Transcript Connie Delaney - School of Nursing

Connie White Delaney, PhD, RN, FAAN, FACMI
School of Nursing Professor & Dean
Academic Health Center
Director, Biomedical Health Informatics (BMHI)
Associate Dir. CTSI-BMI
Nursing Knowledge: Big Data Research
for Transforming Healthcare
Big Data Invitational 2013
Transformed (transforming)
Health Care System
• Challenges of healthcare access, quality,
safety, efficiency, and effectiveness
• Recent financial crisis within the context of
an annual cost of medical errors
• Influx of patients in 2014 of ~32 million
Americans
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Current State of Health Care System
• Health IT & Patient Safety http://www.iom.edu/Reports/2011/Health-ITand-Patient-Safety-Building-Safer-Systems-for-Better-Care.aspx
• Computational Technology for Effective Health Care
http://www.nap.edu/catalog.php?record_id=12572
• Digital Infrastructure for the Learning Health System
http://www.iom.edu/Reports/2011/Digital-Infrastructure-for-a-LearningHealth-System.aspx
• Best Care at Lower Cost: The Path to Continuously Learning Health Care
in America http://www.iom.edu/Reports/2012/Best-Care-at-
Lower-Cost-The-Path-to-Continuously-Learning-Health-Care-inAmerica.aspx
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Vision for the Future Health Care System
Continuously Learning Health System Vision
4
• Generate and apply the best
evidence for the collaborative
health care choices of each
patient and provider;
• Drive the process of new
discovery as a natural
outgrowth of patient care
• Ensure innovation, quality,
safety, and value in health
care.
(Charter of the Institute of Medicine Roundtable
on Value & Science-Driven Health Care)
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HealthIT.gov
Federal Health IT Strategic Plan
2011 - 2015
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5
Clinical and Translational Science Awards (CTSAs)
• Program creates a definable
academic home for clinical
and translational research.
• CTSA institutions work to
transform the local, regional,
and national environment to
increase the efficiency and
speed of clinical and
translational research across
the country
http://www.ncats.nih.gov/research/cts/cts.html;
https://www.ctsacentral.org/
Health & Nursing Knowledge Discovery &
Dissemination
Community
Practice
Bench
Bedside
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• CONNECT WITH CTSI
http://www.ctsi.umn.edu/index.htm
• CONNECT WITH CTSA NURSE SCIENTIST SPECIAL INTEREST
GROUP
https://www.ctsacentral.org/committee/ctsa-nurse-scientist
NINR Strategic Plan:
Innovation
NINR will invest in research that:
• Develops and creatively applies new and existing knowledge to the implementation of health
information technology, including electronic health records
• Expands knowledge and application of health care technologies to facilitate decision support,
self-management, and access to health care
• Uses genetic and genomic technologies to advance knowledge of the “symptome,” including
the biological underpinnings of symptoms associated with chronic illness
• Encourages risk-taking, innovation, re-invention, and creativity, including high-risk/high-return
concepts
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• Develops new technologies and informatics-based solutions that promote health, including
comprehensive high-throughput technologies
Nursing Research
Include interdisciplinary researchers
Build upon the knowledge gained in nursing concept representation to
address genomic and environmental data
Guide the reengineering of nursing practice
Harness new technologies to empower patients and their caregivers for
collaborative knowledge development
Develop user configurable software approaches that support complex data
visualization, analysis, and predictive modeling
Facilitate the development of middle-range nursing informatics theories
Encourage innovative evaluation methodologies that attend to humancomputer interface factors and organizational context
Bakken et al In McDaniel, A. & Delaney, C. (Guest Editors). 2008.
Informatics: Science and Practice. Nursing Outlook, 56(5), 195-279.
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• Nursing informatics agenda for 2008–18 must expand users of interest to:
microscopic
macroscopic
Clinical
Research
Informatics
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Human Health
& Disease
[translational
bioinformatics]
molecular and
cellular processes
tissues &
organs
Consumer
Health
Informatics
UMN Profiles
individual
patients
REDCap (Research
populations
Electronic Data
Capture)
UMN AHC IE Platform
Clinical Data Repository (CDR)
System Partners
1E4
Data sets
with PHI
Ad-hoc
De-ID or
included in
study platform
Fairview Health Svcs
1A
UMP
2A1
Others
SQL
Via VPN Tunnels
Clinical Partners
Individual
Study
Identified High Risk
Masonic Cancer
Center
3B2
PHI Secure
Storage (data
3C1
Identified Low Risk
2B2
Production
Landing
Zone
Data Marts/
Registries
2B5
Internal Systems
Control Zone
2B6
Staging
(data files)
Quality & Clinical Analytics
Operational
Data Store
EDC (REDCap)
2B
Registry web
applications
EMPI/Provider
Master
Consent (RPMS)
2
Research Systems
Payer claims /
CMS
Source-to-Target
Mapping
4A1
ClinicalTrials.gov
2C
4A2
Metadata Management
Subject Area
Model / Data
Dictionary
2D1
al
ity
qu
ta
Da
Data Sources
TIDE
Researchers/
Users
4A5
Terminology
Server
Concept & Data
Mapping
4B
4A3
Public
Metadata
application
2D2
Research Study Platform
NLP Tools
4C
Terminology Management
University
Desktop
Computers
& mobile
devices
4D
Data Integration Tools
fe
e
Natl. Death Index
State Health Dept.
MSI
MSI Network
3
db
ac
k
4
1D
Public Data
2C2
Trusted Independent Data Environment (TIDE)
Data Profiling,
QA, & Validation
SHRINE
4A4
Study/Researcher
Master
Master Data Management
1C
i2b2
web
application
5B
Metadata
views
2C1
Data
Integration Bus
3D2
Metadata Directory
Clinical Data Repository (CDR)
2A
Bio-repository
(caTissue)
UMN i2b2
DB
Operational
Metadata
Data Classification
Filter
CTMS (OnCore)
3D1
De-Identified Low Risk
Reference Zone
2A1
Research
Collaboration
(CTRS Portal,
Profiles) and
Protocol
Compliance
(e-Protocol)
3C3
De-Identified High Risk
2B4
5A
Analytic
applications
School of
Dentistry
1B
5
Analysis
VMWare
Desktops
3C2
extracts for analytics)
2A2
HL7 Interface/
Enterprise
Service Bus
REDCap
web
application
3B1
2B1
Consent
Management
3A
2B3
Lillehei Heart/CV
1
3A
REDCap
DB
3D3
University of Minnesota AHC
Information Exchange Architecture: Vision State
AHC Network Space
University Network
Public Internet
SHRINE
i2b2
i2b2
i2b2
External
Research
Partners
04/18/2013
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1E3
Removed from
University Network
once transferred
Security & Access Rules
1E2
If legacy data
sets with PHI
Data Classification
Filter
1E1
1E1
UMN AHC IE Platform
Clinical Data Repository (CDR)
1E2
If legacy data
sets with PHI
1E3
Removed from
University Network
once transferred
System Partners
1E4
Data sets
with PHI
Ad-hoc
De-ID or
included in
study platform
Fairview Health Svcs
1A
UMP
2A1
Others
SQL
Via VPN Tunnels
Clinical Partners
Consent
Management
2B3
Lillehei Heart/CV
2A2
• Extended Clinical Data
• Other Clinical Partners
• Research Data Marts
School of
Dentistry
2B2
Producti
Landing
Zone
Version 2.1
2B4
Internal Systems
2B5
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HL7 Interface/
Enterprise
Service Bus
Masonic Cancer
Center
1B
2B1
Reference Zone
2A1
Control Z
2B6
Staging
(data files)
CTMS (OnCore)
Operational
Data Store
EDC (REDCap)
2B
Clinical Data Repository (CD
2A
Bio-repository
(caTissue)
2C1
Data
Integration Bus
EMPI/Provider
Master
Consent (RPMS)
2
Research Systems
Payer claims /
CMS
Trusted Independent Data Environment (TID
Data Profiling,
QA, & Validation
Source-to-Target
Mapping
4A1
ClinicalTrials.gov
4A2
Subject Area
Model / Data
Dictionary
4A3
Metadata Management
db
ac
k
4
al
ity
fe
e
Natl. Death Index
State Health Dept.
D
at
a
qu
1D
1
Study/Researc
Master
Master Data Management
1C
Public Data
Operatio
Metada
Data Sources
TIDE
University of Minn
Information Exchange Archi
AHC Network Space
University Ne
Vision for Nursing Data in a
Clinical Data Warehouse
Other Data
Sets
Management
Data
NMMDS
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Clinical Data
NMDS
Continuum of Care
14
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NIH BIG Data Centers
http://www.nih.gov/
news/health/jul201
3/nih-22.htm
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Nursing Minimum Data Set (NMDS)
Werley, HH & Divine, E., & Zorn, C. (1988). Nursing
Minimum Data Set Data Collection Manual.
University of Wisconsin, Milwaukee, WI
Nursing Management Minimum Data Set
(NMMDS)
Huber D, Schumacher L, Delaney C. Nursing management minimum data set
(NMMDS). J Nurs Adm. 1997;27(4):42-48.
• Standardized representation of nursing knowledge
• Integration within broader essential spectrum of clinical care data
into clinical data warehouses
• Engage in big data science for discovery of new knowledge that
enables evidence-based practice.
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Call to action – Roadmap