Caisis 4.0: Re-Designing the Data Supply Chain
Paul Fearn, MBA
Memorial Sloan-Kettering Cancer Center
APIII – Sep 10, 2007
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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
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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
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Caisis 4.0 User Interface
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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
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PathReport
RadiologyReport
LabReport
F/U VisitNote
Figuring Out the Data Supply Chain
TumorRegistryTx Summary
New VisitNote
ResearchDatabase
MedicalRecord
BillingSystem
Clinical DataWarehouse
Data | Consumer | Supplier | Processing | Distribution | Storage | Mgmt
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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
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eForms
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Data Feed Prioritization
>6 Week Lag Real-TimeVelocity
Hig
hLo
wC
olle
cti
on
Co
st
Lab Values
Demo-graphicsAppts
ProceduresSSDIProtocol
Accruals
Where is the “biggest bang for the buck”? Where is the “low-hanging fruit”?
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“Swim-Lanes” and SilosUnderstanding Data Storage and Processing
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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
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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
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Free Software and Collaboration
To demo, download or get more information visit http://Caisis.org
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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
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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
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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
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Appendix: Caisis Next Steps, 2 of 2
eFormsForm tracking and email system for
scheduled surgeries and clinic visitsShift navigation from passive to directing
and “pulling” users through tasksReduce physician time and clicks to
complete formsSpecimen tracking modulePlugins
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Appendix: Multi-Institutional Adoption / CollaborationOver 15 sites, 400 users, and 165,000 patients
1. Baylor College of Medicine2. Cancer Research UK - London3. Case Western Reserve University4. Cleveland Clinic5. Eastern Virginia Medical Center6. Helios/Wuppertal7. George Washington University8. McGill University9. MD Anderson Cancer Center10. Memorial Sloan-Kettering Cancer Center11. North Shore Long Island Jewish Health System 12. Ottawa Hospital – Civic Campus13. Seattle Consortium (Fred Hutchinson / Univ of Washington)14. Stiftung biobank-suisse15. University of Alabama – Birmingham16. University of California - Davis17. University of Malmö - Sweden18. University of Rochester19. University of Texas – San Antonio20. University of Texas Southwest Medical Center21. Wake Forest University22. Wayne State University / Karmanos Cancer Institute23. Westmead / Breast Cancer Tissue Bank – Australia
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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
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Automated variable selection and progression calculations
Appendix: Dataset Production Algorithms
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Appendix: Caisis Protocol Manager
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caTISSUESuite
MSKCCDMZ
Catalog
MSKCCNetwork
Appendix: External Interfaces / caBIG
caBIG Grid
JIT Annotation
caBIG
Tracking
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Appendix: Metrics