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Harmonized Biosurveillan ce Use Case By Resty Namata, Maria Metty & Priyaranjan Tokachichu December 13, 2007

Harmonized Biosurveillance Use Case

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Harmonized Biosurveillance Use Case. By Resty Namata, Maria Metty & Priyaranjan Tokachichu December 13, 2007. Brief History. From November 2002 to 31 July 2003 Severe Acute Respiratory Syndrome in Asia WHO estimates up-to 60 % of cases were healthcare workers. - PowerPoint PPT Presentation

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Page 2: Harmonized Biosurveillance  Use Case

Brief History

From November 2002 to 31 July 2003• Severe Acute Respiratory Syndrome in Asia

• WHO estimates up-to 60 % of cases were healthcare workers

1979 - Sverdlovsk, Russia• Accidental release of anthrax from bio-weapons plant.

• 6 people with flu-like symptoms not treated for anthrax.

• 21 people had died before lab results confirmed anthrax

Following September 11, 2001

• Anthrax release in United States

• Detected early because nation was on heightened alert.

Page 3: Harmonized Biosurveillance  Use Case

Biosurveillance Use Case Requirements

Attributes: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

Ultimate Goal:Early detection

• Need to early detection of natural or man-made disease outbreak in order to mobilize resources and minimize morbidity and mortality

Transmit real-time data from healthcare providers to Public health Agency within 24hr lag

Page 4: Harmonized Biosurveillance  Use Case

BiosurveillanceUse Case context diagram

Public Healthcare Agency

Hospital

Laboratory Organization

Ambulatory - Clinician

Transmit /Receive essential ambulatory care

Send /Receive acknowledgement

Transmit /Receive essential lab results

Transmit /Receive ED visits & Utilization

Data is anonymized & aggregated before transmission from Electronic Medical Record Systems

Page 5: Harmonized Biosurveillance  Use Case

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 24 48 72 96 120 144 168

Incubation Period (Hours)

Disease Detection

Effective Treatment

Period

Gain of 2 days

Early Detection Traditional DiseaseDetection

Phase IIAcute Illness

Phase IInitial

Symptoms

* Use Case Data is collected after lab results and diagnosis

Case for early detection

• Category A disease agents cause non-specific symptoms like fever, cough & fatigue

• People don’t seek medical care during effective treatment period. For example: Consumer Healthcare Products Association survey: 42% of people with “flu” symptoms purchased over-the-counter(OTC) medication prior seeking medical care.

Page 6: Harmonized Biosurveillance  Use Case

Proposal Collect sales data of over-the-counter healthcare

product such as electrolytes, diarrhea, cough, thermometer and fever medications from retail stores and transmit it to public health agency

* Available from National Retail Data Monitor System

* It is low cost

* It has been correlated with disease outbreak.

* It is routinely collected for supply chain management

Page 7: Harmonized Biosurveillance  Use Case

Modified BiosurveillanceUse Case context diagram

Public Healthcare Agency

Hospital

Laboratory Organization

Clinician

Send /Receive acknowledgement

Transmit /Receive essential ambulatory care

Transmit /Receive essential lab results

Transmit /Receive ED visits & Utilization

Retail Store

Transmit Sales OTC sales data

Data is anonymized &aggregated before transmission from Electronic Medical Record Systems

Page 8: Harmonized Biosurveillance  Use Case

OTC-Sales Data Required

Stakeholders• Retails Stores that sell 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

• Universal Product Code• Purchase Date• Sales Total• Product Description• Category• Store Identifiers• Other Information

Page 19: Harmonized Biosurveillance  Use Case

Retail Stores Impact

Page 20: Harmonized Biosurveillance  Use Case

Recommendation

We Recommend adding this data with reservations because:

1. It is difficult for public health to understand the data

2. OTC Data is noisy

3. Hard to identify a person who purchased a product.

4. Due to competitive agreements the store cannot be identified either.