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It is based on an internationalised software “stack” intended to work the same way everywhere

Empowering Translational Research through Distributed Collaborative Computing Dr. Ed Conley Distributed Collaborative Computing Research 1 & Severnside Translational Research Alliance Schools of Computer Science 1 and Medicine 2 Cardiff and Bristol Universities, UK conleyec@cardiff.ac.uk.

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It is based on an internationalised software “stack” intended to work the same way everywhere

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  1. Empowering Translational ResearchthroughDistributed Collaborative ComputingDr. Ed ConleyDistributed Collaborative Computing Research1 &Severnside Translational Research AllianceSchools of Computer Science1 and Medicine2Cardiff and Bristol Universities, UKconleyec@cardiff.ac.uk

  2. Sintero Server is a prototype distributed clinical computing platform funded by Wellcome Trust Translational Programme It tries to Simplify INTEROperability in large-scale healthcare & clinical research applications It aims to be highly cost-efficient with low ‘footprint’to ‘defragment’ collaborative processes It is based on an internationalised software “stack”intended to work the same way everywhere

  3. Distributed Collaborative Computing Sintero is an internationally-distributed collaborative computing platform. For assisting translation, it is targeted equally into two areas: 1. Affordable healthcare initiatives - providing new “low-cost and scalable” options for open source clinical data “node” infrastructures and patient-centred service hosting. For example, “commodity interoperability” solutions that are portable. Through internationalised software “stack” implementations they work the same way everywhere. 2. Standard computational workflows for translational research – i.e. chained blocks of standard executable server code harmonising multi-site (especially international) clinical research collaborations. These absolutely depend on assisting the collaboration “nodes” to use single semantic/syntax standards. Sintero is designed to implement/deploy any externally defined semantic model and work with multiple EHR’s and EDC systems. Trusted nodes are portable: Low-cost replicable ‘STACK’ ~ creates ‘LOCAL’ TRUSTED SERVER ‘NODES’ and reconfigurable: …and scalable: e.g. Collaboration A LOCAL DATA STORAGE NODE e.g. Collaboration B

  4. ‘Nodes’ Can Improve Interoperability …to deploy at scale: External (global) SEMANTIC REFERENCE STANDARD(S) IMPLEMENT IN ‘STACK’ SDD Sets InternalINTER-NODE TRANSACTION SEMANTICS

  5. AIM is a COMMODITY solution for CONTINUOUS WITH (1) AFFORDABLE HEALTHCARE I.T. (2) EHR LINKED-OUTCOMES (PSEUDONYMISED) STUDIES Comparative PERFORMANCE Semantic AGGREGATION Modelling & DISCOVERY Centred on PATIENT Monitor timeline INTERVENTIONS e.g. TIMELINE MONITORING PATIENT NON-IDENTIFIABLE DATA USES PATIENT IDENTIFIABLE DATA USES AUDIT &GOVERNANCE e2e QUALITYe.g. ‘lean clinics’ VALIDATE COHORTOUTCOMES e.g. for efficacy& toxicity CLINICALRESEARCHdistributedcollaborations DIRECTPATIENTCARE PATIENTPATHe.g. @home& mobile OUTCOMES MONITORING& RISKANALYSIS

  6. HL7 CONSENT DIRECTIVE – PRIVACY POLICIES HOSPITAL ‘Patient path’ Consistent Timeline Monitoring(incl. data format, HL7 Consent Directive, CDA release 2, CDISC ODM) DEVICES ID ID ID ID ID ID ID data data data data data data data WORK PP PP PP PP PP PP PP COMMUNITY BIND BIND BIND BIND BIND BIND BIND ‘Data Path’ INDEPENDENTCLINIC Mobile hubs SOCIALCARE GYM BINDDATA MOBILE etc. + IDdata Home hubs Device data HOME Patient consent to Privacy Policy

  7. Sintero Node Interoperability Layers Replicated in each server node OUTPUTS L4: Executable Code Workflows Re-stream Aggrg. Data RBAC Boolean queries L3: Semantic Aggregation Stream Inferable Data Documents RDBMS L2: Internationalised XDS ID’s’, timestamps, codes/values, consent INPUTS L1: data documents (CDA, ODM)

  8. BENEFIT: Attribute-level subject recruitment Globally consistent syntax (Boolean) Inclusion, Exclusion on Code/Value STUDYFEASIBILITY SECOND STREAM LOCAL Aggregation Layer 3 = Limited complexity database (EHR-linked-pseudonymised) STREAMINFERABLEDATA L2-L3 HL7 CONSENT DIRECTIVE LAYER 1 VERBOSEDATA DOCUMENTSCDA / ODM ON TIMELINE L2

  9. XDS BENEFIT: EHR outcomes modelling OUTCOME= Y-variable MEASURES = X-variables Longitudinal entries X X Y Time XDS XDS nodeID’s,PSID’s,codes,values,timestamps Rank variables Compute Y = function (X) Model for Outcome Y

  10. Summary DCC Group TIMELINE 2011-Q2 2010-Q4 2011-Q1 First release • NEXT STEPS • Community of practice funding for internationally-distributed computing • esp. Pharma programmers who analyse results of clinical trials • Regulatory developments (end-to-end) • Information governance for research networks (pseudonymisation)

  11. Acknowledgements - Sintero Team STEERING & ADVISORY GROUPCharlie McCayHL7 UK Dave Iberson-HurstCDISCProf. FoxCancer ResearchUK + Infermed Graham Kennedy NWIS Malcolm Newbury IHE-UK

  12. THANKS to PhUSE!Dr. Ed ConleyDistributed Collaborative Computing Research1 &Severnside Translational Research AllianceSchools of Computer Science1 and Medicine2Cardiff and Bristol Universities, UKconleyec@cardiff.ac.uk

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