Need for Decision Support Tools in Local Public Health Surveillance and Response

Current automated surveillance systems monitor for many possible public health events and receive numerous statistical alerts on a daily basis. It is easy to get lost in both the amount of data and number of aberration signals. Each data source has many variables and much of the available information in these data is not currently factored into traditional statistical algorithms. Public health resources are stretched thin and unable to sort through all of the permutations of attributes such as age, locality, and symptom presentation for each signal. Still part of “the real story” of disease burden is buried in these data.

The question is - What approaches can assist in identifying the actionable information that exists within all of these data/signals? Our proposed solution and challenge to the research community is the development of a decision support system.

Each level of complexity in available data is a potential barrier to the practitioner's ability to find actionable events. Robust logic must be discovered to propose useful response paths thereby limiting complexity for the surveillance system user. In order to accomplish this, work needs to be done to discover:

- likely events of interest in different data sources,

- how information can be harvested from every available data element

- how meaningful differences (not just statistically significant) are detected, and

- suggested response protocols

In the end, practical, user-friendly decision support tools are needed in surveillance systems to assist public health practitioners in quickly making sense of masses of data.