Clinicians can pursue the clinical findings for specific patients until reaching a diagnosis in real time. When using electronic ED complaints, one relies on symptoms volunteered by patients in the triage setting. Patients seek emergency care at different stages of disease and there is scant information detailing how they respond when allowed only 2-3 complaints. Our emergency department (ED) clinical data warehouse includes date, demographics, complaints, diagnosis, laboratory results, and disposition. We used a process similar to reverse engineering to augment our ability to detect chief complaints and test results consistent with MEE. We started with the diagnosis of MEE and examined the chief complaints and diagnostic findings in patients diagnosed with MEE to develop expanded algorithms.
Objective
Our research questions were:
1.) could we use existing data to empirically improve our syndrome surveillance algorithms?
2.) Is it feasible to combine disparate data sources to detect the same event? We studied these questions using the meningoencephali-tis (MEE) syndrome and the West Nile Virus Chicago outbreak in 2002.