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.  

A  Tool  for  Privacy  Protec3ng  Record  Linkage

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.    

0%

5%

10%

15%

20%

25%

1 2 3 4 5 6

Chicago: Percent of Fragmented Care by Number of Years

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

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Chicago Area Patient Centered Outcomes Research Network (CAPriCORN)

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  

PCORnet organizational structure

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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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CAPriCORN Region

Region of 9.5 million residents/EHR data population of over 5 million

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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  

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Org Chart

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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Distributed  data  query  process  with  PopMedNet

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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, MSakho@nm.org