clinical informatics, cer, and pcor: building blocks for ... informatics cer and pc… · clinical...
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EDM ForumEDM Forum Community
Webinars Events
10-31-2012
Clinical Informatics, CER, and PCOR: BuildingBlocks for Meaningful Use of Big Data in HealthCareMark FrisseVanderbilt University
Adam WilcoxColumbia University
Dean SittigThe University of Texas Health Science Center at Houston
Michael KahnUniversity of Colorado, Denver
Marianne Hamilton LopezAcademyHealth
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Recommended CitationFrisse, Mark; Wilcox, Adam; Sittig, Dean; Kahn, Michael; and Hamilton Lopez, Marianne, "Clinical Informatics, CER, and PCOR:Building Blocks for Meaningful Use of Big Data in Health Care" (2012). Webinars. Paper 6.http://repository.academyhealth.org/webinars/6
Clinical Informatics, CER, and PCOR:
Building Blocks for Meaningful Use of
Big Data in Health Care
Mark Frisse, MD, MBA, Vanderbilt University
Adam Wilcox, PhD, Columbia University
Dean Sittig, PhD, The University of Texas Health
Science Center at Houston
Michael Kahn, MD, PhD, University of Colorado,
Denver
Marianne Hamilton Lopez, MPA, AcademyHealth
October 31, 2012
Welcome
Erin Holve, Ph.D., M.P.H.,
M.P.P.
– Director of Research &
Education, AcademyHealth
– Principal Investigator of the
EDM Forum
AcademyHealth: Improving
Health & Health Care AcademyHealth is a leading national organization serving the fields of health
services and policy research and the professionals who produce and use
this important work.
Together with our members, we offer programs and services that support the
development and use of rigorous, relevant and timely evidence to:
1. Increase the quality, accessibility and value
of health care,
2. Reduce disparities, and
3. Improve health.
A trusted broker of information, AcademyHealth
brings stakeholders together to address the current
and future needs of an evolving health system,
inform health policy, and translate evidence into action.
New Resource from the
EDM Forum
Patient Engagement Framework
– Outlines key opportunities for engagement
– Available for download to your iPad through the iTunes app store
Go to iTunes Preview
Learning Objectives:
Trick or Treat?? Compare six different clinical
informatics technology platforms
used for research (e.g. i2b2 and
CER Hub);
Discuss the tradeoffs inherent to
data collection across a variety of
strategies, including mobile devices;
Explore the current peer-reviewed
literature at the intersection of clinical
informatics and CER;
Describe the desirable
characteristics of data models that
can be used for CER.
Today’s Faculty Mark Frisse, MD, MS, MBA,
Vanderbilt University
Michael Kahn, MD, PhD,
University of Colorado,
Denver
Marianne Hamilton Lopez,
MPA, AcademyHealth
Adam Wilcox, PhD,
Columbia University
Dean Sittig, PhD, The
University of Texas
Health Science Center
at Houston
Building the Informatics
Infrastructure for Comparative
Effectiveness Research (CER):
A Review of the Literature
Marianne Hamilton Lopez, MPA
Senior Manager
AcademyHealth
Understanding the Literature:
Why Now?
Technological advances in clinical informatics
have made large amounts of data accessible
and potentially useful for research.
An influx of new research is likely to result in
new scholarship.
Charting the progress of this emerging
scientific endeavor promises:
– Unique and interesting challenges, and
– New opportunities for discovery.
Literature Review Aims Develop and implement a systematic
search strategy for identifying relevant articles at the intersection of CER & CI
Develop codes to classify the literature
Perform an in-depth literature synthesis/analysis
Review themes in the selected body of work
Identify current gaps
Identifying Articles at the
Intersection of CER & CI
Clinical Informatics
Comparative Effectiveness
Research
A Curated Approach to
Identifying Relevant Articles
PubMed Searches: – MeSH search: clinical
informatics and CER (68)
– KW search: “Learning Healthcare System” (7)
Reference Searches – PROSPECT, DRN, &
Enhanced Registries (1,500)
– HIT for Actionable Knowledge Annotated Bibliography (40)
– 2010 AMIA Symposium (2)
Review of Projects (818): – DARTNet
– DEcIDE
– HMORN
– i2b2
– OMOP
– PhysioMIMI and VISAGE
– RedCAP
– Sentinel/Mini-Sentinel
– SHARP Program
– TRIAD (OSU CTSA)
– VINCI and VA informatics
– iDASH
Exclusions upon Citations Review by
biomedical informatician
N = 2,035
Search Strategy 2
Reference Searches
N = 1,542
Search Strategy 3
Review of Projects
N = 818
Combined Total Citations
N = 2,435
Abstracts Reviewed
N = 400
Exclusions upon Abstracts Review
N = 253
Full-Text Articles Reviewed
N = 147
Articles Meeting Criteria
N = 132
Refining the Set
Search Strategy 1
PubMed Searches
N = 75
Exclusions upon Full-Text Articles Review
N = 15
Coding the Primary Folder of Articles on CER & CI
Context
Platforms, Projects
Clinical Informatics
Platforms
Clinical Informatics
Projects
Natural Language
Processing
Data use and quality
Research Networks
Standardized data collection
Identifiers and De-
Identification
Metadata
Patient Involvement
IRBs
Governance
Library of Phenotypes
The Learning Healthcare
system and CER
Single Point Access
Cloud Computing
Security
Cohort Identification
Three Types of Articles Identified
as Major Areas of Focus
1. Historical context or frameworks for
using clinical informatics for research
2. Platforms, projects, and networks
3. Issues, challenges and applications of
natural language processing (NLP)
Two Cross-Cutting Themes:
1. Standardization: Differences in ontologies,
informatics platforms, and data entry practices
contribute to the complexity of collecting and
analyzing multi-site electronic data for research.
2. Governance: CER based on ECD presents unique
data governance concerns related to:
– Transfer and storage,
– De-identification, and
– Access of ECD.
Three Gaps Identified in the
Literature
1. Cohort Identification
2. Single Point Access
3. Cloud Computing
State of the Literature
Cross-cutting themes reflect a nascent, but
rich, discussion
Efforts continue to expand to develop CI
platforms, models, and tools to support new
infrastructure and studies
Breadth of perspectives in this growing
community of scientists are engaged in
expanding the current paradigm of
effectiveness research
Available Resources and Looking Ahead
www.edm-forum.org/ Products from the literature
review:
A set of PubMed search terms
A list of cross-cutting codes
An abstraction form
An annotated bibliography
A glossary of relevant terms
Next Steps:
Updated peer-reviewed search
Grey literature review
Provide Input:
Submit comments
Sign-up for updates
Join the Discussion Sign up at [email protected]
W E L C O M E
Event or Meeting Title
Informatics platforms enable distributed
comparative effectiveness research using
multi-institutional heterogeneous clinical data
Dean F. Sittig, PhD
Compare and contrast 6 large
informatics platforms for CER 20
Today: most data manipulations performed using non-coordinated applications with disjointed institutional control
New informatics platform designs provide access to electronic clinical data and the governance required for inter-institutional CER
“platform” is a suite of interconnected, coordinated applications, together with the operational environment that hosts those applications
Focus on specific CER projects that provide implementations of informatics platforms and highlight design requirements and solutions
CER requires comprehensive data on
many patients 22
Enormous amounts, large variety of data types
from different sources to create complete medical
history
Need in- & out-patient EHRs (free text); billing,
laboratory, pharmacy, and radiology
Document patients actually receive care ordered;
pharmacy dispensing and patient-reported data
Data is nearly always incomplete; methods must
be appropriate for measuring health status and
care events
CER requires data on populations from
multiple organizations 23
Identify small differences, bias, subgroup
analyses, generalizability, evaluation of
demographic & geographic variation, rare events
Include data from multiple organizations, non-
traditional data sources,
long-term care facilities,
home and public health agencies,
ascertain patients’ socio-economic status
CER requires data on populations from
multiple organizations 24
Need to merge data from the same patient who
has received healthcare services and had clinical
data captured at multiple institutions
Requires a community-wide MPI - identifies
patients based on multiple demographic data
(e.g., first name, last name, date of birth, gender,
social security or telephone numbers)
Only health information exchanges for patient
care have tackled this extraordinarily difficult
problem; will be a critical success factor.
CER requires data extraction, modeling,
aggregation and analysis 25
Design and development of “mapping” apps is
big challenges in multi-institutional research
Difficult for researchers to appreciate local
idiosyncratic data issues without active
engagement of local experts
Informaticians working to create powerful, user-
friendly tools for data extraction, manipulation,
and analysis
Developing tools to process clinically-rich free-text
notes documenting patient care
CER must conform to local IRB rules and
local and federal legislation 26
Social, legal, ethical, and political challenges in
CER must not be underestimated
“organizations are understandably reluctant to
move data beyond their own boundaries absent a
clear and specific need to do so, and patients will
be less likely to consent to allow this to happen.”
One design is to retain physical control of raw
data while providing for their aggregation as
limited data sets to answer specific questions
CER must conform to local IRB rules and
local and federal legislation 27
Other governance solutions:
Standardizing data models across the project
Limiting access to authorized personnel while
facilitating remote access
Restricting types of queries that can be executed and
masking patient-specific, identifiable data
Logging all data transactions and access activities
As rules evolve CER platforms and governance
processes must evolve accordingly
Summary and Conclusion 28
CER transform healthcare identifying therapies,
procedures, preventive tests, and healthcare
processes most effective based on cost, quality,
and safety
State-of-the-art informatics platforms are
necessary to carry out this type of research
6 generic steps in CER: data identification,
extraction, modeling, aggregation, analysis, and
dissemination
Funding was provided by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS019912-01 (Scalable PArtnering
Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries
Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (Colorado Clinical and Translational Sciences Institute).
Data Model Considerations for Clinical
Effectiveness Researchers
Michael G. Kahn1,3,4, Deborah Batson4, Lisa Schilling2
1Department of Pediatrics, University of Colorado, Denver 2Department of Medicine, University of Colorado, Denver
3Colorado Clinical and Translational Sciences Institute 4Department of Clinical Informatics, Children’s Hospital Colorado
Electronic Data Methods (EDM) Forum Clinical Informatics Webinar
31 October2012
Disclosures
Presentation based on EDM Forum commissioned paper:
30 Medical Care 50(9) 2012 S60-S67 DOI: 10.1097/MLR.0b013e318259bff4.
What is a data model & why should I care?
• A data model determines: – What data elements can be stored
– What relationships between data
can be represented
– Technical stuff: data type,
allowed ranges, required versus
optional (missingness)
• You should care because it
determines: – How easy can data be recorded
– How easy can data be extracted
– Contributes to data quality
31
Visit- versus Patient-centric data models
32
Query Complexity: “For each patient, how many
medications where filled over a period of time?”
33
Four-table join
Three-table join
Query Complexity: “Average number of
prescriptions written per visit?”
34
Two-table join
Three-table join +
Date comparisons
Key questions for a data model
• From Jeff Brown regarding FDA Sentinel Initiative*:
1. What does the system need to do?
2. What data are needed to meet
system needs?
3. Where will the data be stored?
4. Where will the data be analyzed?
5. Is a common data model needed,
and if so, what will the model look like?
35 *Brown JS, Lane K, Moore K, Platt R. Defining and evaluating possible database models to implement the FDA Sentinel initiative. U.S. Food and Drug Administration;
May 2009 2009.
Additional Data Model Requirements for SAFTINet
• Create patient-level analytic data sets
• Calculate ages to the year for adults, and to smaller units
of measurement for children
• Calculate prescribed drug intervals (drug exposures)
• Link data across disparate data sources
• Use standardized terminologies to take advantage of
conceptual hierarchies and relationships
• Identify a patient as being part of a defined cohort
• Support limited data sets compliant with HIPAA 36
37
Potential data models considered by SAFTINet
Name Developing entity Initial Purpose
Observational
Medical Outcomes
Project (OMOP)
Foundation of the NIH Comparative Drug Outcomes Studies
Virtual Data
Warehouse (VDW)
HMO Research Network Distributed data warehouse to allow
comparative studies across collaborating
sites: HMORN, CRN, Oregon CTRI
i2b2 Partners Healthcare Informatics framework for clinical and
biological data integration
OpenMRS Regenstrief Institute Open source enterprise medical record
system platform
OpenEHR OpenEHR Foundation Semantically-enabled open source health
computing platform
38
Summary Findings
• None of the existing publicly-available data
models met all requirements
• License-free, flexibility, active community and
willingness to collaborate were key features for
SAFTINet
• Each project has different requirements and
priorities.
• There is no best model for all potential CER uses
39
Funding was provided by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS019912-01 (Scalable PArtnering
Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries
Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (Colorado Clinical and Translational Sciences Institute).
Data Model Considerations for Clinical
Effectiveness Researchers
Questions? [email protected]
Adam Wilcox, PhD
Columbia University
Necessary
Need data to be stored electronically ◦ Transcription is expensive
Could collect electronically …
BUT,
Tools for electronic data collection have been cumbersome ◦ Some studies say they are useful
◦ Still do not outweigh the ease of use of paper
HOWEVER,
Recent developments in consumer electronic devices
How will this influence electronic primary data collection?
Looked at 5 case studies, drawn from the EDM Forum projects
Performed semi-structured interview about primary data collection tasks
Questions about ◦ Workflow
◦ Connectivity
◦ Security
◦ Data integration
Assessed each method in terms of ◦ Ease of use
◦ Development experience needed
◦ Instrument and distribution costs
◦ Instrument flexibility
◦ Speed of data entry
◦ Accuracy
◦ Data loss potential
◦ Need for technical support
◦ Hardware/software requirements
Integrating data from clinical and research centers at 4 academic institutions ◦ Supporting cross-institution analysis
Web-based forms ◦ Occasionally collect with paper, then enter after
Advantages ◦ Data collection validation
◦ Rapid quality assurance
Expanding a state-wide health information exchange network ◦ Indiana Network for Patient Care
Scannable forms and a barcode scanner tool (caTrack) ◦ Data collection for biological samples
◦ Information collected in structured paper forms that are scanned, and linked with a barcode
Advantages ◦ Avoid human error
Patient research registry linking electronic health record data in different delivery sites ◦ Quality improvement using both EHR and registry
data
Qualitative interviews with some questionnaires ◦ Paper
Advantages ◦ Flexible
◦ Portable
Distributed health data network supporting CER and quality improvement for safety-net populations
Patient surveys connected with the patient encounter ◦ Structured forms within the EHR
◦ Similar to web-based form, but links to patient data
Advantages ◦ Data automatically connected to patient
Integrating patient data from multiple sites of care with community surveys
Tablet computers ◦ Some with direct internet connection, others with
data storage on the device and uploading to cloud
Advantages ◦ Portable
◦ Low levels of user training and technical support
◦ Integration of components
Camera
GPS
COMET Indiana PROSPECT Pediatric Enhanced
Registry
SAFTINet WICER
Purpose
Case report forms
during clinical visits
Collecting information on tissue samples that will be linked to
clinical data
Qualitative interviews with patients and family
members
Administering validated instrument as part of a
patient assessment
Survey about health related issues for individuals within a
community
Data collection site Clinical research site; direct patient entery could be done at
clinic or home
Clinical research site Clinical site, research center, or
patient’s home
Clinical site Patient’s home
Data collection
workflow
Collected and entered by research coordinator; pursuing direct
patient data entry
Collected and entered by research coordinator, both for mobile devices and scannable
forms
Collected and entered by research coordinator during qualitative
interviews
Collected and entered by clinical research coordinator as part of
clinical visit
Collected and entered by clinical research coordinator
during interview
Data entry approach Web-based forms
during interview
Scannable paper forms (with attached
barcodes)
Paper forms and
audio recordings
Form template within EHR (similar to web-
based form)
Tablet computer
form template
Category Paper forms (Pediatric Enhanced Registry)
Scannable forms (Indiana PROSPECT)
Web-based forms (COMET)
Form template in EHR (SAFTINet)
Tablet computer (WICER)
Ease of use + + - +
Experience required of designer
+ + -
End user training
+ + - Cost
+ - Flexibility
- + + Speed of entry
- - Accuracy/error rate
- + +
Potential for data loss
- + Need for technical support
+ +
Equipment/software requirements
+ -
Huge breadth in data collection methods used ◦ Not variation off the same themes
◦ Specific to the project needs
Trade-offs of different technologies ◦ Project setting and goals most significant in
defining what technology is used
Need: ◦ Decision tree algorithm
◦ Best practices for each approach
Continue the Discussion!
www.edm-forum.org
Medical Care supplement
Issue Briefs: – Meaningful Engagement
– ARRA Infrastructure Investments
CER Project Profiles
Descriptions of eHealth data initiatives for research & QI
Event archives
Wiki glossary
Upcoming Webinars – December 18th: Tackling
Practical Methodological Challenges of Using Electronic Data for CER & PCOR
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