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The Caisis Project: Integrating Patient Care, Research Systems and Workflows. Paul Fearn, MBA Kinjal Vora, MD Memorial Sloan-Kettering Cancer Center. IAMI 2007 – Kochi, India. Supported by National Cancer Institute grant R01-CA119947. New systems may seem difficult to implement.

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the caisis project integrating patient care research systems and workflows

The Caisis Project: Integrating Patient Care, Research Systems and Workflows

Paul Fearn, MBA

Kinjal Vora, MD

Memorial Sloan-Kettering Cancer Center

IAMI 2007 – Kochi, India

Supported by National Cancer Institute grant R01-CA119947

the easy route may become more difficult to manage as the of cases and the database scope increase
The “easy route” may become more difficult to manage as the # of cases and the database scope increase
consider the total database effort over time
Consider the total database effort over time

Steep initial learning curve

1000 cases

Deceptively simple learning curve

what is caisis
What is Caisis?
  • Primarily a Clinical Data Management System (CDMS)
    • Chronologically organized database of patient stories
    • Easy to query
    • Easy to scale # cases
    • Easy to extend to other diseases
  • EMR, Clinical Trial Management System (CTMS), specimen tracking modules
caisis 4 0 technology architecture
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
which institutions are using caisis over 20 sites 400 users and 200 000 patients
Which institutions are using Caisis?Over 20 sites, 400 users, and 200,000 patients
  • Baylor College of Medicine
  • Cancer Research UK - London
  • Case Western Reserve University
  • City of Hope
  • 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
  • Wake Forest University
  • Washington University – St. Louis
  • Wayne State University / Karmanos Cancer Institute
  • Westmead / Breast Cancer Tissue Bank – Australia
why did we start this effort
Why did we start this effort?
  • Improve data quality
  • Overcome scale limits of Access databases and spreadsheets
  • Increase research productivity
    • Reduce duplication of effort
    • Reduce data manager skill specificity
    • Reduce costs of data collection and maintenance over time
  • Promote innovation and collaboration
what were the research motivations
What were the research motivations?
  • Single institution results not reproducible
    • Need standard or interoperable data models
    • Need transparent data processing algorithms
    • Investigator biases built into systems
  • Cannot do next generation research without inter-institutional collaboration
    • Need large, clean, minimally biased datasets
    • Need open source code for innovation
the caisis project goals
The 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
  • Facilitate collaboration through widespread adoption of an open source system
  • Develop economies of experience, scale and scope
  • Do better science! (reproducible results)
can we make the physician more effective
Can we make the physician more effective?



Fundamental Theorem of Biomedical Informatics

Friedman CP, Wyatt JC, Evaluation Methods in Biomedical Informatics, 2nd ed

without overburdening them
Without overburdening them…

“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

the caisis project timeline
The 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 – (80,000 MSKCC patients)
      • Metadata-driven, dynamic forms, new diseases and eForms
caisis 4 0 privacy and security
Caisis 4.0 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)
longitudinal follow up
Longitudinal follow-up…
  • SSDI batch queries
  • Automation tools
plugins and modules
Plugins and modules
  • Plugin framework
    • PSA Graph
    • File/Image Upload
  • Module framework
    • eForms (EDC/EMR)
    • Protocol manager (CTMS)
    • Specimen tracker
what are the effects of integration
What are the 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
what is the big picture


Specimen Tracking


What is the big picture?




TMA Data

Molecular Data







why are people using it now
Why are people using it now?
  • Sustainable electronic data capture
  • Complete patient story in one place
  • Ease of data/information retrieval
  • Facilitates clinical trials
  • Potential for interoperability and collaborations
  • Available support and expertise
  • Thriving community
  • Less expensive than doing it alone
  • Web-based and cross-browser compatible
msk caisis team 2007
MSK Caisis Team - 2007

Kevin Regan

Avinash Chan

Frank Sculi

Vicki Cameron

Kerry McCarthy

Paul Alli

Beth Roby

Brandon Smith

Jason Fajardo

Not pictured: Tumen Tumurchudar, Kinjal Vora

any questions
Any questions?

Free Software and Open Source Collaboration

  • Demo
  • System requirements
  • Wiki documentation
  • Downloads