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Leveraging Electronic Health
Records for Predictive Modeling
Marianne Huebner
Hospital electronic health records
Sources Size [MB] Data types
Cohort 1.21 TS, C, N
Registration 1.07 C,N
Allergies 1.8 TS, N, T
Labs 312 TS, N, T
Pharmacy 174 TS, T, N
Clinical Notes 3.7 TS, T, N
Nurses Flow sheet 830 TS, T, C,N
Orders 405 TS, T
etc
TS= time stamp,
C=categorical,
N=numeric,
T=text
Admission
Discharge Events:
readmission,
death,…
PACU
… Events:
reoperation,
complications
…
ORLab
Models for different time intervals
Tables of 80%
random sample
of Mayo patients:
•Chart events
•Cohorts
•OP Notes
•Allergies
•Cllnical notes
•Flowsheets
•Labs
•MAR
•OP notes
•NLP annotations
evaluated data
Matched
complications
info from NSQIP and
Gold Standard
No match to
complications
info
(6,740)
Joined
surgeries with op
notes (proc. group,
diag. group, proc.
mode)
Added rules
engine features
No match to
rules engine
data
(78)
Surgeries w/
matched
complications info
(1,273)
80% random
sample of
surgeries
(8,013)
Completed
dataset
(1,082)
Pre-surgery
model dataset
(1,082)
30 Days Post
Surgery
model dataset
(898)
Defined
surgeries
removed
outliers
variable
transformation
imputed data
No match to
NLP
annotations
(106)
Surgery
Completion
model dataset
(1,004)
Added NLP
annotations
Machine learning algorithms
Enhanced recovery pathway
Mobile applications
• Identify potential problems
(pain, diet management,
complications)
• Clinician effort
– Current medical records:
30 mi (95% navigation,
619 mouse clicks)
– New Tool: 4 min (25
mouse clicks)
Objective
• Develop accessible and accurate guidance in the design and
analysis of observational studies.
70 statisticians from 15 countries
Topic groups: Missing data, Initial data analysis, Variable
selection, Measurement error and misclassification, Study
design, Evaluating prediction models, Causal inference, Survival
analysis, High dimensional data analysis
STRATOS Initiative (STRengthen Analytical
Thinking in Observational Studies)
Intended for applied statisticians and other data analysts with varying levels of
statistical education, experience, and interests.

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Leveraging Electronic Health Records for Predictive Modeling

  • 1. Leveraging Electronic Health Records for Predictive Modeling Marianne Huebner
  • 2. Hospital electronic health records Sources Size [MB] Data types Cohort 1.21 TS, C, N Registration 1.07 C,N Allergies 1.8 TS, N, T Labs 312 TS, N, T Pharmacy 174 TS, T, N Clinical Notes 3.7 TS, T, N Nurses Flow sheet 830 TS, T, C,N Orders 405 TS, T etc TS= time stamp, C=categorical, N=numeric, T=text Admission Discharge Events: readmission, death,… PACU … Events: reoperation, complications … ORLab
  • 3. Models for different time intervals Tables of 80% random sample of Mayo patients: •Chart events •Cohorts •OP Notes •Allergies •Cllnical notes •Flowsheets •Labs •MAR •OP notes •NLP annotations evaluated data Matched complications info from NSQIP and Gold Standard No match to complications info (6,740) Joined surgeries with op notes (proc. group, diag. group, proc. mode) Added rules engine features No match to rules engine data (78) Surgeries w/ matched complications info (1,273) 80% random sample of surgeries (8,013) Completed dataset (1,082) Pre-surgery model dataset (1,082) 30 Days Post Surgery model dataset (898) Defined surgeries removed outliers variable transformation imputed data No match to NLP annotations (106) Surgery Completion model dataset (1,004) Added NLP annotations
  • 6.
  • 7. Mobile applications • Identify potential problems (pain, diet management, complications) • Clinician effort – Current medical records: 30 mi (95% navigation, 619 mouse clicks) – New Tool: 4 min (25 mouse clicks)
  • 8. Objective • Develop accessible and accurate guidance in the design and analysis of observational studies. 70 statisticians from 15 countries Topic groups: Missing data, Initial data analysis, Variable selection, Measurement error and misclassification, Study design, Evaluating prediction models, Causal inference, Survival analysis, High dimensional data analysis STRATOS Initiative (STRengthen Analytical Thinking in Observational Studies) Intended for applied statisticians and other data analysts with varying levels of statistical education, experience, and interests.

Editor's Notes

  1. Different standards across systems variable names, values, units, date-time standards Missing documentation means different things Compliance Non-compliance Coding problem Not documented Missing Timing of events is difficult to discern: relevant for healthcare delivery Identifying duplicate records Identifying primary procedure
  2. mentioned several times that this is a message that Drs Nelson and Larson should hear. they have a strong desire to be able to move faster