Post on 20-Aug-2020
TheNIHDistributedResearchNetworkNewFunctionalityandFuturePotential
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.
Thegoal
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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VisionfortheNetwork:Manytypesoforganizationsanddata
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
Multipledatasources
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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 3Health 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
Adistributednetworklinksdatasources
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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 3Health 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
Multiplenetworksshareinfrastructure
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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 3Health 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
Not thegoal
• We will not create a new stand‐alone network with its own research agenda or content experts
• Investigators will nothave access to data without data partners’ active engagement
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Year1progress
• 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 PortalKnowledge Management System
Cross project lessons learned, query tracking, meta‐data capture, search functions, etc
AdministrationQuery ToolsSAS, SQL,
menu‐drivenModular Programs
Summary Tables
Feasibility
PROJECTS
LIRE Analytic ToolsOther projects
Query Interface Reporting Tools
Security & Access ControlFile & Query Repository
User Administration Workflow Management
Research dataset 2 9
Currentpartners
• Aetna• Group Health Research Institute• Harvard Pilgrim Health Care• HealthCore• Humana• Optum
Approximately 40 million current members
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Currentdataandfunctionality
• 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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Distributeddata/distributedanalysis
• 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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Usecases
Assess disease burden/outcomesPragmatic clinical trial designSingle study private network• Pragmatic clinical trial follow up• Reuse of research data
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Usecases
Assess disease burden/outcomesPragmatic clinical trial designSingle study private network• Pragmatic clinical trial follow up• Reuse of research data
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NIHquestion
What is the rate of fractures among new bisphosphonate users with a prior diagnosis of osteoporosis?
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Queryofpre‐compiledcounts
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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
0
20,000
40,000
60,000
80,000
100,000
120,000
2008 2009 2010 2011 20120‐21 22‐44 45‐64 65+
Alendronate usersbyyearandagegroup*
~90,000 in 2012
0
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 disodiumusersbyyearandagegroup*
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*Prevalent users based on HCPCS J2430
~1,400 in 2012
Zolendronic acid(Reclast)usersbyyearandagegroup*
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0
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
Zolendronic acid(Zometa)usersbyyearandagegroup*
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0
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
Hipfracture*
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0
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
Vertebralfracturew/oinjurytospinalcord*
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0
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
Vertebralfracturewithinjurytospinalcord*
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0
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
Standardizedqueryofpatient‐leveldata
Validated SAS programs with flexible inputs for exposure, outcome, and other settings
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Keyspecificationsofstandardizedquery
• Define cohort• Define incident user• Define incident events• Query period• Age range• Continuous enrollment gap • Coverage (medical and drug) requirements
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Specificationsforbisphosphonate 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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Incidentusers
131,056
365
36,593
2,9190
20,000
40,000
60,000
80,000
100,000
120,000
140,000
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Fracturesamongincidentusers
1,004
8345
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1,792
26
655
76
5,648
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1,989
159
0
1,000
2,000
3,000
4,000
5,000
6,000 Hip Fracture
Vertebral Fracture
Other Fracture
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Fracturerateamongincidentusers(per100,000daysatrisk)*
2.5
7.6
3.1 3.04.4
24.6
5.8
8.9
13.9
29.3
17.7 18.6
0
5
10
15
20
25
30
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Rate per 100,000
days a
t risk
Hip Fracture
Vertebral Fracture
Other Fracture
*Unadjusted
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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Clinicaltrialsandcomplexobservationalstudies
• 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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Usecases
Assess disease burden/outcomesPragmatic clinical trial designSingle study private network• Pragmatic clinical trial follow up• Reuse of research data
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TheNIHCollaboratory’s LIREproject
• 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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Nextsteps
• 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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TheDRNisreadyforNIHtouse
• 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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ThankYou
For more information• nihcollaboratory.org/Pages/distributed‐research‐network.aspx• PopMedNet.org• info@nihquery.org• Jeff_brown@harvardpilgrim.org
Prior Grand RoundsJune 28, 2013https://www.nihcollaboratory.org/Pages/Grand‐Rounds‐06‐28‐13.aspx
March 15, 2013https://www.nihcollaboratory.org/Pages/Grand‐Rounds‐03‐15‐13.aspx
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