University of Minnesota Masonic Cancer Center

1. University of MinnesotaMasonic Cancer Center Masonic Information Exchange (MIX) Overview March 2, 2010

2. Agenda • Overview of Masonic Information Exchange (MIX) Program • Objectives • Approach • Clinical • Technical • Issues Eclipse Data Systems

3. Business Objectives of MIX Strategic Information Management Eclipse Data Systems

4. MIX Program “Free the data,” Sankaran said “the patient is waiting. – Vish Sankar, Federal Health Architecture Program Director The primary focus for the MIX is to free the data from the systems used to collect it. • The goal of the MIX Program is to establish an environment that will enable any number of projects that require information exchange or integration to be executed in an environment that has standards, security, and tools to enable data integrity, ease of use, and access of data. The environment must include: • Technology such as development, testing and production servers with tools, databases, adapters and applications • Policies and processes to manage information on the MIX platform such as Data Quality, Data Governance, Data Security and Data Rights management. • Methodology for responding to ongoing requests for more data, new applications, additional reports and analytics and sharing across institutions. Eclipse Data Systems

5. Program Objective of MIX ResearchersData Owners Responsible for quality IT Data Stewards Monitor Quality Store/Manage Clinical, Lab Data Analyze Collaborate Data Capture Deliver Improve Augment Governance Process Governance Process Data entry Electronic data feeds Integrate Reports, Analytics, export data feeds, cross-data set analysis Preserve Eclipse Data Systems

6. MIX Design Guidelines Principles: • Capture data as discrete items of information at the point of entry – This eliminates the need to enter data on a form and have it re-typed and re-entered on another form as the current process requires. • Use industry standards wherever possible– Adopting industry established data standards will ensure consistency, resulting in reliable report results and interoperability. • Determine the ‘System of Record’ for each data element captured and stored – This will establish the ‘source of truth’ • Establish a solid data governance planthat will ensure the efficacy of the information: • Insures the integrity and security of the information. • Accessibility to everyone who needs information with the proper authorization • Sufficient flexibility to meet the diverse needs of the entire user community • Sufficient timeliness to stay abreast of the changing needs for new protocols, requests from regulatory or collaborative organizations for multi-site studies • Responsiveness for ingoing requests for reports and data Eclipse Data Systems

7. Initial Scope • Data Access - Develop a seamless reporting and analysis environment for Biostatisticians, PIs, management and regulatory reports • LIMS output reports • miniTACO • Biostatisticians • Others… • Patient Registry – Use caBIG Tool - C3PR • Manage Patient datafor the cancer center • Replace NDB and Stemsoft for managing patient and donor information • Provide Patient registry for PI • BMT Migration – multiple components to analyze • OnCore – BMT Pilot Project • OTTR Analysis • Analysis of Patient Registry to replace Stemsoft • Fairview EPIC interface • AGNIS – CIBMTR Reporting Eclipse Data Systems

8. Clinical Impact • What is the source of data? • Establish a seamless (electronic) interface wherever possible • Document current workflow for BMT • Streamline and reduce multiple data entry into different systems • How does this impact CDAs, Clinicians, Research Staff? • Understand the data that will be captured in EPIC • Establish an electronic interface from Fairview to receive the clinical data: • Reporting and analysis on MIX Platform • CIBMTR Reporting • Document the Patient/Donor workflow • Identify the requirements for Patient Registry to replace Stemsoft • Identify the data that needs to be managed • OnCore Pilot to determine the capacity for OnCore to manage BMT data • OTTR analysis • Currently used by Fairview for solid organ transplants • Developed a version for BMT • Would require significantadditional data entry – contradicting an essential principle Eclipse Data Systems

9. MIX Eclipse Data Systems • Staging Area • Data Management Policies • Map to caDSR – CDE • AGNIS • Data Analysis and profiling • Perform security assessment • Data Repository • Data Analysis - Create Governance, Quality Standards • Design based on standards • Map from Source to Standards • Develop Data Rights policies and controls • Pilot caBIG Tools • caExchange, caDSR, C3PR etc.

10. MIX Technical Platform • Operational Platform – addresses the applications used to collect and manage information to support Masonic Cancer Center (Application Layer) • OnCore, BMT, LIMS, C3PR, CAERS etc. • Integration Platform – supports the data exchange processes, tools, standards that allow data to be exchanged between systems and accessed for reporting (Integration Layer) • caIntegration Hub (CaXchange), Staging Database • Data Repository – Integrated data sets for analysis, Data Marts, persistent historical data (Data Access Layer) • Metadata Platform – standards, processes, rules and definitions that describe data management reqts, access rights, security etc. (Data Management Layer)

11. Data Repository MIX Platform Phase 1 LIMS PI database Reports Mini-TACO Biostatisticians SAS Environment • MIX Data Management • Daily refresh • Exact copies of data • Used for reporting, analytics • Data profiling for QA • Reference Data Sets, Xref Tables • Common or shared transformations • CDEs from caDSR, other standards • Data Rights Management Integration & Transformation Staging Area HL7 BMT Legacy Fairview C3PR Patient Registry OnCore LIMS BMT TASC Source Systems Eclipse Data Systems

12. Data Asset Management Goals • Establish standard definitions of data including: • Common Data Elements from caDSR • CPT, ICD9, SNOMED, LOINC – standard industry definitions • Develop a corporate wide Taxonomy that includes: • Categorization • Contextualization • Increase the value of data thru quality and completeness. • Define data retention criteria for freshness and relevancy • Standardize the content of data by: • Developing standard transformations, calculations and derivations • Documenting business rules • Improve the quality of data through: • Establish System of Record for data • Continual monitoring of data quality • Researching data issues and implementing solutions • Provide security and privacy control around the data.

13. Issues • Opportunities and Challenges • Epic/Beacon implementation • Need reqts for input and output formats for clinical information (AGNIS/NMDP Reporting) • Eliminate NDB and Stemsoft AND support research studies needing patient registry data • caBIG tool implementation – NCI compliance and potential grant • Develop new systems while supporting current operations • Initial Reqts Gathering • work flow, data flow interfaces between systems • Review interoperability and dependencies • Propose new design