Clinical Bioinformatics: New Challenges for Human-Centered Decision-Support Systems Speakers: Riccardo Bellazzi, University of Pavia Patrice Degoulet, Hospital Georges Pompidou (Paris) Amnon Shabo, IBM Research (Haifa) Vimla Patel, New York Academy of Medicine Moderator: Edward H. Shortliffe, Arizona State University www.mie2012.it! 1990s: Relationship of Medical Informatics and Bioinformatics! Biological and Clinical Applications of Interrelated Techniques and Medical Bioinformatics! Solution:Methods! ! Informatics! “Biomedical”! Informatics! Anticipation of their Future! Clinical Interdependencies! Biomedical Informatics in Perspective BiomedicalClinical Informatics (BMI) Basic! Education and Research! Research ! Bioinformatics! Methods, Techniques, Theories! Bioinformatics and Health Informatics: Structural Clinical Informatics Applied (Imaging) and Public Health Research Informatics! Informatics! and Practice! Informatics in Translational Science:! Translational Bioinformatics (TBI) and Clinical Research Informatics (CRI)! Molecules, Cells, Tissues, Patients, Individuals, Populations , Organs! Societies! Four Presentations •" Clinical Bioinformatics – decision support through knowledge and data integration (Riccardo Bellazzi) •" Bioclininical data warehouses and translational research (Patrice Degoulet) •" The Data Representation Challenge: Encapsulate and Bubble-up (Amnon Shabo) •" Cognitive Foundations for Decision Support in Clinical Bioinformatics (Vimla Patel) Billedet kan ikke vises. Computeren har muligvis ikke hukommelse nok til at åbne billedet, eller billedet er muligvis blevet beskadiget. Genstart computeren, og åbn derefter filen igen. Hvis det røde x stadig vises, skal du muligvis slette billedet og indsætte det igen. www.mie2012.it! Discussion! Please use microphone and identify yourself and your affiliation! Bioclinical Data Warehouses & Translational Research : the HEGP Case Patrice Degoulet & Paul Avillach Faculté de Médecine René Descartes, Université Paris 5 Hôpital Européen Georges Pompidou (HEGP) INSERM UMR_S 872 eq22 August 2012 Why should we develop Bioclinical Data Warehouses 1. EHR driven research – Patient selection for CR studies (e.g., EHR4CR) – Biomarker discovery (phenotype-genotype) – Personalized medicine 2. Phenotypic augmentation for clinical research studies – Additional source of bioclinical data for cohort studies – Reuse of EHR data to feed a CR study • Prokosch HU, Ganslandt T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf Med. 2009; 48(1): 38-44. • Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011; 12(6): 417-28. • Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012; 13(6): 395-405. HEGP Background • HEGP is the most recent acute care hospital within the 37 AP-HP hospitals • HEGP meets the needs of the 600, 000 inhabitants of the Paris south-west HEGP background Shared Biobank (2008-) HEGP BDW EHR/BDW integration Production environment Evaluation/Research environment ETL suite (Talend Open Studio) EHR : EHR : Biomedical Data External Operational Mirrored Warehouse Databases Database Database (BDW) (ODS) R Real time requests i2b2/tranSMART tools Business Object Data Analysis IBM Ilog Rules Data Mining HEGP BDW I2b2 + tranSMART data sources Health care Clinical Research Health Information System Browser and Analysis DRG tools available for MD & Pharm EHR forms BDW EHR reports i2b2 ETL Structured data from research Biology CDW studies Imaging ‘omics ETL once aweek ETL +’omics ETL Pathology ‘omics data Rx BDW content i2b2 CDW content (July 2012) Dimensions Categories Numbers Patient 432 033 Concept ICD10 classification 21 356 dimension Laboratory results classification 8 272 Drug classification (ATC) 33 612 EHR forms concepts 5 950 Observation facts ICD10 Diagnosis 2 537 633 Laboratory results 85 598 854 Drug prescriptions 2 474 985 EHR forms items 28 641 547 Text reports 902 747 BDW use i2b2 CDW queries (Jan. 2011-Aug. 2012) • 158 MD + Pharm trained • 1386 requests - 15 cohorts created (i2b2 datamarts) CIS use In vivo evaluation of decision rules (EHR) Rate of inappropriate prescriptions 6 alternating 2-month phases: control vs. intervention (Aug. 2006- Aug. 2007) Physicians Alerting off Alerting on p Junior 21.5% 16.3% Senior 20.9% 29.3% p p=0.88 p=0.01 Total 21.3% 19.9% 0.63 (NS) Sellier E et al. Effect of alerts for drug dosage adjustments in inpatients with renal insufficiency. JAMIA 2009; 16:203-10. BDW use In silico evaluation of decision rules (BDW) Development and optimization of clinical decision rules for dosage adjustment of « renal function dependent » drugs Boussadi A, Caruba T, Zapletal E, Sabatier B, Durieux P, Degoulet P. A clinical data warehouse– based process for refining medication orders alerts. J Am Med Inform Assoc 2012; 19(5): 782-5 BDW use In silico evaluation of decision rules (BDW) Initial set Final set (CPOE vn) Suppressed : 45 (16.1%) (CPOE vn+1) 280 rules 371 rules Modified : 105 (37.5%) (10 drugs) (10 drugs) Added : 136 (48.2%) Boussadi A, Caruba T, Zapletal E, Sabatier B, Durieux P, Degoulet P. A clinical data warehouse– based process for refining medication orders alerts. J Am Med Inform Assoc 2012; 19(5): 782-5 BDW content “Omics” data integration http://www.transmartproject.org ! The Transmart integration process http://www.transmartproject.org BDW content Pilot Study • Laurent-Puig P, Cayre A, Manceau G, Buc E, Bachet J-B, Lecomte T, et al. Analysis of PTEN, BRAF, and EGFR status in determining benefit from cetuximab therapy in wild-type KRAS metastatic colon cancer. J. Clin. Oncol. 2009 Dec 10;27(35):5924–30. TranSMART-based Ontology definition Proof of concept • R module in tranSMART • Published figure in JCO The HEGP BDW Conclusion (1) Achievements • A methodology to export concepts and data from an integrated CIS (2009-) • An operational CDW directly used by MD and Pharm (2011- ) • Installation & evaluation of a tranSMART platform to augment clinical data with omic information (2012-) Benefits of the approach • Availability of clinical data has generated a virtuous cycle at the HEGP end-user level (e.g., improved standardized questionnaires) • tranSMART is based on the i2b2 model for clinical data The HEGP BDW Conclusion (2) Limits of the approach • Lack of semantic integration tools to merge i2b2-tranSMART concepts • i2b2-tranSMART data model heterogeneity Perspectives • Semantic integration within the i2b2-tranSMART platform to enable: 1) meta-analysis from multiple cohorts 2) phenotypic augmentation in genomic driven research Acknowledgments www.i2b2.org Informatics & Public Health Dept. www.transmartproject.org Pierre Durieux, MD Eric Zapletal, PhD www.recomdata.com Kevin Zarca, MD Vincent Canuel, Resident Contacts : [email protected] [email protected] MIE 2012 – Panel on Clinical Bioinformatics August 27, 2012 New challenges for human-centered decision- support systems The Data Representation Challenge Presented by Amnon Shabo (Shvo) • Chair, Medical Informatics Community of IBM Research • Head of IBM HCLS* Standards Program • Co-chair & facilitator of HL7 Clinical Genomics • Co-editor of CDA R2, CCD and Pedigree IBM Haifa Research Lab (HRL) * HCLS = Healthcare and Life Sciences © 2012 IBM Corporation Haifa Research Lab Main Concepts 1. Flat representations are flat tires for CDS engines…! Health data semantics and context cannot be faithfully represented using flat structures (e.g., a list of entries), rather it requires a compositional language that associates various data entries to a meaningful statement 2 Haifa Research Lab The New Generation of Standards is Object-oriented! Observation Procedure Object Object Code Insert into basic Participant Medication health objects Code Object Grammar* Object Code SNOMED, LOINC, ICD, etc. Observation O1 (consisting of Observations O11 and O12 and related to Subject S1), is Clinical Statement the reason for Procedure P1 (performed by Clinician C1) which is the cause of Observation O2… Lab Pharma Docs Others * The grammar should be part of a health-dedicated reference information model, e.g., the HL7 RIM or the openEHR RIM 3 Haifa Research Lab Clinical Omics Statement ! § An effort within the HL7 Clinical Genomics Work Group § Latest effort: CDA Implementation Guide for Genetic Testing Reports (GTR) § An abstract Clinical Omic Statement (COS) template that § Has at its core a genomic observation (e.g., a DNA sequence variation) § If it’s a reportable finding, then it should be associated with indications and interpretations, specimen and genomic source class § The major finding can be associated with associated observation (e.g., amino acid change) § Optionally, performers may be specified (overriding header performers) § The COS abstract template is instantiated by specialized COS’s, e.g., for genetic variations or cytogenetics Associated Observations Indications Omics Interpretations Performers Observation Clinical Genomic Statement Clinical Genomic Specimen Source * Taken from the HL7 CDA Implementation Guide for Genetic Testing Reports. 4 Haifa Research Lab Main Concepts 2. ‘Encapsulate & Bubble-up’… Personal biological key data should be encapsulated in its native format into clinical data structures, where 'bubbled-up' items could be associated with phenotypic data, as part of clinical data standards 5 Haifa Research Lab Clinical Genomics Data Standards: Encapsulate