1. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 Empowering Translational Research through Distributed Collaborative Computing Dr. Ed Conley 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

2. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 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. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 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. Distributed Collaborative Computing Low-cost replicable ‘STACK’ ~ creates ‘LOCAL’ TRUSTED SERVER ‘NODES’ e.g. Collaboration A e.g. Collaboration B Trusted nodes are portable: and reconfigurable: …and scalable: LOCAL DATA STORAGE NODE

4. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 ‘Nodes’ Can Improve Interoperability Sets Internal INTER-NODE TRANSACTION SEMANTICS External (global) SEMANTIC REFERENCE STANDARD(S) …to deploy at scale: IMPLEMENT IN ‘STACK’

5. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 AIM is a COMMODITY solution for (1) AFFORDABLE HEALTHCARE I.T. OUTCOMES MONITORIN G & RISK ANALYSIS OUTCOMES DIRECT PATIENT CARE PATIENT PATH e.g. @home & mobile Monitor timeline INTERVENTIONS Centred on PATIENT PATIENT IDENTIFIABLE DATA USES CLINICAL RESEARCH distributed collaborations AUDIT & GOVERNANCE AUDIT e2e QUALITY e.g. ‘lean clinics’ e2e VALIDATE COHORT OUTCOME S e.g. for efficacy & toxicity VALIDATE COHORT OUTCOME S e.g. for efficacy & toxicity Modelling & DISCOVERY PATIENT NON-IDENTIFIABLE DATA USES ComparativePERFORMANCESemanticAGGREGATION (2) EHR LINKED-OUTCOMES (PSEUDONYMISED) STUDIES CONTINUOUS WITH e.g. TIMELINE MONITORING

6. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 etc. COMMUNITY SOCIAL CARE INDEPENDENT CLINIC GYM HOSPITAL ‘Patient path’ MOBILE Mobile hubs WORK IDdata PP BIND IDdata PP BIND IDdata PP BIND IDdata PP BIND IDdata PP BIND IDdata PP BIND IDdata PP BIND HOME DEVICES HL7 CONSENT DIRECTIVE – PRIVACY POLICIES ‘Data Path ’ + ID data BIND DATA Device data Patient consent to Privacy Policy Home hubs Consistent Timeline Monitoring (incl. data format, HL7 Consent Directive, CDA release 2, CDISC ODM)

7. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 Sintero Node Interoperability Layers Replicated in each server node L1: data documents (CDA, ODM) L2: Internationalised XDS ID’s’, timestamps, codes/values, consent Stream Inferable Data Documents  RDBMS L3: Semantic Aggregation L4: Executable Code Workflows Re-stream Aggrg. Data RBAC Boolean queries INPUTS OUTPUTS

8. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 STUDY FEASIBILITY HL7 CONSENT DIRECTIVE LOCAL Aggregation Layer 3 (EHR-linked-pseudonymised) BENEFIT: Attribute-level subject recruitment SECOND STREAM = Limited complexity database STREAM INFERABLE DATA L2-L3 Globally consistent syntax (Boolean) Inclusion, Exclusion on Code/Value Globally consistent syntax (Boolean) Inclusion, Exclusion on Code/Value LAYER 1 VERBOSE DATA DOCUMENTS CDA / ODM ON TIMELINE L2

9. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 BENEFIT: EHR outcomes modelling Compute Y = function (X) Rank variables Rank variables nodeID’s,PSID’s,codes,values,timestamps Time XDS Longitudinal entries MEASURES = X-variables XDS OUTCOME = Y-variable X Y X Model for Outcome Y

10. PhUSE 2010 Conference - CDISC and Industry Standards Discussion Club - Tuesday, October 19th 2010 Summary DCC Group 2011-Q1 2011-Q2 2010-Q4 TIMELINE 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. Funded by Acknowledgements - Sintero Team STEERING & ADVISORY GROUP Charlie McCay HL7 UK Dave Iberson-Hurst CDISC Prof. Fox Cancer Research UK + Infermed Graham Kennedy NWIS Malcolm Newbury IHE-UK

12. Funded by THANKS to PhUSE! Dr. Ed Conley THANKS to PhUSE! 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