EpiS3: a semantically interoperable
social network for syndromic
surveillance and disease control
Luciana Tricai Cavalini and Timothy Wayne Cook
National Institute of Science and Technology – Medicine Assisted by Scientific Computing
Summary
•
•
•
•
•
•
The problem
The current solution
Remaining challenges
A new approach
Implementation
Future steps
Syndromic Surveillance:
THE PROBLEM
Problem 1: Detecting Cases
Index case
First cases detected
Fever?
Bleeding?
Jaundice?
Problem 2: Decision Making
Syndromic Surveillance:
THE CURRENT SOLUTION
Current solution: Standardize the data model
The Current Solution: Issues
• Top-down data models
– Risk of inaccurate or incomplete data
• Hospital/clinic centered applications
– No records from uncovered populations
• Incipient Decision Support Systems (DSS)
– Mostly academic projects in internal medicine
Syndromic Surveillance:
REMAINING ISSUES
Problem is: Accuracy or Utility?
Remaining Questions
• How to collect data in the most opportune
moment?
– At the point of care
– In the household
• How to get data with proper…
– ...accuracy...
– ...granularity...
• ...that will allow implementation of useful DSS
for syndromic surveillance?
How to get...
Dr. Cool
Your patient: Jane
Updated her problem list
on Apr 29, 2014 5:33pm
- Fever: YES
- Bleeding: YES
- Location: Nose
Suspicious case
of Acute Febrile
Hemorrhagic
Syndrome
What to do
...without
creating another
data silo?
Syndromic Surveillance:
A NEW APPROACH
Fever?
Bleeding?
Jaundice?
MedWeb 3.0 Plugin Suite
AFJHS app
Hospital
Rabies
infection controlPoisonous
prophylaxisBioterrorism
app
app
app
animals app
MLHIM-based
implementation
Minimalistic,
XML-based
MMD technology
Multilevel Model-Driven Approach
Harmonization
And so on…
Epidemiological Surveillance Support System (EpiS3):
IMPLEMENTATION
Acute Febrile Jaundice Hemorrhagic Syndrome (AFJHS) App
AFJS
AFHS
AFJHS
> 1 y/o
Fever 0-3 wks
Jaundice
> 1 y/o
Fever 0-3 wks
Bleeding signs
> 1 y/o
Fever 0-3 wks
Jaundice and Bleeding
Treat
malaria
Malaria blood smear test
Positive
Negative
Evaluate current epidemiological profile of the territory
AFJS
AFHS
AFJHS
Hepatitis
Yellow Fever
Leptospirosis
Sepsis
Typhoid Fever
Dengue
Sepsis
Meningococcemia
Typhoid Fever
Hantavirus
Other Arbovirosis
Hepatitis
Yellow Fever
Leptospirosis
Sepsis
Typhoid Fever
Reference
Model
Concept
Constraint
Definition
Concept Constraint Definition Generator
(CCD-Gen)
www.ccdgen.com
CCD Library on CCD-Gen
www.ccdgen.com/ccdlib
AFJHS App Form on CCD-Gen
AFJHS App: CCD Schema
AFJHS App: Sample Data Instances
AFHS with
spontaneous
bleeding
AFJHS App: Sample Data Instances
AFHS with
tourniquet test
positive
AFJHS App: Sample Data Instances
AFJS with mucosa
jaundice
Already Implemented:
AFHS
16 AFJHS simulated cases
(all possible classifications)
AFJS
- Spontaneous
bleeding
- Tourniquet test +
-
- Mucosa
- Skin
- Both
-
Age
Fever
Fever duration
No signs
-
Negative
-
AFJHS
Spontaneous +
mucosa
Spontaneous + skin
Spontaneous +
both
Tourniquet +
mucosa
Tourniquet + skin
Tourniquet + both
Malaria
+ a R library that converts
the XML data instances
into R data frames
Epidemiological Surveillance Support System (EpiS3):
FUTURE STEPS
EpiS3: Future Steps
• App User Interface
– Desktop and mHealth versions
• DSS Algorithms
– Clinical evaluation
– Messaging
– Reporting
• EpiInfo Form Builder for MLHIM data
Thank you!
lutricav@lampada.uerj.br
tim@mlhim.org
google.com/+MedWeb30