Helseit 2012-klein-plenary on-ehr-cr


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

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  • Meeting the need of having a scalable and sustainable business model for EHR data re-use
  • Helseit 2012-klein-plenary on-ehr-cr

    1. 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 1
    2. 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 2
    3. 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. 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 4
    5. 5. Why should we attempt touse 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 cakeOr … 5
    6. 6. Is the EHR data onlygarbage? 6
    7. 7. If we put garbage in a vault 7
    8. 8. DatatilsynetProtected asgold 8
    9. 9. Do we expect to get a treasure? 9
    10. 10. Is the ocean empty? Studies have shown that in routine usea lot of things never become documented 10
    11. 11. Is the ocean empty?Or is it a gold mine? 11
    12. 12. How can we turn EHRs into gold mines ? 12
    13. 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. 14. The requirements for EHR information and some of the problemsin routine record information for research Arnulf Langhammer 2012 09 19 AL EHR 14
    15. 15. HUNT Research Centre, LevangerProject leader of the Lung and Osteoporosis StudyHead of HUNT Databank General practitioner Høvdinggården Legekontor, Steinkjer 15
    16. 16. The Nord-Trøndelag Health StudyHUNT 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 16
    17. 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. 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. 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. 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. 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. 22. Obstacles to EHRbased researchScattered EHRsThe records over time of oneindividual may be scattered inseveral institutions:- geographic location- specialty- legal entity c.f. the divisionbetween primary care andspecialist health care, in Norway 22
    23. 23. Obstacles to EHRbased researchVarious formats and terminologiesThe data of the EHRs exists in variousformats with regard to informationstructure 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. 24. Obstacles to EHRbased researchLack of structureOften there is very little structure inthe EHR systems of today.Typewriters.Many health care organisations andthus systems have focused on theperceived easiness for the physiciansto record data, with the use of free textdictation as the solution, more andmore often combined with automaticspeech recognition software. 24
    25. 25. Obstacles to EHRbased researchPrivacy concernsConcerns about protecting theconfidentiality of sensitivepersonal information must alsobe addressed. Ethical approvaland patient consent isnecessary. New systems mayfacilitate the latter usingelectronic means and the net. 25
    26. 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. 27. Gerard Freriks showed us impressive figures onthe business case for the pharmaceutical industryWhen conducting clinical trials using EHR datathere are potential savings for one big company alone EUR/year 27
    28. 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 28
    29. 29. Pilot experiences were quite promising 29
    30. 30. Overview of the EHR4CRprojectElectronic 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. 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 31
    32. 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. 32
    33. 33. Protocol Feasibility Pilot• Pilot ready October-November 2012 with 11 Hospitals RDLT meeting July 2012 33
    34. 34. Vision 34
    35. 35. Rong Chen, MD, Ph.D.chief medical informatics officer atCambio Healthcare Systems and affiliated withKarolinska Institutet, Stockholm, SwedenEHR Data Reuse throughopenEHR Archetypes 35
    36. 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. 37. IFK2 – Pilot with the Swedish Heart Failure register 37
    38. 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. 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 39
    40. 40. Rong Chen showed a world premiere of thenew 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 labelsExtensive reuse of existing openEHR specificationsAiming to release through openEHR as open Source 40
    41. 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) 41
    42. 42. Case Study: Antithrombotic Management in AtrialFibrillation • 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 42
    43. 43. Compliance Checking 43
    44. 44. Compliance Checking Results 44
    45. 45. Archetype Research in Ireland(with a focus on records to support biomedical research) Damon Berry Dublin Institute of Technology 45
    46. 46. Example 1: Archetype-basedshared 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. 47. Example 2: Archetypes for CFreview 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. 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. 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. 50. The road to better health goes through research andstructured EHR systems based on standards Strukturert EPJ Gunnar O Klein professor i helseinformatikk Presentation for Helse Midt-Norge, IKT- strategigruppa 13 september, 2012A bridge to the futureIt starts now! 50