topical

summary

Non-

redundant

lists

On

go

ing

extr

acti

on

an

d s

um

mari

zati

on

Personal genetic

variations

Genetic-based

disorders

Haifa Research Lab


New

Legislation

Operational

IT Systems

Provider

Medical

Records

Archive-

Independent

Health Records

BankOperational

IT Systems

Provider

Medical

Records

Archive-

Operational

IT Systems

Provider

Medical

Records

Archive-

Independent

Health Records

Bank

Standard-based

Communications

Operational

IT Systems

Provider

Standard-based

Communications

Operational

IT Systems

Provider

The Conceptual Transition

Current constellation New constellation

PatientIndividual


The End

Thanks for your attention!

Questions?

Comments: [email protected]

Towards a universal health information language

Revolutionizing healthcare through independent lifetime health records

Translational & Interoperable Health Infostructure - The Servant of Three Masters

Healthcare

Transcript of Translational & Interoperable Health Infostructure - The Servant of Three Masters