Clinical research based on EHR systems

Transcription

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

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 EHRsystems (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 2

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 3

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 (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 4

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 5

6 Is the EHR data only garbage? 6

7 If we put garbage in a vault 7

8 Datatilsynet Protected as gold 8

9 Do we expect to get a treasure? 9

10 Is the ocean empty? Studies have shown that in routine use a lot of things never become documented 10

11 Is the ocean empty? Or is it a gold mine? 11

12 How can we turn EHRs into gold mines? 12

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? 13

14 The requirements for EHR information and some of the problems in routine record information for research Arnulf Langhammer AL EHR 14

15 HUNT Research Centre, Levanger Project leader of the Lung and Osteoporosis Study Head of HUNT Databank General practitioner Høvdinggården Legekontor, Steinkjer 15

16 The Nord-Trøndelag Health Study HUNT Oslo County of Nord-Trøndelag Trondheim 24 Municipalities Inhabitants: N=130,000 Age yrs: n = 94,000 Age yrs: n = 10,000 16

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 17

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 18

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? 19

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. 20

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) 21

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 22

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 23

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. 24

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. 25

26 Obstacles are challenges «Obstacles are those frightful things you see when you take your eyes off the goal» (Henry Ford) Sarah Louise Rung 26

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 EUR/year 27

28 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 28

29 Pilot experiences were quite promising 29

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) 30

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

32 Project Facts The IMI EHR4CR project runs over 4 years ( ) 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. 32

33 Protocol Feasibility Pilot Pilot ready October-November 2012 with 11 Hospitals RDLT meeting July

34 Vision 34

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 35

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 (?!) 36

37 IFK2 Pilot with the Swedish Heart Failure register 37

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 38

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:

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 40

41 Clinical Decision Support Workbench (GDL implementation) A tool to import, export and author clinical rules 2. Model new or find existing clinical rules using evidence based guidelines A rule engine to execute the rules Linked to COSMIC (EHR) Intelligence for verification, simulation and compliance checking An extension of Cambio COSMIC (EHR) 1. Identify or monitor the clinical problems 5. Deploy Runtime CDSS inside COSMIC (EHR) 3. Analyze EHR data in CDS workbench 4. Confirm the clinical gaps and find areas for improvements 41

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, doi: /eurheartj/ehq278 42

43 Compliance Checking 43

44 Compliance Checking Results 44

45 Archetype Research in Ireland (with a focus on records to support biomedical research) Damon Berry Dublin Institute of Technology 45

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 46

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. 47

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. 48

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 49

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! 50