Re-purposing The Electronic
Medical Record For Public
Health
Sylvain DeLisle MD, MBA
VA Maryland Health Care System
and University of Maryland
Parade to Promote Sale of War Bonds,
Philadelphia, September 28, 1918
Overall Objective
• To find out if a comprehensive EMR can
contribute to the early detection of an
infectious disease epidemic
Case Detector
EMR
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
Case Detector
CPRS
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
CPRS: Provider Interface
CPRS: Free-text data entry
CPRS: Structured data entry
CPRS: Structured data entry
CPRS is really VISTA
Data Extraction: “MDE”
SQL: Data Transformation
Sequences
SQL: Primary Warehouse
Case Detector
CPRS
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
Case Detector
SQL Database
VISTA
/CPRS
Data Extractor
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
Case Detector
SQL Database
VISTA
/CPRS
Data Extractor
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
Focus on ILI
• We focused on influenza-like illness (ILI) as a
syndrome that may indicate an event of
public health significance
– Anthrax
– Plague
– SARS
– Influenza
ILI Case Definition
• Positive influenza culture or antigen
OR
• Any two of the following (<= 7 days duration)
– Cough
– Fever or chills or night sweats
– Pleuritic chest pain
– Myalgia
– Sore throat
– Headache
AND
• Illness not attributable to a non-infectious etiology
Gold Standard Detector:
Manual Case Review
• VA Maryland Health Care System (VAMHCS) and the Salt Lake
City VA (SLCVAMC)
• Study period: 10/01/03 to 3/31/04
• 15,377 (of 253,818) random sample, ER and selected outpatient
clinics
• All ILI cases and a 10% subsample of the records were rereviewed by a MD, discordant pairs were adjucated by a panel
of three MDs
• Found 280 cases
ICD-9-based ILI Detectors
• Compared “respiratory” ICD-9 groupings from
– BioSense (CDC)
– Essence (DoD)
– Optimized (VA)
Structured Parameters-based
Case Detectors
• Vitals: Temp >38, RR > 22, HR > 100
• Orders/dispense for Rx: expectorants, antibiotics,
antitussives, decongestants, anti-emetics,
antidiarrheals
• Order/results for tests: CBC, Diff, Strep. screen,
Sputum cultures, Gram stain, Respiratory serologies,
Influenza cultures/antigens, Chest/sinus XRays or CT
scans
ILI Case Detector
Retained parameters
• “Cold remedies”
• Fever >= 38ºC
“Cold Remedies”
CN101 opioid analgesics like ”codeine" OR
CN900 CNS medications, other acetaminophen/diphenhidramine, OR
MS102 non-salicylate NSAIS (does not include antirheumatics) , OR
NT100 decongestants, nasal, OR
NT200 anti-inflammatories, nasal, OR
NT400 antihistamine, nasal, OR
NT900 nasal and throat, topical, use “other throat lozenge" only, OR
RE200 decongestants, systemic, OR
RE301 opioid-containing antitussives/expectorants, OR
RE302 non-opioid-containing antitussives/expectorant, OR
RE501 antihist/decongest, OR
RE502 antihist/decongest/antitussive , OR
RE503 antihist/decongest/expectorant , OR
RE507 antihist/antitussive, OR
RE508 antihist/antitussive/expectorant , OR
RE513 decongest/antitussive/expectorant , OR
RE516 decongest/expectorant , OR
RE599 cold remedies, OR
AH102 antihistamines, ethanolamine, OR
AH104 antihistamines, alkylamine
TEXT-based ILI Case Detectors
• Examine the text of all clinical encounter
notes on the day of an index visit
• Used modified NegEx algorithm (Wendy
Chapman)
TXT ILI Case Detectors
NegEx
Mumbo jumbo fever nonsense trivia blabla etc
TXT ILI Case Detectors
NegEx
Mumbo jumbo fever nonsense trivia blabla etc
TXT ILI Case Detectors
NegEx
Mumbo jumbo fever nonsense trivia blabla etc
Negation?
TXT ILI Case Detectors
NegEx
Mumbo jumbo fever nonsense trivia blabla etc
Negation?
TXT ILI Case Detectors
NegEx
Mumbo jumbo fever nonsense trivia blabla etc
Negation?
TXT ILI Case Detectors
NegEx
Mumbo jumbo fever nonsense trivia blabla etc
Negation?
Which One Should We Use?
Case Detector
SQL Database
VISTA
/CPRS
Data Extractor
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
Time Series: VAMHCS, Jan 1999 – Dec 2004
80
Number of ILI Cases
70
60
50
40
30
20
10
0
1999
2000
2001
2002
2003
2004
ILI Cases VAMHCS 2002-2003
70
Number of ILI Cases
60
50
40
30
20
10
0
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb Mar
Apr
May
Jun
Case Detector
SQL Database
VISTA
/CPRS
Data Extractor
Outbreak Generator
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
Outbreak Generator for Baltimore
1.
Age-structured deterministic epidemic model generates
the number of new infections in each age group, each
day, for each zip code
2.
Stochastic metapopulation spatial model determines how
the outbreak will extend in space-time
3.
Stochastic clinical features algorithm determines which
of these infections will be recognized cases at the VA,
and the severity, clinical profile and outcome for each
recognized case
ILI Cases VAMHCS 2002-2003
70
Number of ILI Cases
60
50
40
30
20
10
0
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb Mar
Apr
May
Jun
ILI Cases VAMHCS 2002-2003
70
Number of ILI Cases
60
50
40
30
20
10
0
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb Mar
Apr
May
Jun
ILI Cases VAMHCS 2002-2003
70
Number of ILI Cases
60
50
40
30
20
10
0
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb Mar
Apr
May
Jun
Case Detector
SQL Database
VISTA
/CPRS
Data Extractor
Outbreak Generator
Outbreak Detector
N
Case
Valid?
Y
N
Outbreak
Valid?
Y
Stop
N
Outbreak Y
Sign.?
Escalate PHS
ILI Cases VAMHCS 2002-2003
70
Number of ILI Cases
60
50
40
30
20
10
0
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb Mar
Apr
May
Jun
BIOSENSE (Current)
Fixed Threshold (p-value)
Optimized Threshold
Improved ICD-9 Codesets
Adding Structured Parameters
Adding Text Analysis
Optimizing for Positive Predictive Value
Surveillance for Febrile_ILI
Conclusions (1)
• EMR data can significantly enhance automated
Case detection of ILI compared to the use of ICD9 codes alone
• Whole-system simulation and is required to
evaluate and calibrate the performance of
alternative single-case detectors
Conclusions (2)
• For influenza surveillance, case-detection
algorithms should aim for high positive predictive
value, and target ILI cases who are febrile
Bob Sawyer
VISTA
/CPRS
Data Extractor
Jill Anthony
Shawn Loftus
Brett South
Shobha Phansalkar
Brett South
Matt Samore
Outbreak Generator
Gary Smith
Holly Gaff
Ericka Kalp
Case Detector
SQL Database
Outbreak Detector
Hongzhang Zheng
Case
Zhilian Ma
Fang TianN Valid?
Paul Sun
Y
“Vibrio”
N
Outbreak
Valid?
Sylvain DeLisle
Trish Perl
Y
Stop
N
Outbreak Y
Sign.?
Steve Altman, Raju Vatsaval
Escalate PHS