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Complexities of Building Multi-institutional Clinical Data Networks
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Chicago Area Patient Centered Outcomes Research Network (CAPriCORN)
Northwestern University
Abel Kho MD, MS
Disclosure
I hold an equity stake in Health Data Link LLC which provides privacy protec;ng record linkage so=ware
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Figure 1. Percentage of office-‐based physicians with EHR systems: United States, 2001–2013
SOURCE: CDC/NCHS, Na;onal Ambulatory Medical Care Survey and Na;onal Ambulatory Medical Care Survey, Electronic Health Records Survey.
Why bother with mul3-‐instu3onal data?
§ BeNer capture of the care received by individuals/popula;on § Increased power to conduct large scale studies § Funding
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Quan3fying “Cross-‐over” pa3ents
Finnell et al, Indianapolis, Emergency Department visits: § 7.6% over one year § 15% over four years
Kho et al, Indianapolis, cross-‐over of known pa;ents with MRSA between hospitals: § 10% over one year
Kho et al. Use of a Regional Health InformaIon Exchange to Detect Crossover of PaIents with MRSA between Urban Hospitals. Journal of the American Medical InformaIcs AssociaIon 2008
Kho AN et al. A regional informa;cs plaXorm for coordinated an;bio;c resistant tracking, aler;ng and preven;on. Clinical Infec;ous Diseases 2014.
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Along these lines, the Office for Civil Rights [1] stated that, to resolve confusion about what constitutes a code and how it relates to PHI, it was providing guidance similar to that from the National Institutes of Standards and Technology [2], which states:
De-identified information can be re-identified (rendered distinguishable) by using a code, algorithm, or pseudonym that is assigned to individual records. The code, algorithm, or pseudonym should not be derived from other related information* about the individual, and the means of re-identification should only be known by authorized parties and not disclosed to anyone without the authority to re-identify records. A common de-identification technique for obscuring PII [Personally Identifiable Information] is to use a one-way cryptographic function, also known as a hash function, on the PII.
*This is not intended to exclude the application of cryptographic hash functions to the information. Thus, codes derived from PHI as part of a de-identified data set may be disclosed if an expert determines that the data meets the de-identification requirements at §164.514(b)(1).
In line with this guidance from NIST, a covered en;ty may disclose codes derived from PHI as part of a de-‐iden;fied data set if an expert determines that the data meets the de-‐iden;fica;on requirements at §164.514(b)(1). The re-‐iden;fica;on provision in §164.514(c) does not preclude the transforma;on of PHI into values derived by cryptographic hash func;ons using the expert determina;on method, provided the keys associated with such func;ons are not disclosed, including to the recipients of the de-‐iden;fied informa;on.
Reduc3on in counts with de-‐duplica3on for a sample of condi3ons
Non Deduplicated Deduplicated Diabetes (Type II only) n=135,779 n=103,177
24.0% reduction
Asthma n=110,640 n=79,563 28.0% reduction
Myocardial Infarction n=6,049 n=5,384 10.9% reduction
Kho AN, Cashy JP, Jackson KL, Pah AR, Goel S, Boehnke J, Humphries JE, Kominers SD, Hota BN, Sims SA, Malin BA, French DD, Walunas TL, Meltzer DO, Kaleba EO, Jones RC, Galanter WL. Design and Implementa;on of a Privacy Protec;ng Electronic Health Record Linkage Tool in Chicago. JAMIA 2015.
NSA SHA-‐512
Data Partner A
Secure Agent
HASH BUNDL E
H #01 FN + DoB + Salt1
H #02
H #03
H #17
FN + SSN+ Salt2
FN + LN+ Salt3
LN + DoB + Salt17
HASH GENERATOR
PHI
NSA SHA-‐512
Data Partner B
Secure Agent
HASH BUNDL E
H #01 FN + DoB + Salt1
H #02
H #03
H #17
FN + SSN+ Salt2
FN + LN+ Salt3
LN + DoB + Salt17
HASH GENERATOR
PHI
Matching Engine
Fourth Party
SALT Generator Third Party
Hash Bundle
Matching Algorithm
Returns Cluster ID
Receives Hash Bundles
Cluster ID Generation Process Via secured private linkage
Slides were prepared with help from Eliel Oliveira, lphi.org
What is CAPriCORN?
• Why CAPriCORN was created
• What will CAPriCORN do
• How it works
• Who is involved
• Where are we
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Funded by PCORI
PaIent Centered Outcome Research InsItute -‐ Authorized by Congress, part of the Affordable Care Act
-‐ Mission is to conduct research to provide informaIon about the best available evidence to help paIents and their health care providers make more informed decisions
-‐ PrioriIes: prevenIon, diagnosis and treatment opIons; improving healthcare system; communicaIon and disseminaIon; addressing dispariIes; acceleraIng paIent-‐centered outcomes research & methodological research
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11 CDRN and 18 PPRN awards approved on December 17, 2013 by PCORI’s Board of Governors
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This map depicts the number of PCORI funded Patient-Powered or Clinical Data Research Networks that have coverage in each state.
13 CDRN and 21 PPRN awards approved on July 21, 2015 by PCORI’s Board of Governors
Who is involved?
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CAPriCORN Partners:Blue Cross Blue Shield of Illinois ◦ Center for Medical Technology Policy ◦ Chicago Asthma Consortium ◦ Chicago
Health IT Regional Extension Center (CHITREC) ◦ Comer Children’s Hospital ◦ Have a Heart for Sickle Cell Anemia Foundation ◦ Illinois Hospital Association ◦ Lurie Children’s Hospital ◦ Next Step/Strive ◦ Office of Health
Information Technology ◦ Respiratory Health Association ◦ Sickle Cell Disease Association of Illinois ◦ The Peggy Lillis Memorial Foundation
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The CAPriCORN team and elected officials kicking off the award. Elected officials: Senator Dick Durbin, Congressman Danny K. Davis, State Senator Antonio Munoz, County Commissioner Robert Steele
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Steering Committee Members Cook County Health and Hospital System • Bill Trick
Hines Veterans Affairs • Brian SchmiN
Jesse Brown Veterans Affairs Medical Center • Wendy Brown
Loyola Medicine • Fran Weaver
NorthShore University HealthSystem • Jonathan Silverstein
Northwestern University • Abel Kho
Rush University Medical Center • Raj Shah
The University of Chicago Medicine • David Meltzer
University of Illinois Hospital and Health Sciences System • Jerry Krishnan
Alliance of Chicago Community Health Services (FQHC) • Fred Rachman
External Research Partner • Tom Concannon
PCAC • Madeleine Shalowitz
CAPriCORN PI (The Chicago Community Trust) • Terry Mazany
CAPriCORN Admin (Illinois Medical District Commission)
• John Collins
CAPriCORN Goals in Phase I (18 mo)
1. Establish procedures for clinical data standardiza;on and inter-‐operability across CDRNs and PPRNs
2. Capture detailed longitudinal informa;on on >1
million pa;ents (~50% non-‐white)
3. Opera;onalize a central IRB 28
Phase I Goals (con;nued) 4. Recruit and characterize 5 cohorts (asthma, anemia,
sickle cell disease, obesity, and recurrent Clostridium difficile)
5. Develop capacity to conduct compara;ve effec;veness research (CER) trials and observa;onal studies
6. Engage pa;ents, clinicians & health system leaders throughout research cycle from idea genera;on to implementa;on
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How?
• Develop a cross-cutting infrastructure for sustainable, population-wide and patient-centered CER in Chicago
• Successfully pool EHR data across all 10 of the CAPriCORN institutions • Develop a central IRB that includes representation from member institutions
and a patient /clinician advisory committee that provides input on research prioritization, topics for study, and local review of HIPAA-related issues
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Data Systems
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Kho AN, Hynes DM, Goel S, et al. Chicago Area Pa;ent Centered Outcomes Research Network (CAPriCORN). JAMIA 2014
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Data captured from healthcare delivery, direct encounter basis
Data captured from processes associated with healthcare delivery
Data captured within multiple contexts: healthcare delivery,
registry activity, or directly from patients
Fundamental basis
PATIDBIRTH_DATEBIRTH_TIMESEXHISPANICRACEBIOBANK_FLAG
DEMOGRAPHIC
PATIDENR_START_DATEENR_END_DATECHARTENR_BASIS
ENROLLMENT
PATIDENCOUNTERIDSITEIDADMIT_DATEADMIT_TIMEDISCHARGE_DATEDISCHARGE_TIMEPROVIDERIDFACILITY_LOCATIONENC_TYPEFACILITYIDDISCHARGE_DISPOSITIONDISCHARGE_STATUSDRGDRG_TYPEADMITTING_SOURCE
ENCOUNTERPATIDENCOUNTERID (optional)MEASURE_DATEMEASURE_TIMEVITAL_SOURCEHTWTDIASTOLICSYSTOLICORIGINAL_BMIBP_POSITION
VITAL
PATIDENCOUNTERIDENC_TYPE (replicated)ADMIT_DATE (replicated)PROVIDERID (replicated)DXDX_TYPEDX_SOURCEPDX
DIAGNOSIS
PATIDENCOUNTERIDENC_TYPE (replicated)ADMIT_DATE (replicated)PROVIDERID (replicated)PX_DATEPXPX_TYPE
PROCEDURE
PATIDRX_DATENDCRX_SUPRX_AMT
DISPENSING
PATIDENCOUNTERID (optional)LAB_NAMESPECIMEN_SOURCELAB_LOINCSTATRESULT_LOCLAB_PXLAB_PX_TYPELAB_ORDER_DATESPECIMEN_DATESPECIMEN_TIMERESULT_DATERESULT_TIMERESULT_QUALRESULT_NUMRESULT_MODIFIERRESULT_UNITNORM_RANGE_LOWMODIFIER_LOWNORM_RANGE_HIGHMODIFIER_HIGHABN_IND
LAB_RESULT
PATIDENCOUNTERID (optional)REPORT_DATERESOLVE_DATECONDITION_STATUSCONDITIONCONDITION_TYPECONDITION_SOURCE
CONDITION
PATIDENCOUNTERID (optional)CM_ITEMCM_LOINCCM_DATECM_TIMECM_RESPONSECM_METHODCM_MODECM_CAT
PRO_CM
CAPriCORN CDM Demographics
Social History Encounters Diagnoses Vitals Labs Microbiology
Medica;ons
Ac;ve Medica;ons
Procedures Cohorts
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Phase I Progress 1. Well func;oning organiza;onal structure 2. Technical Infrastructure -‐ MRAIA as central data-‐hub
PopMedNet installed at (almost) all sites 3. Common Data Model – local site data compliant with
proceses for con;nued revision 4. Cohort Development – defini;ons/algorithms established
(anemia, recurrent C. diff, asthma, sickle cell, body weight)
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Phase I Progress (con;nued)
4. Centralized IRB (ChairB) – ac;ve 5. Pa;ent Clinician Advisory CommiNee (PCAC) – ac;ve
and manual established 6. Communica;ons Working Group – formed 7. Governance Plan, Policies, Procedures – near
complete 8. Data Use agreements/Business Associate Agreements
– IN PROGRESS 36
Current focus of efforts
1. Finalize infrastructure, data models, governance, agreements (DUAs/BAAs)
2. Partnerships with PPRNs, CTSAs and CTOs 3. Demonstrate func;onality 4. Develop Sustainability plan (legal and business)
beyond 2019
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ADAPTABLE trial – A First Test of PCORnet • The first PCORnet dedicated clinical trial • Aspirin Dosing: A Pa;ent-‐centric Trial Assessing Benefits and Long-‐term Effec;veness
• A Trial of how we do Clinical Trials • $14 M – 7 CDRNS
• Compara;ve effec;veness of low dose vs high dose aspirin for secondary preven;on of cardiovascular events
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Data networks and HIPAA
A major goal of the Privacy Rule is to assure that individuals’ health informa;on is properly protected while allowing the flow of health informa;on needed to provide and promote high quality health care and to protect the public's health and well being.
The Rule strikes a balance that permits important uses of informa;on, while protec;ng the privacy of people who seek care and healing.
HIPAA BREACHES
1066 Breaches to date affec;ng 500 or more individuals including several high profile ins;tu;ons:
hNp://www.hhs.gov/ocr/privacy/hipaa/administra;ve/breachno;fica;onrule/breachtool.html
Re-‐iden3fica3on Risks
Greater access to data may increase risk of re-‐iden;fica;on
Examples: ◦ 1990s: Iden;fica;on of the Governor of MassachuseNs from discharge data set and voter registra;on records
◦ 2000s: AOL search queries ◦ 2006: NeXlix movie reviews
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Coordinating Center
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Complexities of Building Multi-institutional Clinical Data Networks
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Chicago Area Patient Centered Outcomes Research Network (CAPriCORN)Northwestern University
Abel Kho MD, [email protected]