Transcript Document

NURSING RESEARCH SEMINAR SERIES
Using CTSA Resources for Big Data Research, Scholarship, and
Teaching
Presented by
Connie White Delaney, PhD, RN, FAAN, FACMI
Bonnie L. Westra, PhD, RN, FAAN, FACMI
Monday, February 2, 2015
Noon to 1:00 pm ♦ 4-130 WDH (Benson Center)
Objectives
•Connect Clinical and Translational Science Award (CTSA) resources
to facilitate your teaching and scholarship
•Explore a powerful emerging nursing and other health data set for
research and teaching
Clinical and Translational Science Institute
http://www.ctsi.umn.edu/
Rank order the top 3 words
Estimate how many times were words repeated in
the CTSA RFA?
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Stakeholder
Innovation
Collaboration
Engage/Engagement
Enterprise
Integrate/Integration
… Answer …
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Stakeholder - 10
Innovation - 40
Collaboration - 14
Engage/Engagement - 43
Enterprise - 9
Integrate/Integration - 35
Intro/Exposure
to Research
Foundational
Training
in Research
Training Award
Preparedness
Career Development
in Research
Pathways to
Independence
Career
Establishment
SUCCESS
The CTSI Research Supported Pipeline
Informatics meeting your needs for data,
resources, and collaborators
Services
Tools
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•
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CTMS
Experts@Minnesota
ResearchMatch
Redcap
Analytical tools
Natural Language Processing
MN Supercomputer Institute
(MSI) Tunnel
In process:
• Genotype/phenotype mapping
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•
•
•
Informatics Consulting
Service
AHC IS
CTSI Portal
Front Door
Data
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AHC IE Clinical Data Repository
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i2b2 cohort-discovery tool
•
MN Death Index
In process:
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Dental EHR
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Imaging; Center for Magnetic
Resonance Research (CMRR);
clinical images
•
UMN Biospecimen Enterprise
Storage initiative & data: Enterprise
storage initiative, BioMedical
Genomics Center
Community
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Greater Plains Collaborative
(PCORI)
Hennepin County Medical
Center (NSF grant)
CTSA Collaborations
Education
Researchers
& Users
• Generalist
• Specialist \Informaticians
• IHI – MHI, MS and PhD
• SON – DNP-NI, PhD-NI
• SPH – MPH-Informatics
• UMII Biomedical Informatics
& Computational Biology
Graduate degrees
Informatics meeting your needs for data,
resources, and collaborators
Services
Tools
•
•
•
•
•
•
CTMS
Experts@Minnesota
ResearchMatch
Redcap
Analytical tools
Natural Language Processing
MN Supercomputer Institute
(MSI) Tunnel
In process:
• Genotype/phenotype mapping
•
•
•
•
Informatics Consulting
Service
AHC IS
CTSI Portal
Front Door
Data
•
AHC IE Clinical Data Repository
•
i2b2 cohort-discovery tool
•
MN Death Index
In process:
•
Dental EHR
•
Imaging; Center for Magnetic
Resonance Research (CMRR);
clinical images
•
UMN Biospecimen Enterprise
Storage initiative & data: Enterprise
storage initiative, BioMedical
Genomics Center
Community
•
•
•
Greater Plains Collaborative
(PCORI)
Hennepin County Medical
Center (NSF grant)
CTSA Collaborations
Education
Researchers
& Users
• Generalist
• Specialist \Informaticians
• IHI – MHI, MS and PhD
• SON – DNP-NI, PhD-NI
• SPH – MPH-Informatics
• UMII Biomedical Informatics
& Computational Biology
Graduate degrees
Our Infrastructure capacity for big data
• Minnesota Super Computer Institute (MSI)
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Access to supercomputers that meet high-performance computing needs for
advanced computation and scientific visualization
• Minnesota Population Center
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Access to U.S. census data back to 1790 for the U.S., as well as data from 75
other countries
Technical expertise to support strong empirical orientation for large-scale data
analysis, geospatial analysis, and policy-relevant research
• Optum Labs partnership
Inter-CTSA collaborations
• Greater Plains Collaborative (10 sites) for
the Patient-Centered Outcomes Research
Institute (PCORI) award
– Leader in applying and sharing LOINC mappings
for Labs
– Developed a common data model for demographic
data
– First site to get PopMedNet client installed and
functioning; the tool allows multiple sites to submit
and receive queries
• NCATS Accrual to Clinical Trials
– NCATS ACT leverages i2b2 across 13 CTSA sites
– Our governance model is driving the ACT model
Inter-CTSA collaborations
• Midwest Area Research Consortium for
Health (MARCH)
– Established multi-site IRB agreement
– MARCH leverages i2b2
• UMN/Mayo CTSA
– UMN is a national leader on extended clinical data
space
– Sharing experience and expertise in SHRINE and i2b2
with Mayo
Our partnerships
• Minnesota Department of Health
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E-Health: Public-private collaborative that aims to accelerate the adoption and
use of health information technology
Death Index: Key researcher resource that offers improved data quality, and is
updated weekly
• National Center for Interprofessional Practice and Education
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Data from the nation’s only coordinating center is part of the Academic Health
Center Information Exchange
Providing support throughout the research tools & process
Front Door
Experts@
Minnesota
ResearchMatch
i2b2 cohort-
REDcap
Clinical Data
Repository
Biospecimen
repository
NLP
discovery tool
Define
question
Analytical
Tools
(JMP, R,
SAS, SPSS)
Standards
Knowledge
representation
Data cleaning
Participants
& logistics
Collect data
Findings
OnCore Clinical Trials Management System
Informatics Consulting Service
Share and
output
Translate
How BMI adds value and meets researchers
needs for data, resources, and collaborators
Services
Tools
•
•
•
•
•
•
CTMS
Experts@Minnesota
ResearchMatch
Redcap
Analytical tools
Natural Language Processing
MN Supercomputer Institute
(MSI) Tunnel
In process:
• Genotype/phenotype mapping
•
•
•
•
Informatics Consulting
Service
AHC IS
CTSI Portal
Front Door
Data
•
AHC IE Clinical Data Repository
•
i2b2 cohort-discovery tool
•
MN Death Index
In process:
•
Dental EHR
•
Imaging; Center for Magnetic
Resonance Research (CMRR);
clinical images
•
UMN Biospecimen Enterprise
Storage initiative & data: Enterprise
storage initiative, BioMedical
Genomics Center
Community
•
•
•
Greater Plains Collaborative
(PCORI)
Hennepin County Medical
Center (NSF grant)
CTSA Collaborations
Education
Researchers
& Users
• Generalist
• Specialist \Informaticians
• IHI – MHI, MS and PhD
• SON – DNP-NI, PhD-NI
• SPH – MPH-Informatics
• UMII Biomedical Informatics
& Computational Biology
Graduate degrees
IMPORTANCE AND AVAILABILITY OF
DATA FOR RESEARCH AND TEACHING
Bonnie L. Westra, PhD, RN, FAAN, FACMI
Use of Clinical Data Sets
• Facilitate cross-study comparison of results
• Enable aggregation of data from multiple
studies / sources – greater statistical power,
detect weaker signals
• Speed study start up by selecting from existing
data
• Improve replication and reproducibility
• Find patients for recruitment into studies
U of Minnesota
AHC Information Exchange (AHC IE)
cwd 2012
University of Minnesota AHC IE Platform
2.3 M Patients
UMN CDR - Rows of data
26,068,675
65,597,327
5.6 Billion lines of
18,478,842
2,263,847
data
46,367,516
439,081,234
8 hospitals and 40+
785,879,618
clinical settings
368,473,934
Reference
Accounts / Coverage
Medications
Procedures and Labs
397,546,666
Diagnosis
88,364,370
Flowsheets
Encounter
Encounter Chart
Patient Chart
Flowsheets
1,939,232,775
Episodes
Notes
1,402,423,830
Patient
Interventions
59,924,418
Flowsheet Example - Falls
Flowsheet Data
• Nursing and interprofessional
– OT, PT, ST, Nutrition, SW
• Collected across settings – varies in use
– ED, Clinic, Hospital, Rehab
– Hospital – ICU, Peds, NICU, OB, Adult (generic)
Initial Framework Flowsheet Data
Example Respiratory Data
Example Skin/ Pressure Ulcers
EXAMPLES RESEARCH QUESTIONS
TEACHING STRATEGIES
AHC-IE Services/ Resources
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Access to data – identified/ deidentified
Linking AHC-IE data to other data sets
De-identification of data
Data storage
Access to data analytic tools
• SAS, SPSS, Stata, R and Rstudio, MatLab, Microsoft Office,
JMP Pro, EpiInfo
Sepsis & Diabetes
• Evaluate whether use of EBP guidelines make a difference in
development of complications
• Discover new interventions which lead to improvement in
outcomes and add to EBP guidelines
• Determine if there are differences in use of EBP guidelines and
outcomes for health disparities
• HCMC Epic data – mapping data based on FHS data in AHC-IE
• Data storage/ analytic tools
• Interprofessional team – Faculty & Students
• Computer Science - Michael Steinbach, Vipin Kumar, Pranjul Yadav,
Andrew Hangsleben, Sanjoy Dey, Katherine Hauwiller, Kevin Schiroo
– School of Nursing - Bonnie L. Westra, Connie W. Delaney, Lisiane
Pruinelli
– Institute for Health Informatics - György J. Simon
Predictive Models for CAUTI
• Jung In Park, PhD-C
• Requesting AHC-IE services
– EHR data from the UMN-TIDE and add in UMMC’s
NDNQI Data
– De-identify MRN after matching CAUTI to
hospitalizations
– Link unit level nurse staff characteristics to
patients with CAUTI i.e. education, experience
– Data storage and use analytic tools
Predictors Liver Transplant Survival
• Lisiane Pruinelli, PhD Student
• Transplant Information System
• Requesting AHC-IE services
– Use of secure workbench - assures data remain
secure
– Potentially de-identify dates (date shifting)
– Access to analytic tools
Unanticipated ICU Admissions
After Surgery
• Jessica Peterson, PhD Student
• Examine anesthesia variables and patient
characteristics that predict unanticipated
admission to ICUs
• AHC-IE Services
– Exploring availability of data from UMN TIDE
– Data storage
– Use of analytic tools in secure workbench
Teaching Preparation of EHR Data for
Research
• Participating in a CTSA pilot project (Lisa
Pulkrabek, DNP Student)
• Assisting with mapping flowsheets to concepts
in a clinical data model
• Learning how to apply national data standards
for comparing data across CTSA sites
Discussion – Your Use of CTSI
Resources
• Potential courses
– Evidence-based practice
– Quality improvement
– Research
– Specialty courses – data projects i.e. gero, psych, etc.
• Your research topic and potential use of data and
other CTSI resources
Find Information on Data Access
z.umn.edu/clinicaldata
Data Set Access
NURSING RESEARCH SEMINAR SERIES
Using CTSA Resources for Big Data Research, Scholarship, and
Teaching
Presented by
Connie White Delaney, PhD, RN, FAAN, FACMI
Bonnie L. Westra, PhD, RN, FAAN, FACMI