healthcareopensource2

1 Copyright © 2010 Recombinant Data Corp. All rights reserved.

April 4, 2011

Dan “The Dude” Housman Managing Director, Analytical Applications/Open Source Evangelist

State of Open Source in Healthcare Research Informatics

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Comparative Effectiveness Research - 1747

Josef Lind 1747 HMS Salisbury Bay of Biscay 12 scurvy cases

Treatment N Result

Quart of cider daily 2 Some improvement

X 25 drops of (sulfuric acid)

2 No effect

X 6 spoonfuls vinegar 2 No effect

X ½ pint seawater 2 No effect

2 oranges and one lemon

2 (Ran out of fruit after 6 days) – 1 fit for duty, 1 recovered

X Spicy paste plus barley water drink

2 No effect

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1847 Midwives vs. Medical Students (vs. Street births)

Ignaz Semmelweiss Wien Maternity, Vienna 1847

First Clinic: Medical students Second clinic: Midwives

Lower is better

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Extra, Extra - Washing hands can save lives…..

“The thing that kills women with [puerperal fever]…is you doctors that carry deadly microbes from sick women to healthy ones.” Semmelweis, 1849

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Final Results: System protected status quo

1848: Carl Edvard Marius Levy “with due respect for the cleanliness of the Viennese students, it seems improbable that enough infective matter or vapor could be secluded around the fingernails to kill a patient… his opinions are not clear enough and his findings not exact enough to qualify as scientifically founded.”

1849: Employment terminated in Vienna 1850-1860: Handwashing introduced briefly in other sites 1861: Publishes The Etiology, Concept and Prophylaxis of Childbed

Fever 1862: Open letter to critics “irresponsible murderers and

ignoramuses”. 1865: Dies in mental institution (age 47). Treated with castor oil, cold

water, straight jacket, beaten from escape attempts. Cause of death – pyemia infection caused by beatings.

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Reversion and redemption

Louis Pasteur 1862 – germ theory established from flask experiments

Today?

4 million births/yr US

2%=80,000 8%=320,000 15%=600,000

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To Err is Human

At least 44,000 people, and perhaps as many as 98,000 people, die in hospitals each year as a result of medical errors that could have been prevented, according to estimates from two major studies.

Institute Of Medicine 1999

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To Err is Human

Despite finding small improvements at the margins—fewer patients dying from accidental injection of potassium chloride, reduced infections in hospitals due to tightened infection control procedures—it is harder to see the overall, national impact, Leape and Berwick say. "[T]he groundwork for improving safety has been laid in these past five years but progress is frustratingly slow,"

Institute of Medicine Oct. 2005

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To Err is Human

A disturbing report released recently by (AHRQ), found measurable improvement in fewer than half of the 38 patient safety measures examined. Research shows it takes 17 years before evidence-based practices are incorporated into widespread clinical use.

Institute of Medicine May 2009

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This improving Medicine stuff is hard!

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Challenges in healthcare IT and research

•  How can we run ad-hoc “scurvy” comparative effectiveness experiments in growing EHR systems?

•  Evidence must be an open process. Inquiry must be a central part of practice in translational and personalized medicine concepts.

•  Rapid healthcare system changes will harm patients. How will we know?

•  Current expenditures for research are ~$20-30Billion and it results in a pipeline of 30 new drugs per year… many that are similar.

•  Translation of evidence to practice is lacking.

Something needs to change!

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Openness is needed

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Mission

To improve the quality of patient care and efficiency of medical research by delivering a reliable flow of clinical data and innovative software applications for our clients.

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About My Company – Why I’m Here!

Best of breed software/analytics company focused 100% on secondary uses of clinical data

Core Competencies •  Clinical & research data

warehousing •  Reporting and analytics •  Data strategy, governance &

compliance •  Application integration •  Open source software (social

networking, clinical research, caBIG)

Core Values •  Pragmatism •  Trust •  Effective Communication

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Research Study Process

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Clinical research data flow model

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Getting things done with Open Source in Healthcare

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External Open Source Yardstick? Drupal

560,699 people in 228 countries* speaking 182 languages power

Drupal

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WWDBD - What would Dries Butaert do?

There is no quick rich formula: Build a user conference from in 40 people 2005 to 3,000 in 2010. Have many meet-ups. Be patient.

Hurray for growing pains: Funding comes if you are serving the community and they will support you as you grow out of your current capability.

Build an architecture for evolution: Allow external groups to be able to submit.

Provide the right tools: Processes and tools. Replace planning with co-ordination.

Make money but pay with trust: The open source currency is trust.

Leadership trumps management: Make everyone a respected leader and follower

Six Open Source Secrets from Dries…..(summary)

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Some Open Source projects supporting this mission

Core Infrastructure

MySQL Postgres

Data management Infrastructure

Pentaho Kettle

Mirth HL7

Java J2EE

SVN/Hudson/Eclipse Development tools

Applications

i2b2 Cohorts ++

Profiles Research Networking

caTissue Tissue Bank

Shibboleth Single sign-on

Indivo PCHR

SMArt Substitutable Medical Applications

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Scorecard

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Infrastructure Components

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Data Mgmt. Tools: Kettle and Mirth

Kettle: Allows for deployable/sharable “free” ETL (Extract Transform and Load) without dependency on specific data warehouse implementation (Oracle, MSSQL, Informatica, DataStage, etc.). With support options from Pentaho.

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Infrastructure scorecard (IMHO)

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Infrastructure state: Early adoption…

•  Strengths Cost factors highly attractive if alternative not in use Open approach allows sharing within open projects Support contracts/company success aids in adoption Capabilities often equal or better in open tools Trust erosion from commercial vendors Rapid innovation cycles Community engagement in “vertical frameworks” e.g. Mirth

•  Challenges Limited trust in open source at enterprise/CIO level today Commercial databases most common in open projects Embedding (e.g. SSIS) offer integrated non-open approach Frequent infrastructure vendor FUD – “It’ll never work!” Confusion on “why do I pay” for commercial support Splits – Pentaho & Talend shops divide open community Feature gap resistance/limitations

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i2b2 Informatics for Integrating the

Bench and the Bedside

A success story in academic open source… But a work in progress…

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Step 1 Step 2

Step 3 Step 4

Step 5 Step 6

Step 7

Step 8

Step 10

Step 9 SUBMIT

i2b2 research mart

(Limited Data Set)

i2b2 research mart with patient

identifiers

IRB approval

i2b2 queries

IRB protocol request

Re-issue query to data mart

with PHI

Clinical Data

Warehouse (PHI)

Integration ETL

(data extracts and HL7 feeds)

IRB-Approved PHI is retrieved

source data extracted

EMR

Claims

EMPI

Schedule

Etc.

Data Center (Honest Broker and server support)

Data Collection

Operational Use – QR/QI

i2b2: Overview of Research Workflow

One-time Data Use Agreement

Create “Limited Data Set”

(destroy identifiers)

HIP

AA

WA

LL

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i2b2 History

1999 RPDR initially established at Partners

2000-2004 RPDR and tool evolution at Partners

2004 i2b2 grant to “port RPDR” as an open source framework

2006 First beta release of i2b2

2008 Strong adoption

2009 De-facto CTSA standard CRDW

2010 i2b2 renewal

* RPDR (Research Patient Data Registry) was the precursor to i2b2

i2b2 is linked to over $130M in active research projects at Partners, involving 892

data marts, 1867 users, 315 teams, and 12,000 queries per year

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i2b2 Hive

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i2b2 Open Source Scorecard (IMHO)

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Adoption

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Ecosystem for Recombinant (and i2b2)

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Good for business (Service opportunities)

i2b2 workbench and plug-ins

Custom Java extensions

Data quality (cleansing rules/logic)

Data governance/Management

Collaboration with HIT groups

Data extraction systems

Hw/sw/network operations

IRB/HIPAA auditing

Maintenance / application support

Ontology navigator

Interoperability with CTSA sites

CRC Data repository

i2b2 server / security

Data sets/Reports

TCO factors

Benefits

Indemnification/Risk

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The Johnny Appleseed principle…

A land grant required a settler to “set out at least fifty apple or pear trees” as a condition of the deed.

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Install i2b2 properly

Get better scores on

grant proposals

Increase research capacity

Good for business - Grants

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Good for Business (Grants)

Seven of twelve sites were planning a data warehouse (4 already had data warehouses – Columbia, Duke, Rockefeller and Pitt)

GO grants with i2b2 = 5 CTSA Supplementals, Prospect, etc.

Michael Becich CTSA review AMIA 2008

U Mass 2010… 11 (lowest score) funded! U MN 2011… from 45 to under 15!

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i2b2 extensions: Hacktivation

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Top 10 list for i2b2’s open source success

1.  Strong leadership Zak/Shawn/Suzanne (Harvard) 2.  CTSA grant cycles create demand 3.  Community support/dialog – AUG & outreach 4.  NCBC support grant drivers 5.  Software development execution (Shawn Murphy) 6.  Commercial implementation and pharma partnership 7.  Not force a specific ontology/way of working 8.  SHRINE federation spreads/standardizes install needs 9.  Simplicity “cohort” core – like Twitter or Google 10. Proven prior success with RPDR in use

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Gap: Why are there few to no “external commits”?

•  Grant funding/credit incentives: innovation vs. co-ordination •  Don’t expose too soon approach •  Can the community contribute? •  Legacy of PHI/development framework •  TRUST •  Budget allocation •  Maturity

What happens when funded research and development ends?

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Gaps: Many sites are “early/mid-adoption”

Why?

•  Governance challenges: unclear legislation/interpretation

•  Many groups that can say “no” during process

•  Budget cuts/economic impacts on projects

•  Interference from Meaningful Use & ACO initiatives

•  Changing cultures is harder than adding tools

•  Still immature software/incomplete

•  Implementations are complex, require data

•  Trust among parties is still limited

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Lessons learned to date

•  Be patient with academic projects.

•  Building Trust requires demonstrating delivery not talk

•  Just make it work… the rewards come later

•  Avoid splintering projects BUT keep forwards rapidly

•  Academic open source projects naturally oversell themselves which create expectation problems

•  Open source culture in healthcare/academia is unique and evolving (desire to control is embedded)

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Open data + open software

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•  Trial phase •  Subjects •  Researchers •  Resources •  Finance •  Literature •  Clinical trials •  Images

•  Compound •  Biomarker

•  SNPs •  NextGen Seq. •  mRNA •  ELISA

•  Disease •  Indication •  Outcome •  Internal results •  Public results •  Experimental platforms

Clinical Intelligence problem dimensions

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Infrastructure Solution

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Mapping capabilities to processes

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tranSMART consortium

Amazon Cloud

Int’l U-BIOPRED IMI External

Data

tranSMART.org

Consortium Members

Private Access

Pipelines/���

Search

Data Services

Curation Mgmt.

Shared Data

Pharma

J & J

Millennium

Others

Provider

St. Jude

UCSF

CINJ

Public Access

Open Source

Public Users Public

Provider Public

Pharma

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Sage Bionetworks Mission

Sage Bionetworks is a non-profit organization with a vision to create a “commons” where integrative bionetworks are evolved by contributor scientists with a shared vision to accelerate the elimination of human disease

Sagebase.org

Data Repository

Discovery Platform

Building Disease Maps

Commons Pilots

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Lessons learned to date…

•  Pharma is in the science business and will move to open frameworks if provided

•  Strong support is available for a new strategy •  Progressive leadership is critical (e.g. E. Perakslis J&J) •  Openness with clinical research data is an integrated

problem •  Early challenges with commercial conflicts •  Tool/Data adoption will be a challenge •  Be patient…..

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REDCap

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Openness -> Adoption -> Impact

•  Wikipedia vs. printed encyclopedia •  Proprietary or pure open tools struggle for adoption in

AMCs vs. REDCap •  Consortium model

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Why is REDCap successful?

1.  Leadership – Paul Harris & Vanderbilt 2.  Simple core capability 3.  CTSA standards conversion 4.  CTSA support for Vanderbilt/incentives 5.  Engineering execution 6.  Strong community engagement (calls/etc.) 7.  Consortium commits to community development 8.  Rapid implementation (hacktivation)

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REDCap challenges

•  Not open to non-academics •  Highly controlled (e.g. no ports to commercial DBs) •  Difficult to mix models (open and “academic consortium”) •  Success of model discourages open source •  EDC in regulated environments (Pharma?) •  Needs to be paired with other EDC options

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Profiles Research Networking Software

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Social Networks (Facebook for researchers)

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Professional Open Source Model

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The Crowd and the Cloud

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Profiles Score Card

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Lessons learned to date

•  Real pressure to us is on marketing/dissemination •  Lower grants drive deeper commitment to open source •  Academic roadmaps overly optimistic •  Engagement overlaps on “who gets funded” •  Commercial client expectations high for low price points at small scale •  Need processes to match commercial roadmap and open project roadmap

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Westinghouse vs Edison – AC vs DC

1903 Topsy “bad” elephant electrocuted High voltage AC (Edison “reinvents” FUD)

1893 Niagra falls contract rising dominance of AC

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Change is resisted by very smart people

FUD is an IT institution