Public health officials are now receiving more data than ever in electronic formats, and also stand to benefit more than ever from ongoing advances in the medical and epidemiological sciences. At the same time, this growing body of knowledge as well as volatile world events present an increasingly complex set of threats to population health. As a consequence, public health officials are finding that they need to ask many more, and more complex, questions of their data in order to keep sight of the state of the public’s health. Most current disease surveillance systems enable users to ask many different questions of health data, but are limited in that users can only extract results one question, or query, at a time.
Objective
Develop an Automated Data Query tool to allow public health officials to easily extract batches of raw medical encounter data using custom queries that the officials themselves set up. Additionally, the tool shall be capable of running anomaly detection algorithms against the raw data and returning the statistics. Users shall be able to perform their own analyses on the data and/or the statistical results after using the tool to collect the information efficiently. The tool will help them spot trends of interest that may be specific to their own jurisdictions.