1. Bringing Things Together and Linking to Health Information using openEHR Koray Atalag, MD, PhD, FACHI Senior Research Fellow (ABI & NIHI) k.atalag@auckland.ac.nz

2. cell-cell connections proteins genomic sequence amino acid sequence torso Example: The Heart Physiome 3D cell tissue heart cellular processes nm m =10 9 nm Hunter PJ, Pullan AJ, Smaill, BH. Modeling total heart function. Annual Review of Biomedical Engineering, 5:147-177, 2003 LeGrice IJ, Hunter PJ, Smaill BH. Am.J.Physiol. 272:H2466-H2476, 1997

3. Myocardial activation Ventricular wall mechanics Ventricular blood flow Heart valve mechanics Coronary blood flow Neural control Torso model Composite lumped parameter cell model Hodgkin-Huxley type ion channel model Markov ion channel model 3D protein model (KCNQ1+KCNE1 ) Coarse grained MD model Quantum mechanics model Molecular dynamics model Continuum tissue model Organ model Discrete tissue structure model Calcium transport models Myofilament mechanics Signal pathway models Metabolic pathway models Gene regulation models 3D cell model

4. Tissue OsteonNephronAcinusLiver lobuleLymph nodeCardiac sheets Organ HeartLungsDiaphragmColonEyeKneeLiver Environment Organ system Organism Cell Protein Gene Atom Network x 1million 20 generations The challenge: organs to proteins

5. (www.cellml.org) Cuellar AA, Lloyd CM, Nielsen PF, Halstead MDB, Bullivant DP, Nickerson DP, Hunter PJ. An overview of CellML 1.1, a biological model description language.SIMULATION: Transactions of the Society for Modeling and Simulation, 79(12):740-747, 2003 Physiome Standards: CellML, SBML, FieldML,…

6. Why the need for clinical data? Biophysical models (geometric/mathematical) define real-world biological entities/processes Multi-scale biomedical integration requires understanding of normal and pathological phenomena Clinical records are sinks of valuable knowledge –Embody effects of environment/random phenomena Therefore clinical data will allow for –Better understanding (geno-pheno-enviro) –Model validation –Model customisation (e.g. personal parameters) –Predictive tools & advanced decision support 6

7. 7 Patient Avatar: digital representation of all health-related data that is available for the individual, as the general basis for the construction of Virtual Physiological Human workflows

8. VPH-Share Clinical Workflow Medical records Patient images Population data Images/Data Process Segment Mesh BCs 10% Surgical planning etc Risk of… Something Output Individualised risk score Surgical strategy Therapy Networks Multiple sources/types Cloud Scalable Computing Fast processing Remote Viz Efficient graphics Clinical ICT Simulation Visualisation Analysis

9. Integrating Physiome/Biomedical Informatics Current Physiome / VPH data and model integration work is mostly underpinned by Semantic Web There’s limited uptake of Semantic Web technologies in healthcare delivery (e.g. EHRs, HIS, CIS etc.) openEHR is a key medical informatics standard to link the two worlds; –Models of how to capture/represent clinical information –Supports data creation, validation, storage etc. –Supports explicit semantics via terminology bindings to formal ontologies –Used by many real-world systems today 9

10. The Science Domains 10

11. PhD Research (Aleksandar Zivaljevic): Annotation of clinical datasets using openEHR Archetypes Extending the RICORDO * Framework Bono B de, Hoehndorf R, Wimalaratne S, Gkoutos G, Grenon P. The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions. BMC Research Notes. 2011 Aug 30;4(1):313.

12. Conclusion Linked Data is a necessity –Phenotypes (e.g. diseases, findings, behaviour) –Genotype (e.g. Omics data) –Environment (e.g. physical, food, psychological) –Physiological (models, simulations, visualisations) Physiome / VPH needs to link to “real world” data –Shared resources & annotations key to linkage –TODO: clinical data platform w/ common meta-data has been utilised by VPH-Share (Avatars) HL7’s is also a key standard for exchange 12