Caisis 4.0: Re-Designing the Data Supply Chain

1. Caisis 4.0: Re-Designing the Data Supply Chain Paul Fearn, MBA Memorial Sloan-Kettering Cancer Center APIII – Sep 10, 2007

2. Caisis Project Goals • Integrate research and clinical data management activities and systems to improve quality/efficiency • Optimize data format and organization for processing by both humans and computers • Usability - “To be widely accepted by practicing clinicians, computerized support systems for decision making must be integrated into the clinical workflow. They must present the right information, in the right format, at the right time, without requiring special effort. In other words, they cannot reduce clinical productivity” – Brent C. James, NEJM 2001 • Facilitate collaboration through widespread adoption of an open source system (adopted by 15 sites in four countries, data for over 165,000 patients) • Develop economies of experience, scale and scope • Do better science! (reproducible results) Supported by National Cancer Institute grant R01-CA119947

3. Web-based (and cross-browser compatible) Microsoft SQL Server, ASP.NET, C# platform No special toolkits, frameworks or proprietary modules needed beyond .NET platform Open source license (GPL) to facilitate innovation and collaboration with other sites XML/metadata-driven user interface Designed to include new modules and plug-ins Caisis 4.0 Technology/Architecture

4. Caisis 4.0 User Interface

5. Data Supply Chain Concepts • Data/information - HPI, billing and diagnosis codes, annotation for specimens, medical record, research datasets, tumor registry reports, adverse event reports • Consumers – patients, clinicians, investigators, statisticians, medical records, billing • Suppliers/sources – patients, physicians, institutions, departments, systems, “silos”, other s (eg SSDI) • Processing/activities – physician, data manager, investigator, clinical and research operations • Distribution – manual data entry, ETL, real-time • Storage – “inventory”, “warehouses”, databases and information systems • Management/coordination – design and sustain Hugos, M. Essentials of Suppy Chain Management, 2nd Edition, 2006HBR on Supply Chain Management, 2006

6. Figuring Out the Data Supply Chain BillingSystem New VisitNote LabReport MedicalRecord RadiologyReport Clinical DataWarehouse PathReport TumorRegistry Tx Summary ResearchDatabase F/U VisitNote Data | Consumer | Supplier | Processing | Distribution | Storage | Mgmt

7. Workflow Design: Follow-up Visit • Beginning of visit • Consumer(s): MD • Data: relevant PMH, HPI, recent results, symptoms, medications, QOL • Upstream supplier(s): Patient, Lab, Radiology, Pathology, EMR • End of visit • Downstream consumer(s): patient, billing, medical records, scheduling, researchers • Data: prescriptions, plan, education, encounter bill, documentation, status • Supplier(s): MD

8. eForms

9. Data Feed Prioritization Lab Values Appts Procedures Demo-graphics ProtocolAccruals SSDI High Collection Cost Low Real-Time Velocity >6 Week Lag Where is the “biggest bang for the buck”? Where is the “low-hanging fruit”?

10. “Swim-Lanes” and SilosUnderstanding Data Storage and Processing

11. Quality Effects of Integration Clinic Workflows • Populate clinic forms from research database • Multiple people view, enter and update data • Collect research data during clinical workflows Research Workflows • Fill gaps / correct errors • Identify analysis outliers • Longitudinal follow-up

12. Data “Supply Chain” Analogy • Data / information: in its most raw, granular form • Consumers: Who needs what data or information? When, where and how? What format? • Suppliers / sources: Who generates/collects what data elements? When, where and how? What format? • Processing / activities: Who can most efficiently or effectively process what data? When, where and how? • Distribution: Who transports what data? When, where and how? What format? • Storage: Who stores what data in a warehouse or database? Where and how? What format? • Management / coordination: • Capture data as far upstream as possible • Minimize steps, especially manual ones (OHIO) • Organize chain of collection, movement, storage and processing to efficiently deliver data or information to consumer JIT for use

13. Free Software and Collaboration To demo, download or get more information visit http://Caisis.org

14. MSKCC Caisis Team - 2007 Kevin Regan Avinash Chan Frank Sculi Vicki Cameron Kerry McCarthy Paul Alli Beth Roby Brandon Smith Jason Fajardo Not pictured: Tumen Tumur, Kinjal Vora

15. Appendix: Caisis Project Timeline • Microsoft Access databases • 1999 ProstateDB 1.0 • 2000 PRDB / Prostabase • ColdFusion & SQL Server web-based database • 2002 Valhalla 1.0 – 1.1 • Prostate • 2003 Valhalla 1.2 (7,994 patients) • Billing/EMR compliant populated clinic forms • Microsoft.NET & SQL Server web-based database • 2004 Caisis 2.0 – 2.1 (26,470 patients) • Integrated bladder, kidney, testis • 2005 Caisis 3.0 – 3.1 (44,000 patients) • Prostatectomy eForm, protocol manager, tumor maps • 2006 Caisis 3.5 – (55,000 patients) • GU and Urology Prostate Follow-up eForms • 2007 Caisis 4.0 – (65,000 MSKCC patients) • Metadata-driven, dynamic forms, new diseases and eForms

16. Appendix: Caisis Next Steps, 1 of 2 • BISTI/National Cancer Institute grant R01-CA119947 • Restructure data model to accommodate other diseases through metadata-driven fields and dynamically generated web forms • Migrate dataset production algorithms, nomograms, longitudinal patient follow-up tools, project tracking and other prototyped features into the Caisis framework • Make Caisis compatible with interoperability standards from the Biomedical Informatics Grid (caBIGTM) • Support adoption and collaborative development of Caisis by maintaining the Caisis.org website, web conferences and face-to-face meetings, issue tracking, and training and documentation • Simplify installation, configuration, security, auditing, customization and ongoing maintenance • Program the web-based user interface for compatibility with all major web browsers • Improve the system’s scalability and portability

17. Appendix: Caisis Next Steps, 2 of 2 • eForms • Form tracking and email system for scheduled surgeries and clinic visits • Shift navigation from passive to directing and “pulling” users through tasks • Reduce physician time and clicks to complete forms • Specimen tracking module • Plugins

18. Appendix: Multi-Institutional Adoption / CollaborationOver 15 sites, 400 users, and 165,000 patients • Baylor College of Medicine • Cancer Research UK - London • Case Western Reserve University • Cleveland Clinic • Eastern Virginia Medical Center • Helios/Wuppertal • George Washington University • McGill University • MD Anderson Cancer Center • Memorial Sloan-Kettering Cancer Center • North Shore Long Island Jewish Health System • Ottawa Hospital – Civic Campus • Seattle Consortium (Fred Hutchinson / Univ of Washington) • Stiftung biobank-suisse • University of Alabama – Birmingham • University of California - Davis • University of Malmö - Sweden • University of Rochester • University of Texas – San Antonio • University of Texas Southwest Medical Center • Wake Forest University • Wayne State University / Karmanos Cancer Institute • Westmead / Breast Cancer Tissue Bank – Australia

19. Appendix: Caisis Privacy and Security • Limited access to patient data by job function (role/permissions) and dataset • HIPAA compliant data export • IRB approval or de-identification required • Disclosures logged • Tracking / Logging • Who views which patient • Who performs what action • Nothing is overwritten (full audit trail)

20. Appendix: Dataset Production Algorithms Automated variable selection and progression calculations

21. Appendix: Caisis Protocol Manager

22. Appendix: External Interfaces / caBIG caBIG MSKCC Network MSKCC DMZ caBIG Grid caTISSUE Suite Catalog Tracking JIT Annotation

23. Appendix: Metrics