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