The NIH Distributed Research Network New Functionality and Future - - PowerPoint PPT Presentation

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The NIH Distributed Research Network New Functionality and Future - - PowerPoint PPT Presentation

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The NIH Distributed Research Network New Functionality and Future Potential Millions of people. Strong collaborations. Privacy first. Jeffrey Brown, PhD for the NIH Health Care Systems Collaboratory EHR Core Harvard Pilgrim Health Care Institute

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The NIH Distributed Research Network

New Functionality and Future Potential

Jeffrey Brown, PhD for the NIH Health Care Systems Collaboratory EHR Core Harvard Pilgrim Health Care Institute and Harvard Medical School September 13, 2013

Millions of people. Strong collaborations. Privacy first.

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The goal

Facilitate multi‐site research collaborations between investigators and data partners by creating secure networking capabilities and analysis tools for electronic health data

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Vision for the Network: Many types of organizations and data

Health Plan 1 Health Plan 2 Research Dataset 2

NIH Distributed Research Network Secure Portal

Research Dataset 1 CTSA 2

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Registry 2 CTSA 1 Registry 1

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Multiple data sources

4 Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Outpatient clinic 2 Outpatient clinic 4 Outpatient clinic 6 Outpatient clinic 5

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A distributed network links data sources

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FDA Mini‐Sentinel

Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Outpatient clinic 2 Outpatient clinic 4 Outpatient clinic 6 Outpatient clinic 5

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Multiple networks share infrastructure

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FDA Mini‐Sentinel

Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Outpatient clinic 2 Outpatient clinic 4 Outpatient clinic 6 Outpatient clinic 5

NIH Distributed Research Network

  • Each organization can participate in multiple networks
  • Each network controls its governance and coordination
  • Networks share infrastructure, data curation, analytics, lessons,

security, software development

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Not the goal

  • We will not create a

new stand‐alone network with its own research agenda or content experts

  • Investigators will not

have access to data without data partners’ active engagement

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Year 1 progress

  • Created and tested a secure network with

distributed querying capabilities

  • Identified initial data partners
  • Established draft governance document
  • Laid groundwork for querying i2b2 data

repositories

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NIH Distributed Research Network Coordinating Center

Mini‐ Sentinel A Medical Practice 1 Mini‐ Sentinel B Medical Practice 2 Hospital 1 CTSA 1 Hospital 1 Research dataset 1 CTSA 2 Registry 2 Registry 1

Network Management Research Support Query Support Query Tool Development Knowledge Database Software Development Project Management Data Models & Standards Consultation Health System Expertise

NIH DRN Secure Portal

Knowledge Management System Cross project lessons learned, query tracking, meta‐data capture, search functions, etc

Administration Query Tools

SAS, SQL, menu‐driven Modular Programs Summary Tables Feasibility

PROJECTS

LIRE Analytic Tools Other projects Query Interface Reporting Tools Security & Access Control File & Query Repository User Administration Workflow Management Research dataset 2

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Current partners

  • Aetna
  • Group Health Research Institute
  • Harvard Pilgrim Health Care
  • HealthCore
  • Humana
  • Optum

Approximately 40 million current members

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Current data and functionality

  • Routinely updated and quality‐checked data
  • Over 90 million covered lives
  • Complete data capture for defined intervals
  • Inpatient and outpatient encounters, diagnoses,

procedures

  • Outpatient pharmacy dispensings
  • Demographics
  • Mini‐Sentinel common data model
  • Functionality includes
  • Simple queries of pre‐compiled frequencies
  • Standardized queries of person‐level data

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Distributed data / distributed analysis

  • Data partners keep and analyze their own data
  • Standardize the data using a common data model
  • Distribute code to partners for local execution
  • Provide results, not data, to requestor
  • All activities audited and secure

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Use cases

Assess disease burden/outcomes Pragmatic clinical trial design Single study private network

  • Pragmatic clinical trial follow up
  • Reuse of research data

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Use cases

Assess disease burden/outcomes Pragmatic clinical trial design Single study private network

  • Pragmatic clinical trial follow up
  • Reuse of research data

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NIH question

What is the rate of fractures among new bisphosphonate users with a prior diagnosis of

  • steoporosis?

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Query of pre‐compiled counts

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  • Drugs
  • Alendronate sodium
  • Pamidronate disodium
  • Zoledronic acid, Zometa
  • Zoledronic acid, Reclast
  • ICD9‐CM codes for fracture
  • 805xx (vertebral w/o spinal cord injury)
  • 806xx (vertebral with spinal cord injury)
  • 820xx (neck of femur)
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* Incident users based on a 90‐day wash‐out period

20,000 40,000 60,000 80,000 100,000 120,000 2008 2009 2010 2011 2012 0‐21 22‐44 45‐64 65+

Alendronate users by year and age group*

~90,000 in 2012

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200 400 600 800 1,000 1,200 1,400 1,600 1,800 2008 2009 2010 2011 2012 0‐21 22‐44 45‐64 65+

Pamidronate disodium users by year and age group*

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*Prevalent users based on HCPCS J2430

~1,400 in 2012

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Zolendronic acid (Reclast) users by year and age group*

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2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 2008 2009 2010 2011 2012 0‐21 22‐44 45‐64 65+

*Prevalent users based on HCPCS J3488

~20,000 in 2012

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Zolendronic acid (Zometa) users by year and age group*

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1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 2008 2009 2010 2011 2012 0‐21 22‐44 45‐64 65+

*Prevalent users based on HCPCS J3487

~11,000 in 2012

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Hip fracture*

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5,000 10,000 15,000 20,000 25,000 30,000 35,000 2008 2009 2010 2011 2012 0‐21 22‐44 45‐64 65+

*Prevalence

~30,000 in 2012

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Vertebral fracture w/o injury to spinal cord*

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2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 2008 2009 2010 2011 2012 0‐21 22‐44 45‐64 65+

*Prevalence

~22,000 in 2012

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Vertebral fracture with injury to spinal cord*

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200 400 600 800 1,000 1,200 1,400 1,600 1,800 2008 2009 2010 2011 2012 0‐21 22‐44 45‐64 65+

*Prevalence

~2,900 in 2012

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Standardized query of patient‐level data

Validated SAS programs with flexible inputs for exposure, outcome, and other settings

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Key specifications of standardized query

  • Define cohort
  • Define incident user
  • Define incident events
  • Query period
  • Age range
  • Continuous enrollment gap
  • Coverage (medical and drug) requirements

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Specifications for bisphosphonate request

  • Cohort: Members 40+ years old with an osteoporosis

diagnosis and no fractures in the 365 days before new use

  • Incident exposure: New users of ANY of the 4

bisphosphonates based on a 365 day wash‐out period

  • At risk period: 365 days after incident exposure
  • Incident outcome: Observed fracture (hip, vertebral, non‐

hip/non‐vertebral) in any care setting among new users

  • Query period: January 1, 2008 ‐ December 31, 2012
  • Age groups: 40‐54, 55‐64, 65+ years
  • Continuous enrollment gap: 45 days

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Incident users

131,056 365 36,593 2,919

20,000 40,000 60,000 80,000 100,000 120,000 140,000

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Fractures among incident users

1,004 8 345 26 1,792 26 655 76 5,648 31 1,989 159

1,000 2,000 3,000 4,000 5,000 6,000

Hip Fracture Vertebral Fracture Other Fracture

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Fracture rate among incident users (per 100,000 days at risk)*

2.5 7.6 3.1 3.0 4.4 24.6 5.8 8.9 13.9 29.3 17.7 18.6

5 10 15 20 25 30 35

Rate per 100,000 days at risk

Hip Fracture Vertebral Fracture Other Fracture

*Unadjusted

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Caveats

  • Data intended as an example of network capability
  • Standard limitations of electronic health data
  • Use of diagnosis codes to identify osteoporosis and fractures
  • Codes not validated
  • Treatment indication not available
  • Privately insured population with stable enrollment
  • Bisphosphonate usage is complex
  • Different routes of administration
  • Different indications
  • Different patterns of use
  • Rates not adjusted

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Clinical trials and complex observational studies

  • Standardized programs inform development of full

study protocols

  • NIH DRN can support any analysis
  • NIH DRN facilitates creation and use of pooled

analytic datasets

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Use cases

Assess disease burden/outcomes Pragmatic clinical trial design Single study private network

  • Pragmatic clinical trial follow up
  • Reuse of research data

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The NIH Collaboratory’s LIRE project

  • Creating a network among the LIRE sites and its

coordinating center

  • U Washington (Coordinating center)
  • Group Health Cooperative
  • Kaiser Permanente of Northern Cal.
  • Henry Ford Health System
  • Mayo Clinic
  • Coordinating center can distribute programs to

sites securely

  • Sites can return results securely

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Next steps

  • Add most Kaiser Permanente and HMO Research

Network plans

  • Develop new querying and networking

functionality

  • Potential to expand to other data models
  • i2b2 networks
  • ESP networks
  • CTSAs
  • Registries
  • Others

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The DRN is ready for NIH to use

  • Assess disease burden/outcomes
  • Pragmatic clinical trial design
  • Single study private network
  • Pragmatic clinical trial follow up
  • Reuse of research data

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Thank You

For more information

  • nihcollaboratory.org/Pages/distributed‐research‐network.aspx
  • PopMedNet.org
  • info@nihquery.org
  • Jeff_brown@harvardpilgrim.org

Prior Grand Rounds June 28, 2013 https://www.nihcollaboratory.org/Pages/Grand‐Rounds‐06‐28‐13.aspx March 15, 2013 https://www.nihcollaboratory.org/Pages/Grand‐Rounds‐03‐15‐13.aspx

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