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Burning platform: overwhelming complexity
Setsoffa
ctsperdecisio
n
1000
10
100
5Human cognitivecapacity
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Burning platform: overwhelming complexity
Setsoffa
ctsperdecisio
n
1000
10
100
5Human cognitivecapacity
2000 20101990 2020
Structural genetics:
e.g. SNPs, haplotypes
Functional genetics:Gene expression
profiles
Proteomics and other
effector molecules
Decisions by clinical
phenotype
(Adapted from) Stead WW. Beyond expert-based practice. IOM (Institute of Medicine). Evidence-based
medicine and the changing nature of health care: 2007 IOM annual meeting summary, (Introduction andOverview, p. 19). Washington, DC: The National Academies Press 2008.
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Todays healthcare non-system
Experts practice by
working around
systems
System
development
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Science Evidence Care
Missed Opportunities, Waste, and Harm
Institute of Medicine. Best Care at Lower
Cost: The Path to Continuously Learning
Health Care in America. Washington, DC:
The National Academies Press, 2013.
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one in which science and
informatics, patient-clinicianpartnerships, incentives, and
culture are aligned to
promote and enable
continuous and real-timeimprovement in both the
effectiveness and efficiency
of care
Institute of Medicine. Best Care at Lower
Cost: The Path to Continuously Learning
Health Care in America. Washington, DC:
The National Academies Press, 2013.
Drivers:
System approach to care
Harness system for
discovery
Link discovery back into
system
Learning healthcare system
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System approach to healthcare
Compassion
Pattern
recognition
Judgment
People
Simplification
Standardization
Process+ Informatics+
Reproducible
performance
Systems=
Memory
dependence
Forcing
function
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System
development
System-supportedpractice
Individualize & act Assess
Plan Order
Workflow Peoples roles
Process
Technology tools
Evidence Research
Guidelines
Practice database
Pick population Risk
Cost
Variability
System approach to healthcare
Stead WW. Beyond expert-based practice. IOM (Institute of Medicine). Evidence-basedmedicine and the changing nature of health care: 2007 IOM annual meeting summary, p. 96.
Washington, DC: The National Academies Press 2008.
Monitor & correctProcess Patient Sentinel events
Process outcomes
Clinical outcomes
Status
Results
Trends
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Ventilator Associated Pneumonia (VAP)Data from all Vanderbilt adult ICUs combined
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Using informatics to make process visibleVentilator management dashboard
10
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Ventilator management bundleData from all Vanderbilt adult ICUs combined
Development
Just-in time Adherence Data Feedback
Design Studio
Educaton
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
5
10
15
20
25
30
35
Jan-06
Feb-06
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Con
currentBundleC
ompliance(%)
VAP
Rate
per1000
VentDays
Monthly VAP Rate Overall VAP Rate Bundle Compliance
Education
Development
Just-in time Adherence Data Feedback
Design Studio
Ventilator Associated Pneumonia (VAP)Data from all Vanderbilt adult ICUs combined
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VAP rates per 1000 ventilator days:All Vanderbilt adult ICUs combined
23.6
20.1
29.3
21.5
17.5 17.9
13.8
10.8
7.5
4.53.3
0
5
10
15
20
25
30
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Ra
te
per1000
Ve
ntDays
Dashboard Implementation:
8/07-12/07
VAP Definition Retired by CDC December 2012
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Central conclusions
Current efforts aimed at nationwide deployment of HCITwill not be sufficient to achieve the vision of 21stcentury
health care, and may even set back the cause
Success will require emphasis on providing cognitive
support.
In the near term, embrace measureable health care
quality improvement as the driving rationale for HCITadoption efforts.
Principles to support change
Record all available data to drive care, process
improvement, and research
Architect information and workflow systems toaccommodate disruptive change
Archive data for subsequent re-interpretation
Seek and develop technologies that clarify the context of
data
1/2009
Healthcare IT expectation gap
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data mining
automation
connectivity decision
support
Root cause: mismatch between
computational technique & scale of problem
Stead WW. Electronic Health Records. In: Rouse WB, Cortese DA, eds. Engineering the system of
healthcare delivery. Tennenbaum Institute Series on Enterprise Systems, Vol. 3. Amsterdam: IOSPress; 2010.
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Example - Electronic Health Record as a platform for research
De-identification
Clinical
notes
Physicianorders
Patient and staff
messaging
Billing
codes
Labs, Radiology, test results
Syntheticderivative
Electronic Health Record
~ 2.2 million records
>1.4 with adequate clinical data
De-identified DNA repository152.95k adult samples
20k pediatric samples
>37.6k with dense genetic data
Discarded blood
samples from
routine testing
VanderbiltBioVU
If eligible,
extract DNA
De-identification
http://www.onlinetelemedicine.com/html/product/sam_images/X-Ray.jpg -
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Clinical Notes(NLP - natural language
processing)
Billing codes
ICD9 & CPT
Medications
ePrescribing
& NLPLabs & test results
NLP
Finding phenotypes in the EHR
True cases
Finding phenotypes in the EHR
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Algorithm Development and Implementation
Identify
phenotype
of interest
Case & control
algorithm
development and
refinement
Manual
review; assess
precision
Deploy
with
BioVU
Genetic
discovery &
replication
95%
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Example of EHR discovery
Step 1 - Find individuals with normalhearts
Normal ECG
time
No heart disease
No Na-blocking drugs
No abnormal K, Ca, Mg
Hypothetical Record
n = 5,272 across
5 eMERGE sites
Step 2 - Find genetic variants associated with QRS duration in normal hearts
QRS duration is a continuous variable
in the normal range
Adjusted for age, sex, BMI
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GWAS of QRS DurationSCN5A/SCN10A
n=5,272
Ritchie et al. Circulation 2013;127:1377-1385
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Step 3 - Find phenotypes associated with the genotype of interest
Pull EHRs of individualsWith SCN10A (rs6795970)
Extract clinical phenotypes
(~ 1,600 & controls)
Construct Phenome-wide association scan
(PheWAS)
Denny et al Bioinformatics 2010; 26:1205-1210
n = 13,617 across5 eMERGE sites
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Step 4In silico trial of what happens in a population with normal hearts
Normal ECG
time
No heart diseaseNo Na-blocking drugs
No abnormal K, Ca, Mg
Hypothetical Record
Myocardial infarction Atrial fibrillation
Follow the n=5272 with initial
normal cardiac phenotype for
development of atrial
fibrillation based on genotype
Ritchie et al. Circulation 2013;127:1377-1385
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Workstream 2: Computation & informatics research
Privacy enhancing
architectures & algorithms
Computation across diverse
data sources
Vision: adding non-biologic determinants of health
Feature extraction &
qualification algorithms
Contributions:
Hypotheses
Feature extraction algorithms
Phenotype signatures
Pattern detection &
prediction algorithms
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Structured/ unstructured
personal, health, health
care & community data
sources
Features to collect or
extract
Determinant
signatures
Determinant specific
decision support
End game vision: executable knowledge to support whole
person population health & health care
Executable knowledgeDiscovery platform
Privacy enhancing,
feature extraction &
pattern detection
algorithms
Curated
data sets
Life & health & healthcare management
apps
Clinical trials &
routine use
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OLD NEWOne integrated set of data Sets of data from multiple sources
Capture data in standardizedterminology
Capture raw signal and annotate withstandard terminology.
Single source of truthCurrent interpretation of multiple
related signals
Seamless transfer among systemsVisualization of the collective output of
relevant systemsClinician uses the computer to update
the record during the patient visit.
Clinician & patient work together with
shared records and information.
The system provides transaction-level
data.The system provides cognitive support.
Work processes are programmed andadapt through non-systematic work
around.
People, process and technology worktogether as a system.
Next generation EHR computational paradigm
Stead WW. Electronic Health Records. In: Rouse WB, Cortese DA, eds. Engineering the systemof healthcare delivery. Tennenbaum Institute Series on Enterprise Systems, Vol. 3. Amsterdam:
IOS Press; 2009.
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