Caisis IAMI 2007

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

New systems may seem difficult to implement: 

New systems may seem difficult to implement

What happens if you just take the easiest route?: 

What happens if you just take the easiest route?

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

Some systems become extremely difficult to scale,…: 

Some systems become extremely difficult to scale,…

Or impossible to sustain! : 

Or impossible to sustain!

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: 

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) The Caisis project goals

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 user interface: 

Caisis 4.0 user interface

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) Caisis 4.0 privacy and security

Algorithms…: 

Algorithms…

Longitudinal follow-up…: 

Longitudinal follow-up… SSDI batch queries Automation tools

Caisis protocol manager / CTMS: 

Caisis protocol manager / CTMS

Plugins and modules: 

Plugins and modules Plugin framework PSA Graph File/Image Upload Module framework eForms (EDC/EMR) Protocol manager (CTMS) Specimen tracker

eForms (EDC-EMR): 

eForms (EDC-EMR)

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?: 

What is the big picture? EDC/EMR AP-LIS CP-LIS Portals/PHRs Surveys/PROs Tumor Registry Medical Record 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 Beth Roby Vicki Cameron Jason Fajardo Avinash Chan Brandon Smith Kevin Regan Paul Alli Frank Sculi Kerry McCarthy Not pictured: Tumen Tumurchudar, Kinjal Vora

Any questions?: 

Any questions? http://caisis.org Free Software and Open Source Collaboration Demo System requirements Wiki documentation Downloads