Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.
Lindsay MeyersChristine C GinocchioAimie N FaucettFrederick S NoltePer H GestelandAmy L LeberDiane JanowiakVirginia DonovanJennifer Dien BardSilvia SpitzerKathleen A StellrechtHossein SalimniaRangaraj SelvaranganStefan JuretschkoJudy A DalyJeremy C WallentineKristy LindseyFranklin MooreSharon L ReedMaria E Aguero-RosenfeldPaul D FeyGregory A StorchSteve J MelnickChristine C RobinsonJennifer F MeredithCamille V CookRobert K NelsonJay D JonesSamuel V ScarpinoBenjamin M AlthouseKirk M RirieBradley A MalinMark A PoritzPublished in: JMIR public health and surveillance (2018)
Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.
Keyphrases
- big data
- intellectual disability
- case report
- electronic health record
- infectious diseases
- high resolution
- health information
- risk factors
- adverse drug
- gram negative
- emergency department
- deep learning
- artificial intelligence
- high throughput
- healthcare
- autism spectrum disorder
- data analysis
- antimicrobial resistance
- social media
- respiratory tract
- high speed
- single cell
- tandem mass spectrometry