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Additional Data For Harmonized Use Case for Biosurveillance

Additional Data For Harmonized Use Case for Biosurveillance. HINF 5430 Final Project By Maria Metty, Priyaranjan Tokachichu &Resty Namata December 13, 2007. Agenda. Brief History of Outbreak Harmonized Use Case for Biosurveillance Proposal Costs and Financial Benefits Impact

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Additional Data For Harmonized Use Case for Biosurveillance

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  1. Additional Data For Harmonized Use Case for Biosurveillance HINF 5430 Final Project By Maria Metty, Priyaranjan Tokachichu &Resty Namata December 13, 2007

  2. Agenda • Brief History of Outbreak • Harmonized Use Case for Biosurveillance • Proposal • Costs and Financial Benefits • Impact • Recommendation • Questions

  3. Harmonized Use Case for Biosurveillance Brief History 1979 - Accidental release of anthrax from bio-weapons plant inSverdlovsk, Russia • 6 people with flu-like symptoms not treated & 21 people had died before lab results confirmed anthrax After September 11, 2001 • Anthrax release in United States November 2002 to 31 July 2003 • Severe Acute Respiratory Syndrome in Asia • WHO estimates up-to 60 % of cases were Healthcare workers • What is Biosurveillance Use Case • Specifications given by American Health Information Community for • implementing a Biosurveillance system.

  4. Use Case Requirements Transmit real-time data from electronically enabled healthcare providers to Public health Agency within 24hr lag time. Real-time Fulfilled by data transmission from HER Timeliness Define by Michael Wagner et al. as difference between time of event detection & time event occurred Goal: Early detection Need early detection of natural or man-made disease outbreak in order to mobilize resources and minimize morbidity and mortality

  5. Importance of early detection Traditional Disease Detection Early Detection Gain of 2 days Phase II Acute Illness Phase I Initial Symptoms • Category A disease agents cause non-specific symptoms like fever, cough, fatigue • People don’t seek medical care during effective treatment period. • 42% of people with “flu” symptoms purchased over-the-counter (OTC) • medication prior seeking medical care. • Use Case Data is collected after lab results and diagnosis Effective Treatment Period

  6. Proposal Collect daily sales data of over-the-counter (OTC) healthcare product such as electrolytes, diarrhea, cough, thermometer and fever medications from retail stores and transmit it to public health agency within 24-hour time lag • It has been correlated with disease outbreak. • It is routinely collected for supply chain management • Available from National Retail Data Monitor System • It is low cost

  7. Modified BiosurveillanceUse Case context diagram Clinician Hospital Laboratory Organization Retail Pharmacy Store Public Healthcare Agency Transmit /Receive essential ambulatory care Transmit /Receive ED visits & Utilization Transmit /Receive essential lab results Send /Receive acknowledgement Transmit Sales OTC sales data Current scenario Amended scenario

  8. Scope Includes: Data electronically collected during a routine OTC product sale of pediatric electrolytes, “flu” remedies, chest rubs, diarrhea remedies, thermometer, adult & child anti-fever. Excludes: Sales data of other OTC healthcare products for symptoms not caused by infectious agents e.g allergy Customer identification information.

  9. OTC-Sales Data √ Standard Product identifier √ Purchase Date √ Sales Total √ Product Description √ Category √ Store Identifiers √ Other Information • Stakeholders • Retails Stores that sell OTC healthcare products • Customers who purchase OTC healthcare products • Pre-Conditions • Procedures and agreements signed for data exchange • Post-Conditions • Data transmitted to an authorized Public Health agency • Acknowledgement sent back to sender

  10. Costs

  11. Costs

  12. Costs

  13. Costs

  14. Costs

  15. Impact

  16. Patient Impact

  17. Physician Impact

  18. Physician Impact

  19. Public Health Impact

  20. Retail Stores Impact

  21. Recommendation We Recommend adding this data with reservations because: It is difficult for public health to understand the data OTC Data is noisy 3. Impossible to identify a person who purchased a product. 4. Due to competitive agreements the store cannot be identified either.

  22. Special Thanks to: • Dr. Lael Gatewood • Dr. Richard Pham • Mr. Joseph Plasek • Jayne Griffith Senior Epidemiologist Bioterrorism Unit Minnesota Department of Health 651.201.5085 Jayne.griffith@health.state.mn.us • M. Cleat Szczepaniak • Program Manager of National Retail Data Monitor and Pennsylvania RODS • 412.648.6728

  23. Questions?

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