Use of electronic health data to support a Learning Health System: Lessons from several distributed networks in the US

Jeffrey Brown, PhD

Department of Population Medicine Harvard Medical School and

Harvard Pilgrim Health Care Institute

The Learning Health System in Europe Brussels, Belgium

September 25, 2015

Some distributed networks in US • CDC’s Vaccine Safety Datalink (VSD) • Health Care Systems Research Network • FDA’s post-market safety programs • Meningococcal Vaccine Safety Study • FDA Mini-Sentinel • NIH Health Care Systems Collaboratory • PCORI National Clinical Research Network (PCORnet) • MDPHnet

Some distributed networks in US • CDC’s Vaccine Safety Datalink (VSD) • Health Care Systems Research Network • FDA’s post-market safety programs • Meningococcal Vaccine Safety Study • FDA Mini-Sentinel • NIH Health Care Systems Collaboratory • PCORI National Clinical Research Network (PCORnet) • MDPHnet

Vaccine Safety Datalink • Funded by US Center for Disease Control and

Prevention • Post-marketing safety of vaccines • Integrated delivery systems and health insurers • Data standardization via a common data model • Often rely on chart review • Data are updated frequently (can be weekly) • Work has informed vaccination policy

: Real Time Public Health Surveillance

Cambridge Health Alliance 20 Sites 400,000 patients

Atrius Health 27 Sites 700,000 patients

Mass. League Comm. Health 18 Sites 300,000 patients • Launched November 2012

• Real time access to ambulatory EHR data to inform public health

• Automatically identifies reportable and chronic diseases and enables flexible querying

• Massachusetts Department of Public Health (MDPH) epidemiologists have submitted hundreds of queries

• Now an operational suite of systems supported by MDPH

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www.mini-sentinel.org

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Sentinel History

2008: FDA launched the Sentinel Initiative

2007: FDA Amendments Act • A mandate to create an active surveillance system • Access data from 25 million individuals by July 2010 • Access data from 100 million individuals by July 2012

2009: Mini-Sentinel funded under Sentinel Initiative • 5-year pilot to create active surveillance system

monitoring the safety of FDA-regulated medical products • Develop policies, processes, infrastructure, collaboration

2014: Funding awarded for Sentinel

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Institute for Health

Lead – HPHC Institute

Data and scientific partners

Scientific partners

Collaborators

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Bringing multiple data partners together

Centralized vs. Distributed

Distributed data system is preferred because • Data sits behind data partner’s firewall • Data remains under local control • Only minimally necessary info is shared in a given analysis • Preserve patient privacy & proprietary interests

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Distributed Analysis 1- User creates and submits query (a computer program) 2- Data partners retrieve query 3- Data partners review and run query against their local data 4- Data partners review results 5- Data partners return results via secure network 6 Results are aggregated

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Common Data Model

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Impact / Dissemination

4 FDA drug safety communications • Tri-valent inactivated flu vaccine and febrile seizures

(no increased risk) • Rotarix and intussusception (label change) • Dabigatran and bleeding (no increased risk) • Olmesartan and sprue-like enteropathy (label change)

70 peer-reviewed articles 48 methods reports / white papers Thousands of unique queries and comparisons contributing to

over 140 formal assessments

www.mini-sentinel.org

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Label change

http://www.fda.gov/BiologicsBloodVaccines/SafetyAvailability/ucm356758.htm

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Yih, N Engl J Med. 2014;370:503

15 www.fda.gov/Drugs/DrugSafety/ucm326580.htm; Nov 2, 2012

Drugs

“This assessment […used…] FDA’s Mini-Sentinel pilot...”

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“In the months following the approval of the oral anticoagulant dabigatran ... in October, 2010, the FDA received through the FDA Adverse Event Reporting System many reports of serious and fatal bleeding events associated with use of the drug.”

N Engl J Med 2013. DOI: 10.1056/NEJMp1302834

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Drug Incidence with respect to: Washout Period GIH Events/100k Days at Risk183-Day Washout 2.0365-Day Washout 1.9183-Day Washout 1.6365-Day Washout 1.4183-Day Washout 3.4365-Day Washout 3.7183-Day Washout 3.5365-Day Washout 3.7

Figure 1a. New Events of GIH per 100k Days at Risk in the MSDD between October 19, 2010 and December 31, 2011, by Drug, Incidence Criteria, and Washout Period for Individuals with a Pre-Existing Condition of Atrial Fibrillation

Dabigatran Incident with respect to Dabigatran

Incident with respect to Dabigatran and Warfarin

Warfarin Incident with respect to Warfarin

Incident with respect to Dabigatran and Warfarin

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

183-Day Washout 365-Day Washout 183-Day Washout 365-Day Washout 183-Day Washout 365-Day Washout 183-Day Washout 365-Day Washout

Incident with respect to Dabigatran Incident with respect to Dabigatran and Warfarin

Incident with respect to Warfarin Incident with respect to Dabigatran and Warfarin

Dabigatran Warfarin

New

GIH

Eve

nts

per 1

00k

Days

at

Risk

http://www.mini-sentinel.org/work_products/Assessments/Mini-Sentinel_Modular-Program-Report_%20MSY3_MPR41_Dabigatran-Warfarin-GIH-ICH_Part-1.pdf

Output

Rate of gastrointestinal bleeding per 100k days at risk (8 scenarios)

Dabigatran Warfarin

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Protocol-based assessment – Dabigatran

http://www.mini-sentinel.org/work_products/Assessments/Mini-Sentinel_Protocol-for-Assessment-of-Dabigatran.pdf

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Olmesartan label change: sprue-like enteropathy

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ARBs and celiac disease: all users

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

LOSARTAN IRBESARTAN OLMESARTAN TELMISARTAN VALSARTAN

Case

s per

100

per

son

year

s

Cases 63 10 17 5 50

New users 235,630 40,071 81,560 24,596 153,159

ARBs: New users after >365 day washout; Celiac Disease: 1st dx code after >365 day without diagnosis.

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ARBs and celiac disease: all users

0.0

0.5

1.0

1.5

2.0

2.5

LOSARTAN IRBESARTAN OLMESARTAN TELMISARTAN VALSARTAN

Case

s per

mill

ion

days

at r

isk

All

Cases 213 28 55 10 150

New users

535,045 69,868 171,630 44,770 346,618

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ARBs and celiac disease: 2+ years

0.0

0.5

1.0

1.5

2.0

2.5

LOSARTAN IRBESARTAN OLMESARTAN TELMISARTAN VALSARTAN

Case

s per

mill

ion

days

at r

isk 2+ years

Cases 9 1 5 0 7

New users

25,045 2,721 4,419 1,124 13,925

24 Psaty. N Engl J Med 2014;370:2165

“The Mini-Sentinel ' provides an essential public health service. The current configuration — the data model, the methods development, and the investigative team — represents an impressive achievement..

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Key Contributors to Mini-Sentinel’s Progress Tightly Coupled network Frequent and coordinated interactions between FDA, data

partners, content experts, epidemiologists, and statisticians Clear ownership and goals Established agreements and contracts Distributed data network (no central repository) Public health practice Focused on best understood and useful data for purpose

• First: Claims and administrative data, plus access to full text records – Syntax and semantics are clear and understood

• Then: electronic medical records, registries, … – Much more complex to understand, standardize, and use for research

Rapid cycle development of capabilities

Use of electronic health data to support a Learning Health System: Lessons from several distributed networks in the US

Jeffrey Brown, PhD

Department of Population Medicine Harvard Medical School and

Harvard Pilgrim Health Care Institute

The Learning health System in Europe Brussels, Belgium

September 25, 2015

Thank You

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For More Information

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PopMedNet Website popmednet.org

PopMedNet Wiki popmednet.atlassian.net/wiki

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Thank You

Links www.PopMedNet.org mehi.masstech.org/what-we-do/hie/mdphnet/about www.pcornet.org www.mini-sentinel.org nihcollaboratory.org/Pages/distributed-research-network.aspx

NIH Collaboratory Distributed Research Network Presentations https://www.nihcollaboratory.org/Pages/Grand-Rounds-03-15-13.aspx https://www.nihcollaboratory.org/Pages/Grand-Rounds-09-13-13.aspx https://www.nihcollaboratory.org/Pages/Grand-Rounds-06-13-14.aspx https://www.nihcollaboratory.org/Pages/Grand-Rounds-11-14-14.aspx

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Selected references Vogel J, Brown JS, Land T, Platt R, Klompas K. MDPHnet: Secure, distributed sharing of electronic health record data for public health surveillance,

evaluation, and planning. American Journal of Public Health 2014; 0: e1-e6. Klann, JG, Buck MD, Brown J, Hadley M, Elmore R, Weber G, Murphy S. Query Health: standards-based, cross-platform population health surveillance.

J Am Med Info Assoc 2014; 0:1-7. Toh S, Platt R, Steiner JF, Brown JS. Comparative-effectiveness research in distributed health data networks.Clin Pharmacol Ther 2011; 90(6): 883-7. Behrman RE, Benner JS, Brown JS, McClellan M, Woodcock J, Platt R. Developing the Sentinel System – A national resource for evidence

development. New England Journal Of Medicine February 10, 2011. Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R. Distributed health data networks: A practical and preferred approach to multi-institutional

evaluations of comparative effectiveness, safety, and quality of care. Medical Care 2010; 48 Suppl: S45-S51. Maro JC, Platt R, Holmes JH, Strom BL, Hennessy S, Lazarus R, Brown JS. Design of a national distributed health data network. Ann Intern Med 2009;

151:341-4. Fleurence RL, Curtis LC, Califf RM, Platt R, Selby JV, Brown JS. Launching PCORnet, a national patient-centered clinical research network. J Am Med

Inform Assoc doi:10.1136/amiajnl-2014-002747 [Epub ahead of print] Curtis LC, Brown JS, Platt R. Four Health Data Networks Illustrate The Potential For A Shared National Multipurpose Big-Data Network. Health Affairs,

33, no.7 (2014):1178-1186. Holmes JH, Elliott TE, Brown JS, Raebel MA, Davidson A, Nelson AF, Chung A, La Chance P, and Steiner JH. Data warehouse governance for distributed

research networks in the United States: a systematic review of the literature. J Am Med Inform Assoc. 2014 Mar 28. doi: 10.1136/amiajnl-2013-002370. [Epub ahead of print]

Raebel MA, Haynes K, Woodworth TS, Saylor G, Cavagnaro E, Coughlin KO, Curtis LH, Weiner MG, Archdeacon P, and Brown JS. Electronic Clinical Laboratory Test Results Data Tables: Lessons from Mini-Sentinel. Pharmacoepidemiol Drug Saf. 2014 Feb 18. doi: 10.1002/pds.3580.

Curtis LH, Weiner MG, Boudreau DM, Cooper WO, Daniel GW, Nair VP, Raebel MA, Beaulieu NU, Rosofsky R, Woodworth TS and Brown JS. Design considerations, architecture, and use of the Mini-Sentinel distributed data system. Pharmacoepidemiol Drug Saf. 2012;21(S1): 23–31.

Platt R, Carnahan RM, Brown JS, Chrischilles E, Curtis LH, Hennessy S, Nelson JC, Racoosin JA, Robb M, Schneeweiss S, Toh S and Weiner MG. The U.S. Food and Drug Administration's Mini-Sentinel program: status and direction. Pharmacoepidemiol Drug Saf. 2012;21(S1): 1–8.

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