re-purposing the electronic medical record for public health

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 ObjectiveOverall Objective

• To find out if a comprehensive EMR can contribute to the early detection of an infectious disease epidemic

StoStopp

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OutbreakOutbreakValid?Valid?

OutbreakOutbreakSign.?Sign.?

Escalate PHSEscalate PHS

Case DetectorCase Detector

Outbreak DetectorOutbreak Detector

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CaseCaseValid?Valid?NN

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EMREMR


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CPRSCPRS


CPRS: Provider Interface

CPRS: Free-text data entry

CPRS: Structured data entry

CPRS is really VISTA

Data Extraction: “MDE”

SQL: Data Transformation

Sequences

SQL: Primary Warehouse

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CPRSCPRS


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Data ExtractorData Extractor

SQL DatabaseSQL Database

VISTAVISTA/CPRS/CPRS


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 re-reviewed 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 DetectorRetained parameters

• “Cold remedies”

• Fever >= 38ºC

“Cold Remedies”CN101 opioid analgesics like ”codeine" OR CN900 CNS medications, other acetaminophen/diphenhidramine, ORMS102 non-salicylate NSAIS (does not include antirheumatics) , ORNT100 decongestants, nasal, ORNT200 anti-inflammatories, nasal, ORNT400 antihistamine, nasal, ORNT900 nasal and throat, topical, use “other throat lozenge" only, ORRE200 decongestants, systemic, ORRE301 opioid-containing antitussives/expectorants, ORRE302 non-opioid-containing antitussives/expectorant, ORRE501 antihist/decongest, ORRE502 antihist/decongest/antitussive , ORRE503 antihist/decongest/expectorant , ORRE507 antihist/antitussive, ORRE508 antihist/antitussive/expectorant , ORRE513 decongest/antitussive/expectorant , ORRE516 decongest/expectorant , ORRE599 cold remedies, ORAH102 antihistamines, ethanolamine, ORAH104 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)

Mumbo jumbo fever nonsense trivia blabla etc

TXT ILI Case Detectors

NegEx

TXT ILI Case Detectors

NegEx

Negation?

Mumbo jumbo fever nonsense trivia blabla etc

Which One Should We Use?

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1999 2000 2001 2002 2003 2004

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Number of ILI Cases

Time Series: VAMHCS, Jan 1999 – Dec 2004

ILI Cases VAMHCS 2002-2003

Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

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Number of ILI Cases

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Outbreak GeneratorOutbreak Generator

Outbreak Generator for Outbreak Generator for Baltimore 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



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Number of ILI Cases

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Outbreak Outbreak DetectorDetector




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Number of ILI Cases

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

• EMR data can significantly enhance automated Case detection of ILI compared to the use of ICD-9 codes alone

• Whole-system simulation and is required to evaluate and calibrate the performance of alternative single-case detectors

Conclusions (1)

• For influenza surveillance, case-detection algorithms should aim for high positive predictive value, and target ILI cases who are febrile

Conclusions (2)

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Bob Sawyer

Shawn LoftusBrett South

Ericka Kalp

Sylvain DeLisle

Trish Perl

Steve Altman, Raju Vatsaval

Jill Anthony

Shobha PhansalkarBrett South

Matt SamoreGary SmithHolly Gaff

Hongzhang ZhengZhilian MaFang TianPaul Sun“Vibrio”

re-purposing the electronic medical record for public health

Documents

structured data entry

freetext data entry

ornt900 nasal

illness ili

vista data extraction

provider interface cprs

orre301 opioid

orre302 nonopioid