Big Data, Bias and Analytics – What Can Your EHR Really Tell You? ADAM WILCOX, PHD.
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Transcript of Big Data, Bias and Analytics – What Can Your EHR Really Tell You? ADAM WILCOX, PHD.
Big Data, Bias and Analytics – What Can Your EHR Really Tell You?ADAM WILCOX, PHD
DATAbig
Source:Nature (Feb 13, 2013)
Hype Cycle for Emerging TechnologiesGartner (August 2014)
Outline
Background and Experience
Big Data IntroductionBig Data – Bias Issues
Advancing Big Data
Next Steps and Conclusion
Outline
Background and Experience
Big Data IntroductionBig Data – Bias Issues
Advancing Big Data
Next Steps and Conclusion
Knowledge Representation vs. Knowledge Discovery
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xt
Q U
MLS
ML
UMLS
ML
NLP
Q N
LPru
les
phys
ician
s
text
UMLS
NLP
MachineLearningQueries
Costs/ClinicSalary + training + admin
$92,077
Benefits/Clinic
Productivity (7 MD’s) $99,986
Hospitalizations ↓ * $0
Total (benefits – cost) +$7,909
* Society would save, per clinic, $79,092 in reduced hospitalizations.
Dorr DA, Wilcox AB, et al. The effect of technology-supported, multidisease care management on the mortality and hospitalization of seniors. J Am Geriatr Soc. 2008 Dec;56(12):2195-202.
Effect of Care Management: Outcomes
Increase in CDR View Access
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09 2
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12 2
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0
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6000
Eclipsys MRNs
Tab %
INTE
GRA
TIO
N
SERV
ICES
REPLICATED
Databases
VIRTUAL DATA WAREHOUSE
DATAMARTS
DM
DM
DM
A
B
C
Ad-Hoc Queries–
QuestionsResearch Define
Recurring–
Automated Queries
Management Reports Measure
OLAP–
Analytics
Operational Reports Analyze
Dashboards Point of Care
Reporting Improve
ApplicationsDecisionSupport Control
DATA WAREHOUSE TOOLS
WICER
Improve Use of Information for Learning Health System
• Informed strategy for healthcare transformation
• Measures to support real-time process and quality improvement
• Data and analytics driving research and discovery
Outline
Background and Experience
Big Data IntroductionBig Data – Bias Issues
Advancing Big Data
Next Steps and Conclusion
Raw Clinical
Matched Clinical
Matched Survey
SurveyMatched vs. Matched
Clinical vs. Survey
Age 47.55 52.33 51.12 50.12 0.072 p << .0001
Proportion Female 0.62 0.79 0.78 0.71 0.963 p << .0001
Proportion Hispanic
0.50 0.56 0.94 0.96 p << .0001 p << .0001
Weight kg
75.69 77.16 76.99 75.42 0.851 0.851
Height cm
160.34 158.23 161.31 161.25 p << .0001 p << .0001
BMI 28.10 29.70 28.90 28.20 0.207 0.207
Prevalence of Smoking
0.09 0.08 0.08 0.06 0.944 p << .0001
Systolic 127.23 128.48 127.50 127.68 0.204 0.164
Diastolic 73.07 74.34 79.24 80.95 p << .0001 p << .0001
Prevalence of Diabetes (Survey = self-report, Clinical = >1 Diabetes ICD-9 AND >1 abnormal test)
0.04 0.09 0.22 0.16 p << .0001 p << .0001
Data Collection Methods
Outline
Background and Experience
Big Data IntroductionBig Data – Bias Issues
Advancing Big Data
Next Steps and Conclusion
Data Quality and Assessment
Weiskopf NG, Weng C. Methods and dimensions of data quality assessment: enabling reuse for clinical research. JAMIA 2013
“New” Analytic Methods
• Bootstrapping
• Learning curves and over-fitting
• Hypothesis generation process
t-tests Non-parametric tests (Chi-square)
Bootstrapping
+ Easy + Easy + Robust
+ Powerful + Robust + Powerful
+ Widely implemented + Widely implemented - Less common
- Not appropriate for all data types
- Less powerful - Requires special packages or programming
Big Data Analytic Approaches
• Sub-population analysis
• Investigating surprises– Often more revealing about data quality than
real effects
Outline
Background and Experience
Big Data IntroductionBig Data – Bias Issues
Advancing Big Data
Next Steps and Conclusion
Big Data
• Know the data you need
• Use the data you have
• Get the data you want
• Adapt data to user needs
• Make value accessible
Next Steps to Make it Useful
Minimum Requirements to Provide Value
• Secure database
• Data sources
• Patient-level integration– Master Patient Index*
• Semantic integration– Vocabulary*
• Excellent analysts
Patient Data Integration
Vocabulary and Data Density
Natural Language Processing
Factors Influencing Health
SocioeconomicHealth behaviorsClinical carePhysical envi-ronment
Collecting Patient-Reported Outcomes
• Transcribing
• Patient Portals
• Scanning
• Tablet entry
Patient Reported Information: Tablets vs. Scanned Documents
Scanning Tablets
Institutional
Equipment cost = =Infection risk = =
Security
Theft + -Data loss - +Patient mismatch
- +
Disaster recovery
+ -
Patient Reported Information: Tablets vs. Scanned Documents
Scanning Tablets
Functionality
Office workflow - =Education/training
= =
Data timeliness = +Branching logic - +Extensibility - +
Patient experiencePreference = +Security perception
= -
Goal Task Use User Tool QI Life-cycle
Cost/ Instance Instances Required
Answer a specific
question
Ad hoc query Research Researcher SQL Define + +++++ Defined
request
Observe trends Recurring query
Management reports Manager Reporting
applicationMeasur
e ++ ++++ Available owner
Identify dependencies
Sub-population
analysis
Operational analysis Analyst Analytic tools Analyze +++ +++
Content expert/ analyst
Assist decision making
Dashboard display
Point of care improvement
Clinical team
Registries Improve ++++ ++ Pilot site
Automate processes Application Decision
supportClinician/
RoleEMR
application Control +++++ + Institutional sponsor
Physical Activity