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The Role of Information/Communication Technology and Monitoring/Surveillance Systems in Bioterrorism Preparedness

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Slide Presentation by Michael Wagner, M.D., Ph.D.

On October 21, 2003, Michael Wagner, M.D., Ph.D., made a presentation in the Web-assisted Audioconference entitled Methods for Real-Time Detection and Assessment of Disease Outbreaks Using Information Technology.

The is the text version of Dr. Wagners's slide presentation. Select to access the PowerPoint® slides (990 KB).

Methods for Real-Time Detection and Assessment of Disease Outbreaks Using Information Technology

Michael Wagner, M.D., Ph.D.
Director, Real-Time Outbreak and Disease Surveillance Laboratory
Assistant Professor, Medicine and Intelligent Systems
Center for Biomedical Informatics
University of Pittsburgh
Pittsburgh, PA

Slide 1

What is the Mission of the RODS Lab?

Slide contains text and text boxes linked by directional arrows. On the left most side of the slide is an underlined heading that reads "First Hint of Trouble." Under this heading are three bullets: statistical analysis of data; astute observer; and definitive diagnosis of new or "terrorism" organism. From the top of this bulleted list is an arrow that leads to a text box on the right. Within this text box is the heading "Analysis/Characterization" that is followed by three bulleted questions: Is it an emergency? Quarantine? and Get more antibiotics? Directly below that text box is another that has the heading "Additional Data Collection" which is followed by three bullets: "shoeleather," microchip testing, and decision support at the point-of-care. These two text boxes are linked to each other with semi-circular arrows pointing from the Analysis/Characterization text box to the Additional Data Collection text box. From the Analysis/Characterization text box an arrow and points to the right, to the word "Responses."

Slide 2

National Retail Data Monitor

  • Every product already has a UPC bar code.
  • Every purchase already is scanned optically.
  • 12 big chains already merged thousands of stores and already receive daily batch feeds of sales data from those stores by midnight.
  • We "asked" for the data.
  • We worked with the industry to securely transmit the data every day to Pittsburgh (by 3 p.m.).
  • We built the databases, created the analytic product categories, and the analytic tools.

On the right hand side of the slide are graphics indicating that data goes from the retail stores to a data center, then to the RODS lab and finally to the Pennsylvania Department of Health.

Slide 3

Current Status

  • 18,000 stores (35% market share)—below this heading is a map of the United States that represents the location of each retail store that is using the NRDM.
  • 220 user accounts in 33 States, CDC—below this heading is a bar graph. This graph charts the percentage of weekdays and weekend days on which at least one user from a State logged into the system. States and percentages (weekday followed by weekend) represented on this chart are: NJ (100, 100), UT (100, 50), OH (100, 0), PA (100, 0), FL (100, 0), TN (80, 0), ND (80, 0), MA (60, 0), WV (60, 0), NY (40, 0), AR (40, 0), CA (40, 0), NE (40, 0), MT (40, 0), KY (20, 0), MI (20, 0), and NC (40, 0).

Slide 4

Future Plans

  • Achieve 70% market share.
  • Decrease time latency.
  • Add monitoring of prescription antibiotics.
  • Deploy in second country.
  • More automation of detection analysis.
  • Transition.

NRDM development supported by PA Bioinformatics Grant #ME-01-737, The Alfred P. Sloan Foundation and New York State

Slide 5

For More Information

Slide 6

Real-time Surveillance of Hospital Data: The RODS System

Slide 7

HL7 Admission, Discharge, Transfer Message

This slide shows 9 lines of information written in a code using various symbols. Three text boxes highlight zip code, visit time and date, and free-text chief complaint information included among the lines of code.

Slide 8

Naïve Bayes Text Classifier

The text "N/V/D- Chief Complaint" is followed by an arrow that points to the right towards a text box and is labeled "Naïve Bayes Classifier." The text box contains the following information: P (Respiratory|NVD)= .05, P (Botulinic|NVD)= .001, P (Constitutional|NVD)= .01, P (GI|NVD) = .9, P (Hemorrhagic|NVD)= .001, P (Neurologic|NVD)= .001, P (Rash|NVD)= .001, P (None|NVD)= .036.

On the right hand side of this text box is another arrow that points to the right and is followed by the text "GI Prodrome Output."

Slide 9

Time Series

This slide contains a screen shot of a Web page for Utah's RODS system. At the top of the page is a "Systems Message Log," Information in this log includes date, time and a short note describing any alarm that may be occurring. Below the message log is a series of 8 line graphs. They represent the number of all visits, respiratory, GI, rash, constitutional, hemorrhagic, neurological, and botulinic. At the bottom of the page is a drop-down menu that allows a user to select and view a variety of "counts" or graphs.

Slide 10


This slide contains another screen shot of a Web page from Utah's RODS system. This page is identified at a "Mapplot" and contains a State map that can broken down into many "layers." These layers are identified on the right-hand side of the page as Olympic Village, Healthcare Facilities, Highways, Streets, Water, Landmarks, Airports, Zip Codes, Counties, and Western States. The map also shows information about the number of visits or "prodromes" in each layer. There is a drop-down menu that allows a user to choose a specific category. At the left of the screen is a legend that helps a user to interpret what number of cases each color on the map represents.

Slide 11

RODS Open Source Project

  • Real-time biosurveillance depends (heavily) on software.
  • Good software doesn't grow on trees.
  • University of Pittsburgh created RODS and then released it—for free.
  • Consultants, users, programmers also do not arise by spontaneous generation.
  • Commonwealth of Pennsylvania funding is being used to catalyze and transition RODS to become one of the world's most used and most advanced syndromic surveillance systems.

Slide 12

For More Information

RODS development supported by PA Bioinformatics Grant #ME-01-737, AHRQ, National Library of Medicine Training Grant (Dr. Espino).

Current as of December 2003

Internet Citation:

Methods for Real-Time Detection and Assessment of Disease Outbreaks Using Information Technology. Text Version of a Slide Presentation at a Web-assisted Audioconference. Agency for Healthcare Research and Quality, Rockville, MD.

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The information on this page is archived and provided for reference purposes only.


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