Clinical research based on EHR systems

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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

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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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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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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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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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Is the EHR data only

garbage?

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Datatilsynet

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Is the ocean empty?

Studies have shown that in routine use a lot of things never become documented

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Is the ocean empty?

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How can we turn EHRs into gold mines ?

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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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The requirements for EHR information and some of the problems

in routine record information for research

Arnulf Langhammer

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15 AL 05

General practitioner

Høvdinggården Legekontor, Steinkjer

HUNT Research Centre, Levanger

Project leader of the Lung and Osteoporosis Study Head of HUNT Databank

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Oslo

Trondheim

The Nord-Trøndelag Health Study

HUNT

County of Nord-Trøndelag 24 Municipalities

Inhabitants: N=130,000

Age 20-100 yrs: n = 94,000 Age 13- 19 yrs: n = 10,000

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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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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

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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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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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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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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

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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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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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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

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Obstacles are challenges

«Obstacles are those frightful things you see when you take your eyes off the goal» (Henry Ford)

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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

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Reduce time needed for:

• Study Design • Site selection • Site initiation

Reduce time needed for:

•Patient recruitment •Study execution

Less attrition

Less Site closure

Less effort by investigator Reduce time needed for:

•Post processing

Better data quality Less data curation

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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

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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

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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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Protocol Feasibility Pilot

• Pilot ready October-November 2012 with 11 Hospitals

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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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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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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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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

openEHR Archetype

SNOMED CT ???

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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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Clinical Decision Support Workbench

(GDL implementation)

• A tool to import, export and author clinical rules • A rule engine to execute

the rules

• Linked to COSMIC (EHR) Intelligence for verification, simulation and compliance checking • An extension of

Cambio COSMIC (EHR)

2. Model new or find

existing clinical rules using evidence based

guidelines

3. Analyze EHR data in CDS workbench

4. Confirm the clinical gaps and find areas for

improvements 5. Deploy Runtime

CDSS inside COSMIC (EHR)

1. Identify or monitor the clinical problems

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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

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Archetype Research in Ireland

(with a focus on records to support

biomedical research)

Damon Berry

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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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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.

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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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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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Strukturert EPJ

Gunnar O Klein

professor i helseinformatikk

Presentation for Helse Midt-Norge, IKT- strategigruppa 13 september, 2012

The road to better health goes through research and structured EHR systems based on standards

A bridge to the future It starts now!