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

3. 2 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) Caisis Project Goals Supported by National Cancer Institute grant R01-CA119947

4. 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

5. 4 Caisis 4.0 User Interface

6. 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, 2 nd Edition, 2006 HBR on Supply Chain Management, 2006

7. 6 Path Report Radiology Report Lab Report F/U Visit Note Figuring Out the Data Supply Chain Tumor Registry Tx Summary New Visit Note Research Database Medical Record Billing System Clinical Data Warehouse Data | Consumer | Supplier | Processing | Distribution | Storage | Mgmt

8. 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

9. 8 eForms

10. 9 Data Feed Prioritization >6 Week Lag Real-Time Velocity High Low Collection Cost Lab Values Demo- graphics Appts Procedures SSDI Protocol Accruals Where is the “biggest bang for the buck”? Where is the “low-hanging fruit”?

11. 10 “Swim-Lanes” and Silos Understanding Data Storage and Processing

12. 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

13. 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

14. 13 Free Software and Collaboration To demo, download or get more information visit http://Caisis.org

15. 14 MSKCC Caisis Team - 2007 Beth Roby Vicki Cameron Jason Fajardo Avinash Chan Brandon Smith Kevin Regan Paul Alli Frank Sculi Kerry McCarthy Not pictured: Tumen Tumur, Kinjal Vora

16. 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

17. 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 (caBIG TM ) 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

18. 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

19. 18 Appendix: Multi-Institutional Adoption / Collaboration Over 15 sites, 400 users, and 165,000 patients 1. Baylor College of Medicine 2. Cancer Research UK - London 3. Case Western Reserve University 4. Cleveland Clinic 5. Eastern Virginia Medical Center 6. Helios/Wuppertal 7. George Washington University 8. McGill University 9. MD Anderson Cancer Center 10. Memorial Sloan-Kettering Cancer Center 11. North Shore Long Island Jewish Health System 12. Ottawa Hospital – Civic Campus 13. Seattle Consortium (Fred Hutchinson / Univ of Washington) 14. Stiftung biobank-suisse 15. University of Alabama – Birmingham 16. University of California - Davis 17. University of Malmö - Sweden 18. University of Rochester 19. University of Texas – San Antonio 20. University of Texas Southwest Medical Center 21. Wake Forest University 22. Wayne State University / Karmanos Cancer Institute 23. Westmead / Breast Cancer Tissue Bank – Australia

20. 19 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) Appendix: Caisis Privacy and Security

21. 20 Automated variable selection and progression calculations Appendix: Dataset Production Algorithms

22. 21 Appendix: Caisis Protocol Manager

23. 22 caTISSUE Suite MSKCC DMZ Catalog MSKCC Network Appendix: External Interfaces / caBIG caBIG Grid JIT Annotation caBIG Tracking

24. 23 Appendix: Metrics