Syndromic surveillance using automated collection of computerized discharge diagnoses

被引:0
|
作者
William B. Lober
Lisa J. Trigg
Bryant T. Karras
David Bliss
Jack Ciliberti
Laurie Stewart
Jeffrey S. Duchin
机构
[1] University of Washington,Clinical Informatics Research Group
[2] Public Health-Seattle and King County,undefined
[3] Overlake Hospital Medical Center,undefined
关键词
Biological warfare; Bioterrorism; Data collection; Database; Informatics; Information systems; Sentinel surveillance;
D O I
10.1007/PL00022320
中图分类号
学科分类号
摘要
The Syndromic Surveillance Information Collection (SSIC) system aims to facilitate early detection of bioterrorism attacks (with such agents as anthrax, brucellosis, plague, Q fever, tularemia, smallpox, viral encephalitides, hemorrhagic fever, botulism toxins, staphylococcal enterotoxin B, etc.) and early detection of naturally occurring disease outbreaks, including large foodborne disease outbreaks, emerging infections, and pandemic influenza. This is accomplished using automated data collection of visit-level discharge diagnoses from heterogeneous clinical information systems, integrating those data into a common XML (Extensible Markup Language) form, and monitoring the results to detect unusual patterns of illness in the population. The system, operational since January 2001, collects, integrates, and displays data from three emergency department and urgent care (ED/UC) departments and nine primary care clinics by automatically mining data from the information systems of those facilities. With continued development, this system will constitute the foundation of a population-based surveillance system that will facilitate targeted investigation of clinical syndromes under surveillance and allow early detection of unusual clusters of illness compatible with bioterrorism or disease outbreaks.
引用
收藏
页码:i97 / i106
相关论文
共 50 条
  • [31] The validity of chief complaint and discharge diagnosis on emergency department-based syndromic surveillance
    Fleischauer, AT
    Silk, BJ
    Schumacher, M
    Komatsu, K
    Santana, S
    Vaz, V
    Wolfe, M
    Hutwagner, L
    Cono, J
    Berkelman, R
    Treadwell, T
    ACADEMIC EMERGENCY MEDICINE, 2004, 11 (12) : 1262 - 1267
  • [33] AUTOMATED COLLECTION OF WATER-QUALITY AND DISCHARGE DATA ON STREAMS
    STURGES, DL
    WINTER, CJ
    57TH ANNUAL MEETING WESTERN SNOW CONFERENCE, 1989, : 122 - 125
  • [34] Using syndromic surveillance for unintentional and undetermined intent drowning surveillance in a large metropolitan area
    Rohit P. Shenoi
    Briana Moreland
    Jennifer L. Jones
    Nicholas Peoples
    Elizabeth A. Camp
    Ned Levine
    Injury Epidemiology, 11 (Suppl 1)
  • [35] Machine learning for syndromic surveillance using veterinary necropsy reports
    Bollig, Nathan
    Clarke, Lorelei
    Elsmo, Elizabeth
    Craven, Mark
    PLOS ONE, 2020, 15 (02):
  • [36] Syndromic surveillance using ambulance transfer data in Tokyo, Japan
    Sugishita, Yoshiyuki
    Sugawara, Tamie
    Ohkusa, Yasushi
    Ishikawa, Takatoshi
    Yoshida, Michihiko
    Endo, Hiroyoshi
    JOURNAL OF INFECTION AND CHEMOTHERAPY, 2020, 26 (01) : 8 - 12
  • [37] Syndromic surveillance using veterinary laboratory diagnostic test requests
    Dorea, F. C.
    Muckle, C. A.
    Kelton, D.
    McEwen, B. J.
    McNab, W. B.
    Sanchez, J.
    Revie, C.
    EPIDEMIOLOGIE ET SANTE ANIMALE, NO 59-60, 2011, 59-60 : 128 - 130
  • [38] Syndromic surveillance for West Nile virus using raptors in rehabilitation
    Alba, Ana
    Perez, Andres M.
    Ponder, Julia
    Puig, Pedro
    Wunschmann, Arno
    Vander Waal, Kimberly
    Alvarez, Julio
    Willette, Michelle
    BMC VETERINARY RESEARCH, 2017, 13
  • [39] Syndromic surveillance of COVID-19 using crowdsourced data
    Desjardins, Michael R.
    LANCET REGIONAL HEALTH-WESTERN PACIFIC, 2020, 4
  • [40] Syndromic surveillance for West Nile virus using raptors in rehabilitation
    Alba Ana
    M. Perez Andrés
    Ponder Julia
    Puig Pedro
    Wünschmann Arno
    Vander Waal Kimberly
    Alvarez Julio
    Willette Michelle
    BMC Veterinary Research, 13