Comparison of office visit and nurse advice hotline data for syndromic surveillance--Baltimore-Washington, D.C., metropolitan area, 2002

Kaiser Permanente of the Mid-Atlantic States (KPMAS) is collaborating with the Electronic Surveillance System for Early Notification of Community-Based Epidemics II (ESSENCE II) program to understand how managed-care data can be effectively used for syndromic surveillance.

This study examined whether KPMAS nurse advice hotline data would be able to predict the syndrome diagnoses made during subsequent KPMAS office visits.

All nurse advice hotline calls during 2002 that were linked to an outpatient office visit were identified. By using International Classification of Diseases, Ninth Revision (ICD-9) codes, outpatient visits were categorized into seven ESSENCE II syndrome groups (coma, gastrointestinal, respiratory, neurologic, hemorrhagic, infectious dermatologic, and fever). Nurse advice hotline calls were categorized into ESSENCE II syndrome groups on the basis of the advice guidelines assigned. For each syndrome group, the sensitivity, specificity, and positive predictive value of hotline calls were calculated by using office visits as a diagnostic standard. For matching syndrome call-visit pairs, the lag (i.e., the number of hours that elapsed between the date and time the patient spoke to an advice nurse and the date and time the patient made an office visit) was calculated.

Of all syndrome groups, the sensitivity of hotline calls for respiratory syndrome was highest (74.7%), followed by hotline calls for gastrointestinal syndrome (72.0%). The specificity of all nurse advice syndrome groups ranged from 88.9% to 99.9%. The mean lag between hotline calls and office visits ranged from 8.3 to 50 hours, depending on the syndrome group.

The timeliness of hotline data capture compared with office visit data capture, as well as the sensitivity and specificity of hotline calls for detecting respiratory and gastrointestinal syndromes, indicate that KPMAS nurse advice hotline data can be used to predict KPMAS syndromic outpatient office visits.