The Learning Healthcare

System: a European

perspective

Brendan Delaney Wolfson Professor of General Practice, King’s

College London

Challenges of the EBP Paradigm

Clinical Research in crisis

• Hard to identify subjects

• Complex, costly CRFs with duplicate data entry

• RCTs not cost-effective

• Potential for evidence manipulation

Diagnostic error

• 60% of litigation claims against GPs

• Failure of Decision Support Systems for Diagnosis

2 2 April, 2014

The Learning Healthcare

System

3 2 April, 2014

SAFER CLINICAL

PRACTICE

MORE RESEARCH

EVIDENCE

KNOWLEDGE

TRANSLATION

RESEARCH

A natural progression of EBP

Past 2 decades ICT has taken centre stage in

healthcare

BIG DATA

Genomics and stratified medicine

Decision support

Massive increase in clinical practice guidelines

Prompts and alerts in health records

4 2 April, 2014

OK.... BUT....

Clinicians don’t code

And when they do, data stays in silos

Alert fatigue and unknown cognitive effects

DATA quality isn’t good enough for research

Big data Good data

Next Generation Sequencing plus Millions of patients

health record data = Peta bytes......

5 2 April, 2014

The informatics challenge

6 2 April, 2014

• Meaning of data is

lost in translation

between…

• Clinician and data

entry

• One system and

another (or simply

left in silos)

• Clinical use and

research use

• Research

knowledge and

translation

“What we say to dogs”…Larsen

Requirements of the Learning Healthcare

system

7 2 April, 2014

TRUST

EPRs

Genomics

Proteomics,

Metaobolomics data

Primary

Care

DW

CDW Primary

Care

EPRs

Environ

mental

data

Patient

portal

Semantic integration tools (including embedded case

report forms and prediction models), Workflow (alerts

triggers and reminders), provenance

Data linkage, cohort

discovery, provenance,

security.

The integrated clinical

laboratory

RESEARCH COHORTS

ACUTE AND TERTIARY

DATA

INTEGRATIVE INFORMATICS

SYSTEM

PRIMARY CARE DATA

Patient safety

and quality

improvement

Cohorts,

biomarkers,

Genotype-

phenotype

association

studies, RCTs

Problems with routine data quality Reimbursement bias

• Why record a BMI in a thin person?

Software bias

• System initiated – UK eHRs don’t allow

negative values and <>

Data errors

• 1% ‘resurrection’ rate in one UK longitudinal

study

• Myocardial infarction in code ‘NOT’ in text….

• Different pick lists for terminologies and the use

of non-standard representations e.g. BP!

Barriers

Obtaining routine data from primary care eHRs is

possible (EU sentinel networks)

In most countries linkage via a trusted third party is

also happening

BUT this data is not ‘collected’ for research

purposes………

AND eHRs have mostly failed to provide

interoperability or indeed to support research

translation

TranSMART

11 2 April, 2014

FP7 TRANSFoRm Consortium

2 April, 2014 12

Aims of TRANSFoRm

To develop methods, models, services, validated architectures and demonstrations to support:

• Epidemiological research using GP records, including genotype-phenotype studies and other record linkages

• Research workflow embedded in the EHR

• Decision support for diagnosis

www.transformproject.eu

13 2 April, 2014

Use case 1: Type 2 Diabetes

Research Question: In type 2 diabetic patients, are selected single nucleotide polymorphisms (SNPs) associated with variations in drug response to oral antidiabetic drugs (Sulfonylurea)?

Design: Case-control study

Data: primary care databases (phenotype data) and genomic databases (genetic risk factors) – data federation

14

Use case 2: Gastro-oesophageal reflux disease (GORD)

Research Question: What gives the best symptom relief and improvement in QoL: continuous or on demand PPI use?

Design: Randomised Controlled Trial (RCT)

Data: Collection through eHR & web based questionnaire - eCRF

15

Translational Research and Patient Safety in Europe

Use case 3: Diagnostic Decision Support

• Alerting v prompting (assisting v

correcting) in chest pain, abdominal pain

and shortness of breath

–Clinical Prediction rule web service (with

underlying ontology)

–Prototype DSS integrated with InPS EHR system

6

Overall requirements use cases 1+2

Requirement Note Use case

Authorisation Explicit or general Cohort and case-control,

RCT

Consent Informed or explicit Cohort and case-control,

RCT

Linked phenotype Maintained and

refreshed

Cohort and case-control

Genetic data Browsing and selection Cohort and case-control

Recruitment Embedded real-time in

eHR, manages contact

and consent

Cohort and RCT

eCRF A functional tool rather

than an CTDMS

Cohort and RCT

Research subject portal Patient Related

Outcome Measures

Cohort and RCT

Genotype-phenotype

evaluations

Clinical Data Integration

Model: Ontology-based Upper ontology:

Basic Formal Ontology (BFO)

Middle (domain) ontologies:

OGMS (Ontology of General Medical Science)

IAO (Information Artefact Ontology)

VSO (Vital Signs Ontology) www.ifomis.org/bfo

Biodynamic Ontology: Applying BFO in the Biomedical Domain, D. M. Pisanelli

(ed.), Ontologies in Medicine, Amsterdam: IOS Press, 2004, 20–38

http://code.google.com/p/ogms

R. H. Scheuermann et al, Toward an Ontological Treatment of Disease and

Diagnosis, Proceedings of the 2009 AMIA Summit on Translational Bioinformatics,

San Francisco, CA, 2009. p 116-120

http://code.google.com/p/vital-sign-ontology/

Albert Goldfain et al, Vital Sign Ontology, Proceedings of the Workshop on Bio-

Ontologies, ISMB, Vienna, June 2011, 71-74

http://code.google.com/p/information-artifact-ontology/

2 April, 2014 19

20

Reference

Terminologies

and mappings

CDIM

CRIM

Workbench

Data node

connector

DS model

(DSM)

CDIM-DSM

mappings

Middleware

Provenance

and security

models

Study/Trial

db

used by used by

used by used by

used by

2 April, 2014

Thing

Entity

Continuant

Independent Dependent

Material

Chemical -> Form ->

Product

Molecular -> DNA -> SNP

Object -> Human -> Patient

Information

Content

Document -> Rx

Directive -> Act -> Rx item

Directive -> Condition -> Rule

Label -> Measurement unit -> Unit

label

Data item

Measurement

datum

Systolic measurement

Lab measurement

Pulse rate measurement

…

Clinical finding -> Phys

Exam

Clinical finding -> Lab

finding

Diagnosis

Prognosis

…

length -> human height

Mass -> dose

phenotype -> gender

pressure -> diastolic pressure

Quality

Clinical Data

Integration Model

(ontology)

2 April, 2014 21

GORD RCT

evaluations

Agent-based Technology for real

time recruitment

Autonomous

• provides configurable flexibility

• adaptive to user requirements

• non-intrusive behaviour

Asynchronous automation

• agents self-update their knowledge/registry

• configure for performance needs

Real-time recruitment and

notification

24 2 April, 2014

Central

Control

Service

Study

information

server

EHR

systems

CPRD

Agent

Pop

up

Data Standards……

Brendan Delaney 25

2 April, 2014

Data

Elements

ISO11179, 13606

IHE Profiles

CRPC, RPE, RFDC

Core Standards, CDISC, HL7, UMLS

CDISC Operational Data

Model

Standard for the description of metadata associated

with a clinical trial.

Allows exchange of datasets.

Allows vendor extensions.

Does not allow groups within groups on a form in its

unextended format.

ODM instance would be an xml document with bound

terminology and descriptors for text, value, value

range, code etc.

Translational Research and Patient Safety in Europe

Archetypes

A computable expression of a domain content model

in the form of structured constraint statements

based on a reference information model.

Often encapsulated together in Templates.

Sit between lower level knowledge resources and

production systems

Independent of interface and system

Archetypes and Forms

28 2 April, 2014

Diagnostic Learning

Healthcare System

29 2 April, 2014

60% of litigation against GPs and A+E is for failure to

diagnose

We don’t use Clinical Prediction Rules

Stand alone DSS is ineffective

How to integrate evidence with EHR?

• In a standardized way

• That integrates with clinical workflow

• That can be easily updated

• That helps generate new diagnostic evidence

3

Decision Support Tool

Clinical Evidence Service

Query Interface

Update Interface

Clinical Evidence Ontology

Data Mining Tools

Analysis

Research Repository

TRANSHIS Project

Literature

Family Practice EHR

Diagnostic Evidence Models

Data Mining Tools

Decision Support Tools

Decision

Support

Components

CDIM based Data Connector

General

model of evidence

4

Red Flag

Group

/ hasCue

/ hasRedFlagGroup

Data Mining TransHIS

ClinicianPatientEposide of care

Encounter 2

Diagnostic cuesRFEs 2 Diagnosis 2

Encounter n

Diagnostic cuesRFEs n Diagnosis n

tim

e

Encounter 1

Diagnostic cuesDiagnosis 1RFEs 1

Data Mining: Steps

Web tool (clinical evidences)

Web tool (RuleViewer)

KNIME tool

ImportXML

CSV

Encounter data

Encounter data

TransHIS

Calculate quality

measures

Derive association

rules

1 2

3

4

5

Clinical review

Filter based on high

quality rules

Evidence transfer to ontology

RFE -Dysuria

U01

RFE -Frequency

U02

Urinary Tract

Infection U71

Quantification -

Support x

Confidence y

Lift z

Demographic -

Netherlands

Female

Symptom -

Fever A03

hasDifferentialDiagnosis

hasDemographic

hasSymptom

hasRFE

Ontology Representation

isQuantificationOf

RFE -

Abdominal

Pain D06

36 Alice GREEN 16/06/1988 (F) (NHS No: 577 459 7164) 71 While Lion Walk, Leeds, Z99

9ZZ

New Consultation

New consultation for: Alice GREEN 16/06/1988 (F)

Reason for encounter / presenting complaint: Edit

Done

1969.00 Abdominal pain

<Comments>

Temperature Y N

Save

(N) *Fy0.. Sleep disorders

Doesn’t wake up at night, only to…

X

X

(Y) 19F..11 Diarrhea

Just once yesterday…

Signs, symptoms and examinations:

Diagnosis:

Possible

Diagnoses:

Appendicitis

Urinary tract infection

Bacterial enteritis

Pyelonephritis

Crohn’s disease

Ectopic Pregnancy

Irritable bowel syndrome

Ovarian cancer

37 Alice GREEN 16/06/1988 (F) (NHS No: 577 459 7164) 71 While Lion Walk, Leeds, Z99

9ZZ

New Consultation

Fitting it all together….

We need to:

Separate system components from knowledge

Use domain ontology to understand clinical

terminology and use it better

Have EHR systems that can use ontology for clinical,

research and knowledge translation purposes

Have secure and ‘fast’ middleware (plumbing)

Be better educated about informatics

38 2 April, 2014

CEN 13606: independence of

semantic representation.

eHR Interface Clinical

terminologies

Semantic representation

of clinical concepts

Database

Conclusion: Collaboration is essential

• In UK

• In Europe

• Internationally

• Public

• Private

• IT industry

• Health

• Pharma

• Biotech

• Patients

40 2 April, 2014

Acknowledgments

King’s College London: Natassa Spiridou, Fennie Liang, Simon Miles, Adel Taweel

Imperial College: Vasa Curcin

University of Rennes: Jean Francois Ethier, Anita Burgin-Parenthoine

University of Dundee: Mark McGilchrist

University of Birmingham: Theodoros Arvanitis, James Rossiter, Lei Zhao

RCSI, Dublin: Derek Corrigan

Karolinska Institute: Anna Nixon Andreasson, Lars Agreus

University of Antwerp: Paul van Royen, Hilde Bastiens, Johan Wens

NIVEL: Robert Verheij

CPRD: John Parkinson, Tjeerd van Staa

Trinity College Dublin: Siobhan Clarke

Brendan Delaney

41

2 April, 2014