SlideShare a Scribd company logo
1 of 1
Download to read offline
What	
  is	
  PEDSnet?	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
The	
  prominent	
  considera0ons	
  in	
  building	
  PEDSnet	
  include:	
  	
  
•  lack	
  of	
  EHR	
  data’s	
  orienta0on	
  toward	
  research	
  analy0cs:	
  unstructured	
  and	
  locally	
  
coded	
  data,	
  complex	
  clinical	
  workflows	
  
•  seman0c	
  heterogeneity:	
  diverse	
  source	
  systems	
  (5	
  Epic,	
  2	
  Cerner,	
  1	
  AllScripts)	
  and	
  
vocabularies	
  (ICD9,	
  RxNorm,	
  GPI,	
  CPT-­‐4,	
  etc.)	
  
•  data	
  peculiari0es	
  in	
  pediatrics:	
  procedures	
  conducted	
  before	
  a	
  child	
  is	
  born	
  such	
  as	
  
prenatal	
  surgery	
  or	
  tes0ng	
  
With	
   the	
   ul0mate	
   goal	
   of	
   serving	
   a	
   variety	
   of	
   research	
   projects,	
   a	
   prerequisite	
   in	
  
PEDSnet	
  is	
  to	
  ensure	
  that	
  the	
  network’s	
  data	
  is	
  “high	
  quality.”	
  Major	
  gaps	
  in	
  this	
  area	
  
include	
   the	
   lack	
   of	
   a	
   clear	
   defini0on	
   of	
   “data	
   quality”	
   in	
   CDRNs	
   and	
   the	
   lack	
   of	
  
standardized	
   guidelines	
   for	
   conduc0ng	
   data	
   quality	
   assessments	
   in	
   large-­‐scale	
  
distributed	
  networks.	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
We	
   implement	
   a	
   comprehensive	
   suite	
   of	
   data	
   quality	
   checks,	
   and	
   propose	
   a	
   semi-­‐
automa0c	
   workflow	
   to	
   conduct	
   data	
   quality	
   assessment	
   in	
   PEDSnet,	
   and	
   make	
   the	
  
following	
  contribu0ons:	
  
Data	
  Integra2on	
  and	
  Valida2on	
  in	
  PEDSnet	
  
Fig	
  1	
  PEDSnet	
  Partner	
  Sites	
  (Total	
  8)	
  
1.  L a r g e -­‐ s c a l e	
   d a t a	
   q u a l i t y	
  
assessments	
  in	
  pediatrics	
  
ü  ~4.7M	
   children	
   (nearly	
   5%	
   of	
   US	
  
child	
  popula2on)	
  
ü  Observa2onal	
  data	
  associated	
  with	
  
>97M	
  clinical	
  encounters	
  
Our	
  Contribu2ons	
  
2.	
   Providing	
   a	
   real-­‐world	
   account	
   on	
   data	
  
quality	
  issues	
  in	
  a	
  CDRN	
  
ü  Iden2fied	
  and	
  tracked	
  the	
  causes	
  of	
  ~600	
  
issues	
  	
  
ü  Longitudinal	
   analysis	
   showing	
   trends	
   of	
  
causes	
  and	
  domains	
  
Collabora0ons	
  across	
  mul0ple	
  ins0tu0ons	
  are	
  
essen0al	
  to	
  achieve	
  adequate	
  cohort	
  sizes	
  in	
  
pediatrics	
   research.	
   PEDSnet	
   is	
   a	
   newly	
  
established	
   clinical	
   data	
   research	
   network	
  
(CDRN)	
   that	
   aggregates	
   electronic	
   health	
  
record	
   (EHR)	
   data	
   from	
   eight	
   of	
   the	
   na0on’s	
  
largest	
  children’s	
  hospitals.	
  PEDSnet	
  is	
  part	
  of	
  
a	
   much	
   larger	
   network,	
   PCORnet,	
   supported	
  
by	
   the	
   Pa0ent-­‐Centered	
   Outcomes	
   Research	
  
Ins0tute	
  (PCORI).	
  	
  
	
  
The	
  goal	
  of	
  PEDSnet	
  is	
  to	
  support	
  a	
  variety	
  of	
  
research	
  projects:	
  
•  Compara0ve	
  effec0veness	
  studies	
  	
  
•  Drug	
  safety	
  studies	
  	
  
•  Descrip0ve	
  studies	
  	
  
•  Computable	
  phenotypes	
  
Identifying and Understanding Data Quality Issues in a Pediatric Distributed
Research Network
Ritu Khare1, Levon Utidjian1, Evanette Burrows1, Greg Schulte2, Kevin Murphy1, Sara Deakyne2, Richard Hoyt3,
Nandan Patibandla4, Byron Ruth1, Aaron Browne1, Megan Reynolds3, Keith Marsolo5, Michael Kahn2, Charles Bailey1
1Children’s Hospital of Philadelphia, 2Children’s Hospital Colorado,3Nationwide Children’s Hospital,
4Boston Children’s Hospital, 5Cincinnati Children’s Hospital Medical Center
PEDSnet	
  Data	
  Quality	
  Assessment	
  Workflow	
  
Results	
  
Data	
  Quality	
  
Assessment	
  
Program	
  
Issue	
  Ranking	
  
and	
  
Adjudica2on	
  	
  
S1	
  
Fig	
  2.	
  A	
  typical	
  data	
  cycle	
  in	
  PEDSnet	
  showing	
  communica0on	
  between	
  the	
  Data	
  Coordina0ng	
  Center	
  and	
  the	
  Partner	
  Sites	
  
Key	
  Findings	
  &	
  Conclusions	
  
Even	
  a`er	
  the	
  4th	
  data	
  cycle,	
  
on	
  an	
  average	
  74	
  issues	
  
iden0fied	
  per	
  site.	
  	
  
Prospec0ve	
  data	
  quality	
  
assurance	
  is	
  important	
  but	
  
not	
  sufficient	
  for	
  valida0ng	
  
data	
  quality	
  in	
  CDRNs.	
  
The	
  percentage	
  of	
  ETL	
  issues	
  
decreased	
  across	
  successive	
  
data	
  cycles.	
  
	
  
A	
  strong	
  post	
  hoc	
  data	
  
quality	
  assessment	
  program	
  
is	
  necessary	
  to	
  ensure	
  data	
  
quality	
  in	
  CDRNs.	
  
The	
  total	
  number	
  of	
  
provenance	
  issues	
  increased	
  
across	
  cycles.	
  
This	
  study	
  serves	
  as	
  a	
  data	
  
educa0on	
  pladorm	
  where	
  
the	
  experts	
  learn	
  and	
  evolve	
  
the	
  data	
  quality	
  program	
  
with	
  each	
  itera0on.	
  	
  
Related	
  Publica2on	
  
Iden2fying	
  and	
  Understanding	
  Data	
  Quality	
  Issues	
  in	
  a	
  Pediatric	
  Distributed	
  Research	
  Network	
  
Ritu	
  Khare;	
  Levon	
  U1djian;	
  Gregory	
  Schulte;	
  Keith	
  Marsolo;	
  Charles	
  Bailey	
  
AMIA	
  Annual	
  Symposium	
  2015,	
  November	
  14-­‐18	
  2015,	
  San	
  Francisco,	
  CA	
  
Future	
  Direc2ons	
  
Implement	
  advanced	
  data	
  quality	
  checks,	
  e.g.	
  
comparison	
  with	
  external	
  datasets	
  and	
  published	
  
results	
  
Use	
  the	
  data	
  quality	
  results	
  to	
  determine	
  the	
  analy0c	
  
fitness	
  of	
  the	
  PEDSnet	
  dataset	
  with	
  respect	
  to	
  a	
  given	
  
target	
  research	
  study	
  
PEDSnet	
  Sites	
  (at	
  various	
  loca1ons)	
  
1.  Shared	
  private	
  repository	
  for	
  
extract-­‐transform-­‐load	
  (ETL)	
  
scripts	
  
2.  ETL	
  conven0ons	
  document	
  	
  
3.  Weekly	
  troubleshoo0ng	
  mee0ngs	
  	
  
Acknowledgments	
  
This	
  work	
  was	
  supported	
  by	
  PCORI	
  Contract	
  CDRN-­‐1306-­‐01556.	
  	
  
The	
   inves0gators	
   would	
   like	
   to	
   thank	
   the	
   PEDSnet	
   informa0cs	
   team	
   for	
   their	
  
efforts	
  ,	
  and	
  the	
  pa0ents	
  and	
  families	
  contribu0ng	
  to	
  the	
  PEDSnet	
  dataset.	
  	
  
Most	
  Frequent	
  Issues	
  
Missing	
  Data	
   31%	
  
En0ty	
  Outliers	
   15%	
  
Unexpected	
  difference	
  
from	
  previous	
  ETL	
  run	
  
9%	
  
Implausible	
  Dates	
   9%	
  
Temporal	
  Outliers	
   6%	
  
Unexpected	
  Facts	
   4%	
  
Unexpected	
  concept	
  
iden0fiers	
  
3%	
  
Numerical	
  Outliers	
   3%	
  
u ETL:	
  due	
  to	
  an	
  ETL	
  programming	
  error	
  at	
  the	
  site	
  largely	
  due	
  
to	
  incompleteness	
  or	
  ambiguity	
  in	
  the	
  conven0ons	
  document	
  
u Provenance:	
  due	
  to	
  the	
  nature	
  of	
  EHR	
  data,	
  e.g.	
  data	
  entry	
  
error,	
  clinical	
  &	
  administra0ve	
  workflows,	
  true	
  anomaly,	
  etc.	
  
u I2b2	
  transform:	
  due	
  to	
  limita0ons	
  of	
  i2b2-­‐>PEDSnet	
  
transforma0on	
  scripts	
  
u Non-­‐issue:	
  false	
  alert	
  by	
  the	
  data	
  quality	
  assessment	
  program	
  
Longitudinal	
  Analysis	
  of	
  Issues	
  Summary	
  of	
  Data	
  Quality	
  Issues	
  
Contact	
  Person	
  
Ritu	
  Khare,	
  PhD	
  (kharer@email.chop.edu)	
  
PEDSnet	
  Data	
  Coordina2ng	
  Center	
  	
  Prospec2ve	
  Data	
  Quality	
  Assurance	
  
Aggregated	
  
(OMOP	
  CDM)	
  
S2	
   S3	
   S4	
  
S5	
   S6	
   S7	
   S8	
  
Types	
  of	
  Data	
  Quality	
  Checks	
  implemented	
  in	
  the	
  Program	
  
Fidelity	
   the	
  degree	
  to	
  which	
  PEDSnet	
  data	
  accurately	
  reflect	
  
the	
  source	
  systems	
  from	
  which	
  it	
  is	
  drawn	
  	
  
e.g.	
  the	
  distribu0ons	
  of	
  gender	
  values	
  do	
  not	
  match	
  between	
  
the	
  source	
  system	
  (EHR)	
  and	
  the	
  derived	
  PEDSnet	
  dataset.	
  	
  
Consistency	
  	
  
	
  
the	
  degree	
  to	
  which	
  a	
  specific	
  type	
  of	
  informa0on	
  is	
  
recorded	
  in	
  the	
  same	
  way	
  in	
  the	
  different	
  data	
  sources	
  
contribu0ng	
  to	
  PEDSnet	
  
Value	
  set	
  viola0ons	
  (e.g.	
  incorrect	
  concept	
  iden0fier	
  used	
  to	
  
represent	
  no	
  informa0on	
  in	
  race	
  field)	
  or	
  mapping	
  issues	
  (e.g.	
  a	
  
site	
  using	
  internal	
  codes	
  to	
  capture	
  drug	
  informa0on	
  cannot	
  
completely	
  map	
  to	
  standard	
  RxNorm	
  codes)	
  
Accuracy	
   the	
  degree	
  to	
  which	
  PEDSnet	
  data	
  correctly	
  reflect	
  the	
  
clinical	
  characteris0cs	
  of	
  pa0ents	
  	
  
e.g.	
  a	
  site	
  having	
  significantly	
  larger	
  ra0o	
  for	
  average	
  number	
  of	
  
facts	
  (observa0ons,	
  	
  procedures,	
  or	
  drug	
  exposures)	
  per	
  pa0ent	
  	
  
Feasibility	
   the	
  degree	
  to	
  which	
  a	
  given	
  type	
  of	
  informa0on	
  is	
  
actually	
  collected	
  and	
  available	
  in	
  PEDSnet	
  	
  
e.g.	
  the	
  recorded	
  gesta0onal	
  age	
  is	
  missing	
  for	
  70%	
  of	
  pa0ents	
  
Expert	
  Review	
  
Communica0on	
  of	
  issues	
  to	
  the	
  origina0ng	
  sites	
  
At	
  the	
  end	
  of	
  the	
  4th	
  data	
  cycle,	
  the	
  data	
  quality	
  assessment	
  
program	
  helped	
  iden0fy	
  591	
  data	
  quality	
  issues.	
  
Domains	
  with	
  Most	
  Issues	
  
Drug	
  Exposure	
   21%	
  
Measurement	
   13%	
  
Observa0on	
   12%	
  
Condi0on	
  Occurrence	
   11%	
  
Procedure	
  Occurrence	
  	
   10%	
  
Person	
   8%	
  
Visit	
  Occurrence	
   7%	
  
0	
  
10	
  
20	
  
30	
  
40	
  
50	
  
60	
  
Jan	
  '15	
  
(OMOP	
  v4)	
  
Mar	
  '15	
  
(OMOP	
  v4)	
  
May	
  '15	
  
(OMOP	
  v5)	
  
Jul	
  '15	
  
(OMOP	
  v5)	
  
Percentage	
  of	
  Issues	
  
Data	
  Cycles	
  
Distribu2on	
  of	
  Issue	
  Causes	
  across	
  Data	
  Cycles	
  
PCORnet	
  	
  
CDM	
  
Final	
  Target	
  

More Related Content

What's hot

Data Mining and Big Data Analytics in Pharma
Data Mining and Big Data Analytics in Pharma Data Mining and Big Data Analytics in Pharma
Data Mining and Big Data Analytics in Pharma Ankur Khanna
 
Pistoia Alliance USA Conference 2016
Pistoia Alliance USA Conference 2016Pistoia Alliance USA Conference 2016
Pistoia Alliance USA Conference 2016Pistoia Alliance
 
Olivia Velez - Requirements Analysis for an mHealth Application with Midwive...
Olivia Velez -  Requirements Analysis for an mHealth Application with Midwive...Olivia Velez -  Requirements Analysis for an mHealth Application with Midwive...
Olivia Velez - Requirements Analysis for an mHealth Application with Midwive...Johns Hopkins
 
ML & AI in pharma: an overview
ML & AI in pharma: an overviewML & AI in pharma: an overview
ML & AI in pharma: an overviewPaul Agapow
 
Pistoia Alliance-Elsevier Datathon
Pistoia Alliance-Elsevier DatathonPistoia Alliance-Elsevier Datathon
Pistoia Alliance-Elsevier DatathonPistoia Alliance
 
Methods for the design
Methods for the designMethods for the design
Methods for the designLe Van Dat
 
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...Barry Smith
 
HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...
HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...
HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...REBRATSoficial
 
eSource: What You Need To Know
eSource: What You Need To KnoweSource: What You Need To Know
eSource: What You Need To Knowwww.datatrak.com
 
Research in the time of Covid: Surveying impacts on Early Career Researchers
Research in the time of Covid: Surveying impacts on Early Career ResearchersResearch in the time of Covid: Surveying impacts on Early Career Researchers
Research in the time of Covid: Surveying impacts on Early Career ResearchersRebecca Grant
 
Do Open data badges influence author behaviour? A case study at Springer Nature
Do Open data badges influence author behaviour? A case study at Springer NatureDo Open data badges influence author behaviour? A case study at Springer Nature
Do Open data badges influence author behaviour? A case study at Springer NatureRebecca Grant
 
Strategies for success - volunteer recruitment & prevention of over-volunteer...
Strategies for success - volunteer recruitment & prevention of over-volunteer...Strategies for success - volunteer recruitment & prevention of over-volunteer...
Strategies for success - volunteer recruitment & prevention of over-volunteer...Steffan Stringer
 
Efficient Data Reviews and Quality in Clinical Trials - Kelci Miclaus
Efficient Data Reviews and Quality in Clinical Trials - Kelci MiclausEfficient Data Reviews and Quality in Clinical Trials - Kelci Miclaus
Efficient Data Reviews and Quality in Clinical Trials - Kelci MiclausQuanticate
 
eSource: Data Capture Simplified - Uncover Time and Cost Saving Possibilities
eSource: Data Capture Simplified - Uncover Time and Cost Saving PossibilitieseSource: Data Capture Simplified - Uncover Time and Cost Saving Possibilities
eSource: Data Capture Simplified - Uncover Time and Cost Saving Possibilitieswww.datatrak.com
 
Big Data in Biomedicine: Where is the NIH Headed
Big Data in Biomedicine: Where is the NIH HeadedBig Data in Biomedicine: Where is the NIH Headed
Big Data in Biomedicine: Where is the NIH HeadedPhilip Bourne
 

What's hot (20)

TRI's DIA 2015 Presentation, Therapeutic KRIs: Digestive Disease
TRI's DIA 2015 Presentation, Therapeutic KRIs:  Digestive DiseaseTRI's DIA 2015 Presentation, Therapeutic KRIs:  Digestive Disease
TRI's DIA 2015 Presentation, Therapeutic KRIs: Digestive Disease
 
Data Mining and Big Data Analytics in Pharma
Data Mining and Big Data Analytics in Pharma Data Mining and Big Data Analytics in Pharma
Data Mining and Big Data Analytics in Pharma
 
Pistoia Alliance USA Conference 2016
Pistoia Alliance USA Conference 2016Pistoia Alliance USA Conference 2016
Pistoia Alliance USA Conference 2016
 
Olivia Velez - Requirements Analysis for an mHealth Application with Midwive...
Olivia Velez -  Requirements Analysis for an mHealth Application with Midwive...Olivia Velez -  Requirements Analysis for an mHealth Application with Midwive...
Olivia Velez - Requirements Analysis for an mHealth Application with Midwive...
 
ML & AI in pharma: an overview
ML & AI in pharma: an overviewML & AI in pharma: an overview
ML & AI in pharma: an overview
 
Pistoia Alliance-Elsevier Datathon
Pistoia Alliance-Elsevier DatathonPistoia Alliance-Elsevier Datathon
Pistoia Alliance-Elsevier Datathon
 
Methods for the design
Methods for the designMethods for the design
Methods for the design
 
Clean Clinical Trial
Clean Clinical Trial Clean Clinical Trial
Clean Clinical Trial
 
AGENDA CBI RBM 2019 | RISK-BASED TRIAL MANAGEMENT and MONITORING
AGENDA CBI RBM 2019 | RISK-BASED TRIAL MANAGEMENT and MONITORINGAGENDA CBI RBM 2019 | RISK-BASED TRIAL MANAGEMENT and MONITORING
AGENDA CBI RBM 2019 | RISK-BASED TRIAL MANAGEMENT and MONITORING
 
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...
 
HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...
HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...
HTAi 2015 - A Tool to Monitor and Evaluate a HTA Network: The Case of REBRATS...
 
eSource: What You Need To Know
eSource: What You Need To KnoweSource: What You Need To Know
eSource: What You Need To Know
 
PCORnet: Building Evidence through Innovation and Collaboration
PCORnet: Building Evidence through Innovation and CollaborationPCORnet: Building Evidence through Innovation and Collaboration
PCORnet: Building Evidence through Innovation and Collaboration
 
Research in the time of Covid: Surveying impacts on Early Career Researchers
Research in the time of Covid: Surveying impacts on Early Career ResearchersResearch in the time of Covid: Surveying impacts on Early Career Researchers
Research in the time of Covid: Surveying impacts on Early Career Researchers
 
Do Open data badges influence author behaviour? A case study at Springer Nature
Do Open data badges influence author behaviour? A case study at Springer NatureDo Open data badges influence author behaviour? A case study at Springer Nature
Do Open data badges influence author behaviour? A case study at Springer Nature
 
Strategies for success - volunteer recruitment & prevention of over-volunteer...
Strategies for success - volunteer recruitment & prevention of over-volunteer...Strategies for success - volunteer recruitment & prevention of over-volunteer...
Strategies for success - volunteer recruitment & prevention of over-volunteer...
 
Efficient Data Reviews and Quality in Clinical Trials - Kelci Miclaus
Efficient Data Reviews and Quality in Clinical Trials - Kelci MiclausEfficient Data Reviews and Quality in Clinical Trials - Kelci Miclaus
Efficient Data Reviews and Quality in Clinical Trials - Kelci Miclaus
 
eSource: Data Capture Simplified - Uncover Time and Cost Saving Possibilities
eSource: Data Capture Simplified - Uncover Time and Cost Saving PossibilitieseSource: Data Capture Simplified - Uncover Time and Cost Saving Possibilities
eSource: Data Capture Simplified - Uncover Time and Cost Saving Possibilities
 
CDM
CDMCDM
CDM
 
Big Data in Biomedicine: Where is the NIH Headed
Big Data in Biomedicine: Where is the NIH HeadedBig Data in Biomedicine: Where is the NIH Headed
Big Data in Biomedicine: Where is the NIH Headed
 

Similar to PEDSnet DQA CHOP Symposium

Lecture 9C
Lecture 9CLecture 9C
Lecture 9CCMDLMS
 
Clinical Healthcare Data Analytics
Clinical Healthcare Data AnalyticsClinical Healthcare Data Analytics
Clinical Healthcare Data Analyticsdansouk
 
Who needs fast data? - Journal for Clinical Studies
Who needs fast data? - Journal for Clinical Studies Who needs fast data? - Journal for Clinical Studies
Who needs fast data? - Journal for Clinical Studies KCR
 
Applying FAIR principles to linked datasets: Opportunities and Challenges
Applying FAIR principles to linked datasets: Opportunities and ChallengesApplying FAIR principles to linked datasets: Opportunities and Challenges
Applying FAIR principles to linked datasets: Opportunities and ChallengesARDC
 
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
 
Understanding Gaps between Data Quality Checks and Research Capabilities in a...
Understanding Gaps between Data Quality Checks and Research Capabilities in a...Understanding Gaps between Data Quality Checks and Research Capabilities in a...
Understanding Gaps between Data Quality Checks and Research Capabilities in a...The Children's Hospital of Philadelphia
 
The Role of Data Lakes in Healthcare
The Role of Data Lakes in HealthcareThe Role of Data Lakes in Healthcare
The Role of Data Lakes in HealthcarePerficient, Inc.
 
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...Target Health, Inc.
 
eMagzine Spring 2016 (Trusha Hake)
eMagzine  Spring 2016 (Trusha Hake)eMagzine  Spring 2016 (Trusha Hake)
eMagzine Spring 2016 (Trusha Hake)Trusha Hake
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
 
Developing Protocols & Procedures for CT Data Integrity
Developing Protocols & Procedures for CT Data Integrity Developing Protocols & Procedures for CT Data Integrity
Developing Protocols & Procedures for CT Data Integrity Bhaswat Chakraborty
 
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSINGMETA DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSINGIJCSEIT Journal
 
2022-06-07 Berman Lew Great Plains Conference FINAL.pptx
2022-06-07 Berman Lew Great Plains Conference FINAL.pptx2022-06-07 Berman Lew Great Plains Conference FINAL.pptx
2022-06-07 Berman Lew Great Plains Conference FINAL.pptxLew Berman
 
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...MEASURE Evaluation
 
CDM_Process_Overview_Katalyst HLS
CDM_Process_Overview_Katalyst HLSCDM_Process_Overview_Katalyst HLS
CDM_Process_Overview_Katalyst HLSKatalyst HLS
 
ePCR Research Paper for Western Journal for EM
ePCR Research Paper for Western Journal for EMePCR Research Paper for Western Journal for EM
ePCR Research Paper for Western Journal for EMlarry_johnson
 
Retina Today (Nov-Dec 2014): The Clinical Data Management Process
Retina Today (Nov-Dec 2014): The Clinical Data Management ProcessRetina Today (Nov-Dec 2014): The Clinical Data Management Process
Retina Today (Nov-Dec 2014): The Clinical Data Management ProcessStatistics & Data Corporation
 

Similar to PEDSnet DQA CHOP Symposium (20)

10th Annual Utah's Health Services Research Conference - Data Quality in Mult...
10th Annual Utah's Health Services Research Conference - Data Quality in Mult...10th Annual Utah's Health Services Research Conference - Data Quality in Mult...
10th Annual Utah's Health Services Research Conference - Data Quality in Mult...
 
Lecture 9C
Lecture 9CLecture 9C
Lecture 9C
 
National Workshop to Advance Use of Electronic Data
National Workshop to Advance Use of Electronic DataNational Workshop to Advance Use of Electronic Data
National Workshop to Advance Use of Electronic Data
 
PEDSnet : 18 month summary on data integration and data quality
PEDSnet : 18 month summary on data integration and data qualityPEDSnet : 18 month summary on data integration and data quality
PEDSnet : 18 month summary on data integration and data quality
 
Clinical Healthcare Data Analytics
Clinical Healthcare Data AnalyticsClinical Healthcare Data Analytics
Clinical Healthcare Data Analytics
 
Who needs fast data? - Journal for Clinical Studies
Who needs fast data? - Journal for Clinical Studies Who needs fast data? - Journal for Clinical Studies
Who needs fast data? - Journal for Clinical Studies
 
Applying FAIR principles to linked datasets: Opportunities and Challenges
Applying FAIR principles to linked datasets: Opportunities and ChallengesApplying FAIR principles to linked datasets: Opportunities and Challenges
Applying FAIR principles to linked datasets: Opportunities and Challenges
 
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...
 
Understanding Gaps between Data Quality Checks and Research Capabilities in a...
Understanding Gaps between Data Quality Checks and Research Capabilities in a...Understanding Gaps between Data Quality Checks and Research Capabilities in a...
Understanding Gaps between Data Quality Checks and Research Capabilities in a...
 
The Role of Data Lakes in Healthcare
The Role of Data Lakes in HealthcareThe Role of Data Lakes in Healthcare
The Role of Data Lakes in Healthcare
 
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...
 
eMagzine Spring 2016 (Trusha Hake)
eMagzine  Spring 2016 (Trusha Hake)eMagzine  Spring 2016 (Trusha Hake)
eMagzine Spring 2016 (Trusha Hake)
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health System
 
Developing Protocols & Procedures for CT Data Integrity
Developing Protocols & Procedures for CT Data Integrity Developing Protocols & Procedures for CT Data Integrity
Developing Protocols & Procedures for CT Data Integrity
 
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSINGMETA DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING
 
2022-06-07 Berman Lew Great Plains Conference FINAL.pptx
2022-06-07 Berman Lew Great Plains Conference FINAL.pptx2022-06-07 Berman Lew Great Plains Conference FINAL.pptx
2022-06-07 Berman Lew Great Plains Conference FINAL.pptx
 
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...
Tracking Mother-Infant Pairs across the Cascade of the Prevention of Mother-t...
 
CDM_Process_Overview_Katalyst HLS
CDM_Process_Overview_Katalyst HLSCDM_Process_Overview_Katalyst HLS
CDM_Process_Overview_Katalyst HLS
 
ePCR Research Paper for Western Journal for EM
ePCR Research Paper for Western Journal for EMePCR Research Paper for Western Journal for EM
ePCR Research Paper for Western Journal for EM
 
Retina Today (Nov-Dec 2014): The Clinical Data Management Process
Retina Today (Nov-Dec 2014): The Clinical Data Management ProcessRetina Today (Nov-Dec 2014): The Clinical Data Management Process
Retina Today (Nov-Dec 2014): The Clinical Data Management Process
 

Recently uploaded

Role of Herbs in Cosmetics in Cosmetic Science.
Role of Herbs in Cosmetics in Cosmetic Science.Role of Herbs in Cosmetics in Cosmetic Science.
Role of Herbs in Cosmetics in Cosmetic Science.ShwetaHattimare
 
Pests of Redgram_Identification, Binomics_Dr.UPR
Pests of Redgram_Identification, Binomics_Dr.UPRPests of Redgram_Identification, Binomics_Dr.UPR
Pests of Redgram_Identification, Binomics_Dr.UPRPirithiRaju
 
Applied Biochemistry feedback_M Ahwad 2023.docx
Applied Biochemistry feedback_M Ahwad 2023.docxApplied Biochemistry feedback_M Ahwad 2023.docx
Applied Biochemistry feedback_M Ahwad 2023.docxmarwaahmad357
 
IB Biology New syllabus B3.2 Transport.pptx
IB Biology New syllabus B3.2 Transport.pptxIB Biology New syllabus B3.2 Transport.pptx
IB Biology New syllabus B3.2 Transport.pptxUalikhanKalkhojayev1
 
Basic Concepts in Pharmacology in molecular .pptx
Basic Concepts in Pharmacology in molecular  .pptxBasic Concepts in Pharmacology in molecular  .pptx
Basic Concepts in Pharmacology in molecular .pptxVijayaKumarR28
 
Pests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdf
Pests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdfPests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdf
Pests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdfPirithiRaju
 
Human brain.. It's parts and function.
Human brain.. It's parts and function. Human brain.. It's parts and function.
Human brain.. It's parts and function. MUKTA MANJARI SAHOO
 
M.Pharm - Question Bank - Drug Delivery Systems
M.Pharm - Question Bank - Drug Delivery SystemsM.Pharm - Question Bank - Drug Delivery Systems
M.Pharm - Question Bank - Drug Delivery SystemsSumathi Arumugam
 
Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...
Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...
Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...Sérgio Sacani
 
biosynthesis of the cell wall and antibiotics
biosynthesis of the cell wall and antibioticsbiosynthesis of the cell wall and antibiotics
biosynthesis of the cell wall and antibioticsSafaFallah
 
Alternative system of medicine herbal drug technology syllabus
Alternative system of medicine herbal drug technology syllabusAlternative system of medicine herbal drug technology syllabus
Alternative system of medicine herbal drug technology syllabusPradnya Wadekar
 
Minimal Residual Disease in leukaemia andhematological malignancies
Minimal Residual Disease in leukaemia andhematological malignanciesMinimal Residual Disease in leukaemia andhematological malignancies
Minimal Residual Disease in leukaemia andhematological malignanciessadiya97
 
Role of herbs in hair care Amla and heena.pptx
Role of herbs in hair care  Amla and  heena.pptxRole of herbs in hair care  Amla and  heena.pptx
Role of herbs in hair care Amla and heena.pptxVaishnaviAware
 
THE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptx
THE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptxTHE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptx
THE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptxAkinrotimiOluwadunsi
 
Lehninger_Chapter 17_Fatty acid Oxid.ppt
Lehninger_Chapter 17_Fatty acid Oxid.pptLehninger_Chapter 17_Fatty acid Oxid.ppt
Lehninger_Chapter 17_Fatty acid Oxid.pptSachin Teotia
 
3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...
3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...
3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...PirithiRaju
 
SUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdf
SUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdfSUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdf
SUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdfsantiagojoderickdoma
 
Physics Serway Jewett 6th edition for Scientists and Engineers
Physics Serway Jewett 6th edition for Scientists and EngineersPhysics Serway Jewett 6th edition for Scientists and Engineers
Physics Serway Jewett 6th edition for Scientists and EngineersAndreaLucarelli
 
Principles & Formulation of Hair Care Products
Principles & Formulation of Hair Care  ProductsPrinciples & Formulation of Hair Care  Products
Principles & Formulation of Hair Care Productspurwaborkar@gmail.com
 

Recently uploaded (20)

Role of Herbs in Cosmetics in Cosmetic Science.
Role of Herbs in Cosmetics in Cosmetic Science.Role of Herbs in Cosmetics in Cosmetic Science.
Role of Herbs in Cosmetics in Cosmetic Science.
 
Pests of Redgram_Identification, Binomics_Dr.UPR
Pests of Redgram_Identification, Binomics_Dr.UPRPests of Redgram_Identification, Binomics_Dr.UPR
Pests of Redgram_Identification, Binomics_Dr.UPR
 
Applied Biochemistry feedback_M Ahwad 2023.docx
Applied Biochemistry feedback_M Ahwad 2023.docxApplied Biochemistry feedback_M Ahwad 2023.docx
Applied Biochemistry feedback_M Ahwad 2023.docx
 
IB Biology New syllabus B3.2 Transport.pptx
IB Biology New syllabus B3.2 Transport.pptxIB Biology New syllabus B3.2 Transport.pptx
IB Biology New syllabus B3.2 Transport.pptx
 
Basic Concepts in Pharmacology in molecular .pptx
Basic Concepts in Pharmacology in molecular  .pptxBasic Concepts in Pharmacology in molecular  .pptx
Basic Concepts in Pharmacology in molecular .pptx
 
Pests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdf
Pests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdfPests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdf
Pests of wheat_Identification, Bionomics, Damage symptoms, IPM_Dr.UPR.pdf
 
Human brain.. It's parts and function.
Human brain.. It's parts and function. Human brain.. It's parts and function.
Human brain.. It's parts and function.
 
M.Pharm - Question Bank - Drug Delivery Systems
M.Pharm - Question Bank - Drug Delivery SystemsM.Pharm - Question Bank - Drug Delivery Systems
M.Pharm - Question Bank - Drug Delivery Systems
 
Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...
Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...
Legacy Analysis of Dark Matter Annihilation from the Milky Way Dwarf Spheroid...
 
biosynthesis of the cell wall and antibiotics
biosynthesis of the cell wall and antibioticsbiosynthesis of the cell wall and antibiotics
biosynthesis of the cell wall and antibiotics
 
Alternative system of medicine herbal drug technology syllabus
Alternative system of medicine herbal drug technology syllabusAlternative system of medicine herbal drug technology syllabus
Alternative system of medicine herbal drug technology syllabus
 
Cheminformatics tools supporting dissemination of data associated with US EPA...
Cheminformatics tools supporting dissemination of data associated with US EPA...Cheminformatics tools supporting dissemination of data associated with US EPA...
Cheminformatics tools supporting dissemination of data associated with US EPA...
 
Minimal Residual Disease in leukaemia andhematological malignancies
Minimal Residual Disease in leukaemia andhematological malignanciesMinimal Residual Disease in leukaemia andhematological malignancies
Minimal Residual Disease in leukaemia andhematological malignancies
 
Role of herbs in hair care Amla and heena.pptx
Role of herbs in hair care  Amla and  heena.pptxRole of herbs in hair care  Amla and  heena.pptx
Role of herbs in hair care Amla and heena.pptx
 
THE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptx
THE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptxTHE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptx
THE HISTOLOGY OF THE CARDIOVASCULAR SYSTEM 2024.pptx
 
Lehninger_Chapter 17_Fatty acid Oxid.ppt
Lehninger_Chapter 17_Fatty acid Oxid.pptLehninger_Chapter 17_Fatty acid Oxid.ppt
Lehninger_Chapter 17_Fatty acid Oxid.ppt
 
3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...
3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...
3.2 Pests of Sorghum_Identification, Symptoms and nature of damage, Binomics,...
 
SUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdf
SUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdfSUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdf
SUKDANAN DIAGNOSTIC TEST IN PHYSICAL SCIENCE ANSWER KEYY.pdf
 
Physics Serway Jewett 6th edition for Scientists and Engineers
Physics Serway Jewett 6th edition for Scientists and EngineersPhysics Serway Jewett 6th edition for Scientists and Engineers
Physics Serway Jewett 6th edition for Scientists and Engineers
 
Principles & Formulation of Hair Care Products
Principles & Formulation of Hair Care  ProductsPrinciples & Formulation of Hair Care  Products
Principles & Formulation of Hair Care Products
 

PEDSnet DQA CHOP Symposium

  • 1. What  is  PEDSnet?                 The  prominent  considera0ons  in  building  PEDSnet  include:     •  lack  of  EHR  data’s  orienta0on  toward  research  analy0cs:  unstructured  and  locally   coded  data,  complex  clinical  workflows   •  seman0c  heterogeneity:  diverse  source  systems  (5  Epic,  2  Cerner,  1  AllScripts)  and   vocabularies  (ICD9,  RxNorm,  GPI,  CPT-­‐4,  etc.)   •  data  peculiari0es  in  pediatrics:  procedures  conducted  before  a  child  is  born  such  as   prenatal  surgery  or  tes0ng   With   the   ul0mate   goal   of   serving   a   variety   of   research   projects,   a   prerequisite   in   PEDSnet  is  to  ensure  that  the  network’s  data  is  “high  quality.”  Major  gaps  in  this  area   include   the   lack   of   a   clear   defini0on   of   “data   quality”   in   CDRNs   and   the   lack   of   standardized   guidelines   for   conduc0ng   data   quality   assessments   in   large-­‐scale   distributed  networks.                 We   implement   a   comprehensive   suite   of   data   quality   checks,   and   propose   a   semi-­‐ automa0c   workflow   to   conduct   data   quality   assessment   in   PEDSnet,   and   make   the   following  contribu0ons:   Data  Integra2on  and  Valida2on  in  PEDSnet   Fig  1  PEDSnet  Partner  Sites  (Total  8)   1.  L a r g e -­‐ s c a l e   d a t a   q u a l i t y   assessments  in  pediatrics   ü  ~4.7M   children   (nearly   5%   of   US   child  popula2on)   ü  Observa2onal  data  associated  with   >97M  clinical  encounters   Our  Contribu2ons   2.   Providing   a   real-­‐world   account   on   data   quality  issues  in  a  CDRN   ü  Iden2fied  and  tracked  the  causes  of  ~600   issues     ü  Longitudinal   analysis   showing   trends   of   causes  and  domains   Collabora0ons  across  mul0ple  ins0tu0ons  are   essen0al  to  achieve  adequate  cohort  sizes  in   pediatrics   research.   PEDSnet   is   a   newly   established   clinical   data   research   network   (CDRN)   that   aggregates   electronic   health   record   (EHR)   data   from   eight   of   the   na0on’s   largest  children’s  hospitals.  PEDSnet  is  part  of   a   much   larger   network,   PCORnet,   supported   by   the   Pa0ent-­‐Centered   Outcomes   Research   Ins0tute  (PCORI).       The  goal  of  PEDSnet  is  to  support  a  variety  of   research  projects:   •  Compara0ve  effec0veness  studies     •  Drug  safety  studies     •  Descrip0ve  studies     •  Computable  phenotypes   Identifying and Understanding Data Quality Issues in a Pediatric Distributed Research Network Ritu Khare1, Levon Utidjian1, Evanette Burrows1, Greg Schulte2, Kevin Murphy1, Sara Deakyne2, Richard Hoyt3, Nandan Patibandla4, Byron Ruth1, Aaron Browne1, Megan Reynolds3, Keith Marsolo5, Michael Kahn2, Charles Bailey1 1Children’s Hospital of Philadelphia, 2Children’s Hospital Colorado,3Nationwide Children’s Hospital, 4Boston Children’s Hospital, 5Cincinnati Children’s Hospital Medical Center PEDSnet  Data  Quality  Assessment  Workflow   Results   Data  Quality   Assessment   Program   Issue  Ranking   and   Adjudica2on     S1   Fig  2.  A  typical  data  cycle  in  PEDSnet  showing  communica0on  between  the  Data  Coordina0ng  Center  and  the  Partner  Sites   Key  Findings  &  Conclusions   Even  a`er  the  4th  data  cycle,   on  an  average  74  issues   iden0fied  per  site.     Prospec0ve  data  quality   assurance  is  important  but   not  sufficient  for  valida0ng   data  quality  in  CDRNs.   The  percentage  of  ETL  issues   decreased  across  successive   data  cycles.     A  strong  post  hoc  data   quality  assessment  program   is  necessary  to  ensure  data   quality  in  CDRNs.   The  total  number  of   provenance  issues  increased   across  cycles.   This  study  serves  as  a  data   educa0on  pladorm  where   the  experts  learn  and  evolve   the  data  quality  program   with  each  itera0on.     Related  Publica2on   Iden2fying  and  Understanding  Data  Quality  Issues  in  a  Pediatric  Distributed  Research  Network   Ritu  Khare;  Levon  U1djian;  Gregory  Schulte;  Keith  Marsolo;  Charles  Bailey   AMIA  Annual  Symposium  2015,  November  14-­‐18  2015,  San  Francisco,  CA   Future  Direc2ons   Implement  advanced  data  quality  checks,  e.g.   comparison  with  external  datasets  and  published   results   Use  the  data  quality  results  to  determine  the  analy0c   fitness  of  the  PEDSnet  dataset  with  respect  to  a  given   target  research  study   PEDSnet  Sites  (at  various  loca1ons)   1.  Shared  private  repository  for   extract-­‐transform-­‐load  (ETL)   scripts   2.  ETL  conven0ons  document     3.  Weekly  troubleshoo0ng  mee0ngs     Acknowledgments   This  work  was  supported  by  PCORI  Contract  CDRN-­‐1306-­‐01556.     The   inves0gators   would   like   to   thank   the   PEDSnet   informa0cs   team   for   their   efforts  ,  and  the  pa0ents  and  families  contribu0ng  to  the  PEDSnet  dataset.     Most  Frequent  Issues   Missing  Data   31%   En0ty  Outliers   15%   Unexpected  difference   from  previous  ETL  run   9%   Implausible  Dates   9%   Temporal  Outliers   6%   Unexpected  Facts   4%   Unexpected  concept   iden0fiers   3%   Numerical  Outliers   3%   u ETL:  due  to  an  ETL  programming  error  at  the  site  largely  due   to  incompleteness  or  ambiguity  in  the  conven0ons  document   u Provenance:  due  to  the  nature  of  EHR  data,  e.g.  data  entry   error,  clinical  &  administra0ve  workflows,  true  anomaly,  etc.   u I2b2  transform:  due  to  limita0ons  of  i2b2-­‐>PEDSnet   transforma0on  scripts   u Non-­‐issue:  false  alert  by  the  data  quality  assessment  program   Longitudinal  Analysis  of  Issues  Summary  of  Data  Quality  Issues   Contact  Person   Ritu  Khare,  PhD  (kharer@email.chop.edu)   PEDSnet  Data  Coordina2ng  Center    Prospec2ve  Data  Quality  Assurance   Aggregated   (OMOP  CDM)   S2   S3   S4   S5   S6   S7   S8   Types  of  Data  Quality  Checks  implemented  in  the  Program   Fidelity   the  degree  to  which  PEDSnet  data  accurately  reflect   the  source  systems  from  which  it  is  drawn     e.g.  the  distribu0ons  of  gender  values  do  not  match  between   the  source  system  (EHR)  and  the  derived  PEDSnet  dataset.     Consistency       the  degree  to  which  a  specific  type  of  informa0on  is   recorded  in  the  same  way  in  the  different  data  sources   contribu0ng  to  PEDSnet   Value  set  viola0ons  (e.g.  incorrect  concept  iden0fier  used  to   represent  no  informa0on  in  race  field)  or  mapping  issues  (e.g.  a   site  using  internal  codes  to  capture  drug  informa0on  cannot   completely  map  to  standard  RxNorm  codes)   Accuracy   the  degree  to  which  PEDSnet  data  correctly  reflect  the   clinical  characteris0cs  of  pa0ents     e.g.  a  site  having  significantly  larger  ra0o  for  average  number  of   facts  (observa0ons,    procedures,  or  drug  exposures)  per  pa0ent     Feasibility   the  degree  to  which  a  given  type  of  informa0on  is   actually  collected  and  available  in  PEDSnet     e.g.  the  recorded  gesta0onal  age  is  missing  for  70%  of  pa0ents   Expert  Review   Communica0on  of  issues  to  the  origina0ng  sites   At  the  end  of  the  4th  data  cycle,  the  data  quality  assessment   program  helped  iden0fy  591  data  quality  issues.   Domains  with  Most  Issues   Drug  Exposure   21%   Measurement   13%   Observa0on   12%   Condi0on  Occurrence   11%   Procedure  Occurrence     10%   Person   8%   Visit  Occurrence   7%   0   10   20   30   40   50   60   Jan  '15   (OMOP  v4)   Mar  '15   (OMOP  v4)   May  '15   (OMOP  v5)   Jul  '15   (OMOP  v5)   Percentage  of  Issues   Data  Cycles   Distribu2on  of  Issue  Causes  across  Data  Cycles   PCORnet     CDM   Final  Target