Using Indian Health Service Electronic Medical Records for ... · Using Indian Health Service...
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Using Indian Health Service Electronic Medical Records for
Public Health Surveillance:IHS Influenza Awareness System (IIAS), 2009
Jim Cheek, MD, MPHDirector, Division of Epidemiology and Disease Prevention (DEDP)
John T. Redd, MD, MPH, FACPChief, Infectious Diseases Branch, DEDP
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Introduction
• Background– Surveillance in IHS– Electronic surveillance
• Influenza-like illness (ILI) surveillance– Development– Evaluation
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Surveillance in IHS
• Routine state-based surveillance• No parallel IHS system
– Support state system since 1970s– Enhance/improve data– Collaborative effort
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Fully-Electronic Surveillance
• Bioterrorism--syndromic surveillance– Poor specificity– Not a replacement for traditional system
• Electronic surveillance pilots– Lack of data--laboratory or clinical– Difficult to translate case definitions– Disparate computer systems or none
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eSurveillance in IHS
• Opportunity– Common computer system– Rich clinical information– Reasonably timely with electronic health
record– Local awareness alert system
• Challenges– Lack of uniform laboratory data– Electronic case definitions
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H1N1 Influenza Surveillance
• Public health emergency– Need for data in timely fashion– Political will
• Features of ILI surveillance– Syndrome--not dependent on lab data– Tested sets of ICD-9 codes
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IHS H1N1 Surveillance System
• Syndromic surveillance system conceived first weekend of outbreak
• Support/permission obtained from IHS CMO and Area CMOs
• Programming began quickly; multiple software patches since
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Server that
receives export files
Facility 1 RPMS
Facility 2 non RPMS
Facility 3RPMS
Facility 4 NextGen
Facility 5nonWAN RPMS
Server where files
are processed
HHS Portal Site
National Report
AreaReport
FacilityReport
Data Flow
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Fields Collected (Initial)
• Gender, DOB, Community of Residence
• Service unit, date of visit/hospitalization
• Up to 3 ILI diagnoses (initially 36 codes)
• Temperature• Denominator (per day at each facility)
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Fields Collected (New)
• Risk factor diagnoses– Asthma– Diabetes– Obesity– Pregnancy status
• BMI• Severity codes
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Fields Collected (New)
• Pneumococcal vaccine status• H1N1 vaccine status• Seasonal influenza vaccine status• Adverse events
– Collaboration with FDA
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Clinics Covered
General Diabetic Gyn
Immunization Internal Medicine Pediatrics
Well Child Care
Family Practice
Emergency Room
Epsdt Women’s Health Urgent Care
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Development of Definition
• Consideration of other definitions– ESSENCE– BIOSENSE
• Initial manual chart review at Southwestern site (~ 120 patients)
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Development of Definition
• Comparison of IIAS codes with chart ILI
• Full 36-code set clearly overly sensitive (“ILI” % up to 30%)
• Code-by-code frequency analysis– Multiple models considered
• Simplicity considerations
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OR
Influenza-Like Illness Definition
ICD-9 diagnosis code of 488.1 or 487.x
A body temperature of 100º F or greaterAND
one or more ILI ICD-9 diagnosis codes:079.99 464.00 478.9382.00 464.10 480.9382.9 464.20 485460 465.0 486461.8 465.8 490461.9 465.9 780.6462 466.0 780.60463 466.19 786.2
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Examples of ICD-9 Codes(total of 24 collected)
079.99 Unspecified viral infection
382.00 Acute suppurativeotitis media
382.9 Unspecified otitis media
460 Acute nasopharyngitis [common cold]
461.8 Other acute sinusitis
461.9 Acute sinusitis, unspecified
462 Acute pharyngitis
463 Acute tonsillitis
780.60 Fever, unspecified
786.2 Cough 486 Pneumonia, organism unspecified
480.9 Viral pneumonia, unspecified
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Example of Data Shared with Local Site
Example of Data Shared with Local Site
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Influenza-Like Illness Counts by Day, Age Group,
and Sex for all days
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Percentage of Visits for Influenza-Like Illness,
Weeks 12-47
0
1
2
3
4
5
6
7
10 15 20 25 30 35 40 45 50
Ove
rall
ILI %
(ILI V
isits
/Tot
al V
isits
)
Week
All data are preliminary and may change as more reports are received.
Percentage of Visits for Influenza-Like Illness (ILI) in IHS, Weeks 12 (3/22/09 –
3/28/09) through 47 (11/22/09 – 11/28/09)
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ILI %, Weeks 35 - 47
%
Week
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Results
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IHS ILI Visits by Age, as % of Total, Week 47
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IHS ILI Hospitalization Rate per 100,000 Total Visits
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Vaccine Delivery
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Uptake: At least 1 dose of H1N1 by Risk Group
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Evaluation: Methodology• Chart review
– Outpatient and emergency visits– September 14th and 21st, 2009– Screen visits using ILINet ILI case definition
Temperature ≥ 100°F + sore throat or cough
• Comparison of IIAS surveillance data with chart review (gold standard)
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Results• Reviewed 82.8% (1,968) of
ambulatory visits on study days• Chart review found 63 visits that met
ILI criteria• IIAS surveillance captured 95 visits
from the same patient group
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IIAS Surveillance Attributes
• ILI = 3.2% (by chart review)
• IIAS sensitivity = 95.2%• IIAS specificity = 98.2%• IIAS positive predictive
value = 63.2%
60
3 1870
35
Chart review+
+
-
-
Influenza-like illness by:
IIAS
sur
veill
ance
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False Positives• IIAS identified 35 false positive ILI visits
– 22 afebrile patients with an influenza ICD9 code
– 13 febrile patients with an ILI-related ICD9 code without documented cough or sore throat
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Usefulness• Provides ILI information on a
vulnerable population• Fills geographic gaps in ILINet
surveillance• ILI prevalence used to allocate
influenza vaccine to federal healthcare workers at IHS facilities
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Simplicity and FlexibilitySimplicity
• Automated data collection, analysis, and dissemination
• Comprehensive surveillance without personnel burden
Flexibility• IIAS collects
additional influenza information– Hospitalizations– Vaccination coverage– Adverse events
following immunization• Ability to modify
computer patch and look retrospectively
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Timeliness and Stability
Timeliness• Dependent on data
entry into RPMS• Variable by
location
Stability• Stable since
inception
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Where do we go now?
• ILI surveillance– Robust, timely system– Demonstrated utility
• Expand to other diseases/conditions– Develop electronic case definitions– Standardize laboratory data (LOINC)
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Vision for the future
• Capture all notifiable diseases• Timely reporting
– Public health authorities– Augment state-based systems
• Provide a model system
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Acknowledgements
• Dr. Larry Layne• Dr. Terry Cullen• Dr. Anil Suryaprasad• Dr. James Keck• Dr. Michael Bruce• Dr. Thomas Hennessy