Identification

Author

Lazarus R, Haney G, Hou X, Daniel J, Campion F, et al.

Title

The Electronic Support for Public Health (ESP) Project:Automated Detection and Electronic Reporting of Notifiable Diseases

Year

2007

Publication type

Article

Journal

Advances in Disease Surveillance

Created

2012-10-05 20:48:01+00:00

Modified

2016-08-09 20:29:07.990267+00:00

Details

Series

52

Volume

3

Number

4

Pages

1

Extended information

Abstract

OBJECTIVE
To leverage electronic medical record (EMR) systems
to improve the timeliness, completeness, and
clinical detail of notifiable disease reporting.
BACKGROUND
Clinician reporting of notifiable diseases has historically
been slow, labor intensive, and incomplete.
Manual and electronic laboratory reporting (ELR)
systems have increased the timeliness, efficiency, and
completeness of notifiable disease reporting but cannot
provide full demographic information about patients,
integrate an array of pertinent lab tests to yield
a diagnosis, describe patient signs and symptoms,
pregnancy status, treatment rendered, or differentiate
a new diagnosis or from follow-up of a known old
diagnosis. EMR systems are a promising resource to
combine the timeliness and completeness of ELR
systems with the clinical perspective of clinician initiated
reporting. We describe an operational system
that detects and reports patients with notifiable diseases
to the state health department using EMR data.
METHODS
The Electronic medical record Support for Public
health (ESP) system detects notifiable diseases by
scanning ambulatory encounter data for combinations
of laboratory test results, diagnostic codes, medication
prescriptions, and vital signs suggestive of target
conditions. ESP is configured as an independent data
repository populated by daily flat file extracts of
comprehensive encounter data received from a clinical
practice’s EMR [1]. The decoupled architecture
offloads computing burden from the source EMR and
make facilitates ESP integration with varying EMR
products. Security is controlled by placing ESP behind
the source practice’s firewall. Proprietary and
idiosyncratic source EMR codes are translated into
universal nomenclature via a user-generated map.
When a notifiable case is found, ESP generates an
HL7 case report and securely transmits it to the state
health department. Case reports include patient and
clinician contact information, lab tests, symptoms,
treatment, and pregnancy status. ESP has been installed
in Atrius Health, a multi-site, multi-specialty
medical practice with over 600 clinicians serving
more than 600,000 patients in eastern Massachusetts.
Currently, ESP identifies and reports cases of chlamydia,
gonorrhea, and pelvic inflammatory disease.
Reporting on acute hepatitis A, acute hepatitis B,
chronic hepatitis B, acute hepatitis C, Lyme disease,
and tuberculosis is scheduled to begin later this year.
RESULTS
ESP has been operational in Atrius Health since
January 2007. Since activation, ESP has generated
over 800 case reports. The positive predictive value
of ESP reports relative to chart review is 100%.
Comparison with concurrent, independent manual
reporting through April 18, 2007 showed a substantial
increase in the number of case reports: 517 versus
370 cases of chlamydia (40% increase) and 64
versus 43 cases of gonorrhea (49% increase). There
was also a substantial increase in pelvic inflammatory
disease cases (18 versus 0 cases). Treatment information
was included on 100% of patients reported by
ESP versus 88% of manually reported patients.
Pregnancy status was included on 100% of ESP reports
versus only 30% of traditional, manual reports.
Amongst patients manually reported to the health
department, ESP identified 32 missed cases of pregnancy.
An additional 14 cases of pregnancy were
found amongst patients reported by ESP alone.
CONCLUSIONS
Automated detection and electronic reporting of notifiable
diseases using data from EMR systems can
substantially increase the completeness and clinical
richness of case reports compared to clinician initiated
paper-based reporting systems.