A plenary lecture at the national IT for health conference in Norway September 2012 on Clinical research using Electronic Health Records

1. Clinical research based
on EHR systems –
Why is it so hard and what can be done about it ?
Gunnar O Klein
professor in Health Informatics
at NSEP – Norwegian Centre for EHR Research
Plenary presentation at HelseIT
in Trondheim 2012-09-20
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2. We had a workshop yesterday
• Together with some very interesting invited experts
we got an update on some recent projects that in
various ways provide insights into the future
possibilities for research using clinical data in EHR-
systems (Electronic Health Record) – or EPJ in Norwegian
• In this presentation I will attempt to give some highlights from
these presentations with the kind permission of the authors
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3. The panel
• Gerard Freriks, Netherlands, former GP and medical
scientist, past convenor of the CEN working group
that developed the EHR standard. Now working for
the EN13606 Association
• Arnulf Langhammer, Associate Professor, NTNU,
The Nord-Trøndelag health study (HUNT)
• Rong Chen MD, PhD, Sweden, Chief Medical
Informatics Officer, Cambio HealthCare Systems &
Karolinska Institutet, Stockholm
• Damon Berry, PhD, Dublin Institute of Technology,
Ireland
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4. Who is Gunnar Klein
• Professor of Health informatics at NTNU Jan 2012
• Have worked with ICT for health since 1975 in
different roles, often from Karolinska Institutet
• Chairman of European standardization of Health
Informatics in Europe 1997-2006 (CEN/TC 251)
• Leader and participant of a number of European R&D
projects, particularly in Information Security and for
communication of EHRs with semantic interoperabilty
• Physician, mainly in Primary care but 2009 at the
Karolinska University hospital
• Also a background as a Cancer researcher and in
Biotech industry in the 1980ies
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5. Why should we attempt to
use data from clinical records?
• There is so much we do not know in medicine
– and about health systems effectiveness and efficiency
• A lot has been found in the past using records, even
paper records – but very inefficiently
• With electronic records it should be much easier –
piece of cake
Or …
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6. Is the EHR data only
garbage?
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7. If we put garbage in a vault
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8. Datatilsynet
Protected as
gold
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9. Do we expect to get a treasure?
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10. Is the ocean empty?
Studies have shown that in routine use
a lot of things never become documented
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11. Is the ocean empty?
Or is it a gold mine?
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12. How can we turn EHRs
into gold mines ?
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13. There is so much we do not know
• Evaluations of health outcomes related to various
interventions, including medication
– On real life patient groups in large scale, at all locations
– With multiple diseases and treatments
– In all age groups
• Comparing biomedical laboratory data, genotypic and
phenotypic with outcomes and treatments - IRL
• Generate and test new hypotheses for basic
biomedical functions – compared with genetics –
Functional genomics
• Results for management of quality and planning of
health services. Eg. Do we follow guidelines?
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14. The requirements for EHR information and
some of the problems
in routine record information for research
Arnulf Langhammer
2012 09 19 AL EHR
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15. HUNT Research Centre, Levanger
Project leader of the Lung and Osteoporosis Study
Head of HUNT Databank
General practitioner
Høvdinggården Legekontor, Steinkjer
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16. The Nord-Trøndelag Health Study
HUNT
24 Municipalities
County of Nord-Trøndelag
Inhabitants: N=130,000
Trondheim
Age 20-100 yrs: n = 94,000
Age 13- 19 yrs: n = 10,000
Oslo
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17. EHR sources for HUNT
• Hospitals
– Levanger and Namsos
– St Olavs Hospital
• General practices
– All use electronic patient records
– Linked to Helsenett
– Most communication with hospitals electronically
– Electronic prescription handling
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18. Data from hospital records
Challenges were discovered during the
HUNT studies over a long period of time
– Change in ICD-codes
• ICD 9 replaced by ICD 10
– Validity of ICD codes
• Diagnostic uncertainty – code + ? (e.g. fracture maybe)
• Precision – Different according to level of speciality
– Change of diagnostic criteria :
• Myocardial infarction
• COPD
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19. The alternatives: Registries
• Special health registries on a national or local level
that has collected certain data for certain purposes.
The general registry of all causes of deaths and the
cancer registries are such examples but also the
more recent quality registries in relation to certain
diseases or procedures.
– Has generated a lot of useful information despite very limited in
information content
– Cumbersome to get data, often increased work for health
professionals and double registrations also in EHRs.
– A limited and predetermined set of questions that may be asked
even if a lot remains to be explored
• One question of today – How can we improve
collection of data from EHRs to these registries?
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20. The alternatives: Questionaires
• Questionaires to the persons included. This has often
been performed in conjunction with the collection of
the biological sample but may be repeated over the
years. More and more examples from various
countries are using web based surveys for easy data
collection. The method has several weaknesses in
addition to the ethical consequences related to
disturbing repeatedly possibly healthy persons with
intimate questions on their health. The answers are
subjective and may often lack the accuracy of a
professional assessment that may be needed to
achieve the desired results.
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21. The alternatives: Examniations
• Special clinical and laboratory examinations of the
study group for the sole purpose of obtaining
research data.
• This is the typical means of conducting clinical trials
e.g. for the approval of new medicines
– Very time consuming and expensive
– Interfering with the daily lives of the study population
• Will be necessary for a long time – But how do we
find the interesting patients if they have a particular
health problem ( excl. a general population study)
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22. Obstacles to EHR
based research
Scattered EHRs
The records over time of one
individual may be scattered in
several institutions:
- geographic location
- specialty
- legal entity c.f. the division
between primary care and
specialist health care, in Norway
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23. Obstacles to EHR
based research
Various formats and terminologies
The data of the EHRs exists in various
formats with regard to information
structure and terminology used.
- partly follows various EHR products
- Whereas the exchange of some
limited data in the form of electronic
messages has some good results,
essentially no attention has been
given to the task of long term
harmonization of EHR structure of
terminology in order to create a
better infrastructure for clinical
research
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24. Obstacles to EHR
based research
Lack of structure
Often there is very little structure in
the EHR systems of today.
Typewriters.
Many health care organisations and
thus systems have focused on the
perceived easiness for the physicians
to record data, with the use of free text
dictation as the solution, more and
more often combined with automatic
speech recognition software.
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25. Obstacles to EHR
based research
Privacy concerns
Concerns about protecting the
confidentiality of sensitive
personal information must also
be addressed. Ethical approval
and patient consent is
necessary. New systems may
facilitate the latter using
electronic means and the net.
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26. Obstacles are challenges
«Obstacles are those frightful things you see when you
take your eyes off the goal» (Henry Ford)
Sarah Louise Rung
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27. Gerard Freriks showed us impressive figures on
the business case for the pharmaceutical industry
When conducting clinical trials using EHR data
there are potential savings for one big company alone
2.000.000.000 EUR/year
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28. Reduce time needed for: Less attrition
• Study Design Less Site closure
• Site selection Less effort by investigator
• Site initiation Reduce time needed for:
Reduce time needed for: •Post processing
•Patient recruitment Better data quality
•Study execution Less data curation
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29. Pilot experiences were quite promising
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30. Overview of the EHR4CR
project
Electronic Health Record systems for Clinical Research
Selected presentation slides kindly provided by Mats
Sundgren (AstraZeneca, coordinator) and prof
Georges De Moor, univ Gent.
Gunnar O Klein
NTNU/NSEP (member of the advisory board)
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31. Project Objectives
• To promote the wide scale data re-use of EHRs to
accelerate regulated clinical trials, across Europe
• EHR4CR will produce:
– A requirements specification
• for EHR systems to support clinical research
• for integrating information across hospitals and countries
– The EHR4CR Technical Platform (tools and services)
– Pilots for validating the solutions
– The EHR4CR Business Model, for sustainability
RDLT meeting July 2012
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32. Project Facts
• The IMI EHR4CR project runs over 4 years (2011-2014) with a
budget of +16 million €
– 10 Pharmaceutical Companies (members of EFPIA)
– 22 Public Partners (Academia, Hospitals and SMEs)
– 5 Subcontractors
• The EHRCR project is to date- one of the largest public-private
partnerships aiming at providing adaptable, reusable and
scalable solutions (tools and services) for reusing data from
Electronic Health Record systems for Clinical Research.
• Electronic Health Record (EHR) data offer large opportunities for
the advancement of medical research, the improvement of
healthcare, and the enhancement of patient safety.
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33. Protocol Feasibility Pilot
• Pilot ready October-November 2012 with 11 Hospitals
RDLT meeting July 2012
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34. Vision
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35. Rong Chen, MD, Ph.D.
chief medical informatics officer at
Cambio Healthcare Systems and affiliated with
Karolinska Institutet, Stockholm, Sweden
EHR Data Reuse through
openEHR Archetypes
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36. Quality Registers Background
• About 80+ quality registers (QR) in Sweden
– National or regional ones
– Usually single condition based
• Common challenges/issues with QR data report
– (Aggregated) data sets do not exist in EHRs
– Unsynchronized data structures among QRs
– Mismatched terminology bindings
– Some QR are guideline based, some not
– Multiple integrations, multiple data entries
– Clinical decision support from QRs (?!)
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37. IFK2 – Pilot with the Swedish Heart Failure register
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38. IFK2 Results - Archetypes
• Total 21 archetypes
• 7 international archetypes
– openEHR-EHR-OBSERVATION.blood_pressure.v2
– openEHR-EHR-OBSERVATION.body_weight.v2
– openEHR-EHR-OBSERVATION.ecg_12_lead_standard_recording.v1
– openEHR-EHR-OBSERVATION.heart_rate.v2
– openEHR-EHR-OBSERVATION.height.v2
– openEHR-EHR-OBSERVATION.lab_test.v1
– openEHR-EHR-OBSERVATION.waist_hip.v2
• Expected generally reusable
– openEHR-EHR-OBSERVATION.eq_5d.v2
– openEHR-EHR-OBSERVATION.heart_failure_stage.v2
• Some expected to be reusable in QR reports
– openEHR-EHR-EVALUATION.review_of_conditions.v1
– openEHR-EHR-EVALUATION.review_of_procedures.v1
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39. Clinical Decision Support
openEHR Archetype
??? SNOMED CT
A L Rector PD Johnson S Tu C Wroe and J Rogers (2001) Interface of inference models with concept and
medical record models. in S Quaglini, P Barahona and S Andreassen (eds) Proc Artificial Intelligence in
Medicine Europe (AIME-2001 ) Springer:314-323
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40. Rong Chen showed a world premiere of the
new Guide Definition Language (GDL)
• A sub-language of dADL, driven by an object
model
• The object model consists of
– Header: Id, concept, language, description, translation
– Archetype binding
– Guide definition, pre-condition and list of rules
– Each rule has when and then expressions
– Term definition for language-dependent labels
Extensive reuse of existing openEHR specifications
Aiming to release through openEHR as open Source
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41. Clinical Decision Support Workbench
(GDL implementation)
2. Model new or find
• A tool to import, export existing clinical rules
and author clinical rules using evidence based
guidelines
• A rule engine to execute
the rules 1. Identify or monitor 3. Analyze EHR data in
the clinical problems CDS workbench
• Linked to COSMIC
(EHR) Intelligence for
verification, simulation
and compliance checking 5. Deploy Runtime 4. Confirm the clinical
CDSS inside COSMIC gaps and find areas for
• An extension of (EHR) improvements
Cambio COSMIC (EHR)
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42. Case Study: Antithrombotic Management in Atrial
Fibrillation
• 20% of strokes caused by atrial fibrillation
• Evidence-based European guideline on management of
atrial fibrillation, European Heart Journal (2010) 31, 2369–2429
doi:10.1093/eurheartj/ehq278
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43. Compliance Checking
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44. Compliance Checking Results
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45. Archetype Research in Ireland
(with a focus on records to support
biomedical research)
Damon Berry
Dublin Institute of Technology
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46. Example 1: Archetype-based
shared assessment tool
(Hussey 2010)
• Using archetype tools and services in the development
of a shared assessment tool between
– Community care nurses
– Public health nurse
– Community intervention team
– Respite care
– Primary care
– Acute care
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47. Example 2: Archetypes for CF
review records (Corrigan 2009)
• Cystic Fibrosis (CF) has high incidence in Ireland
• An assessment of how archetypes could be applied for
representation of CF record for multi-disciplinary teams
• Starting point, CF Registry of Ireland
• Develop archetypes, through to user interface to
experience development process.
• Feed back archetypes to openEHR org.
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48. Example 3: Archetypes for
wound care (Gallagher – 2012)
• MSc (HI) student who is an experienced tissue viability
nurse.
• Recognised wound care documentation issues in Irish
health system
• Studied doc. practices “on the ground”
• Researched best practice re documentation
• Incorporated ideas based on this study into draft archetype
and submitted to CKM.
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49. Conclusions
• Yes – We can turn EHR data into a goldmine for
Clinical Research
• To fully exploit the possibilities for secondary use of
data for research and quality management we need
structured data
– Using standardised structures EN ISO 13606/openEHR with
archetypes modelled by the clinical professionals and defined
terminologies (for international use SNOMED CT is preferable)
– This also gives new possibilities for decision support
– Very encouraging support from DIPS the major Norwegian EHR
supplier to hospitals
• It is possible to start building infrastructures for
clinical research using archetype methodology and
conversions of legacy data
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50. The road to better health goes through research and
structured EHR systems based on standards
Strukturert EPJ
Gunnar O Klein
professor i helseinformatikk
Presentation for Helse Midt-Norge, IKT- strategigruppa
13 september, 2012
A bridge to the future
It starts now!
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