Biology Reference
In-Depth Information
certain diseases can spread so rapidly that even a few hours can significantly
reduce the data's usefulness and relevance for predicting trends.
Support assessment of confidence in composite data
. Providing health profes-
sionals with confidence ratings for the accuracy of the information displayed
will support their decision-making as well as alert them to when confirmation
of the information is needed (e.g., a coding error for Crimean hemorrhagic
fever instead of congestive heart failure). In addition, oftentimes, unreliable
system performance (e.g., high false alarms) and lack of human interactions
when using a system may be due to design issues that are caused by insuf-
ficient information standardization and management. System designs that
focus on improving system reliability may mitigate such issues.
Use information filtering carefully
. As more sources of data are brought into
biosurveillance systems, more new sources are identified. Recently, more
attention has been brought to loosely structured sources of data (such as
news feeds, Internet search queries), which may offer much earlier detec-
tion than structured sources. However, an inherent problem with such
sources is that users must sort through a great deal of noise to isolate the
information that is most useful and relevant for their tasks. Nevertheless,
the complexity of sorting through multiple data sources can be mitigated
by designing the tool to carefully filter incoming information based on the
goals and SA information requirements of users as identified using the
GDTA methodology.
Build a common operating picture to support team operations
. This can be
accomplished by providing shared information displays and virtual collab-
orative spaces. Such tools are especially critical for health professionals in
the field (e.g., local area doctors) to help establish a shared understanding of
the situation. One current program, called Mesh4X by InSTEDD has been
designed to support cross-organizational information sharing between dif-
ferent databases, desktop applications, Web sites, and devices (http://code.
google.com/p/mesh4x). The system provides synchronization between
different software and data types so that researchers can easily share and
transfer data that is needed to build a common picture. Even providing a
standard mapping tool such as Google Maps will help to build shared SA.
Avoid display overload in shared displays
. Data overload is a common prob-
lem in many complex interface designs, including biosurveillance systems.
The sheer amount and varying types of data that must be considered and
analyzed by a biosurveillance system could easily cause the tool to be very
cumbersome, complex, and difficult for the end user to utilize and develop
good SA. Biosurveillance systems address this by the development of auto-
mated detection algorithms, which help focus the user's attention on poten-
tial threats, and visualization applications that seek to make patterns in the
input data seem more obvious. Yet, the amount of data produced can still be
quite difficult to manage. Thus, as noted earlier, to be maximally effective,
biosurveillance system interfaces should organize this information around
users' goals and critical SA information requirements.
