620 using automated surveillance technology for influenza detection

Saturday, March 20, 2010
Grand Hall (Hyatt Regency Atlanta)
Linda R. Greene, RN, MPS, CIC , Rochester General Hospital, Rochester, NY
Angela Bivens, MSN, NP-C, CIC , Premier Inc., Charlotte, NC
Salah Qutaishat, PhD., CIC., FSEA , Premier Inc., Marshfield, WI

Background: In October of 2009, increasing numbers of influenza A cases were identified from the outpatient and inpatient settings. In order to manage this outbreak, enhanced surveillance methodologies were necessary.

Objective: To determine if electronic surveillance could provide a timely and effective method of tracking influenza in our hospital and outpatient setting while streamlining surveillance activities associated with isolation and reporting to relevant internal and external stakeholders

Methods: Using an automated surveillance software ( Safety Survellor , Premier inc, Charlotte, NC) , rules were built to automatically generate alerts and e-mail notification to Infection Preventionists ( IP's ) on all confirmed laboratory reports of influenza. Additional reports were developed and accessed by the IP'S daily. Reports included a line listing of all patients tested for influenza (including negative results). This information provided the IP's with a threshold of suspicion for influenza assuring isolation of patients pending final results. A second report included all hospitalized patients receiving antiviral treatment specific to influenza. Both reports included detailed patient specifc information, demographic and pharmacy data necessary for assessment, evaluation, and public health reporting.

Results:  Data were tracked during a six week period ending the week ending 11/ 7/09.  30 % of the patients testing positive were identified by rapid antigen (30 % sensitivity rate). This supported the hospital policy to continue isolation on all tested patients pending final culture results. The total number of patients identified by microbiology data was 660 (198 by rapid antigen, 462 by shell viral culture) 658 patients were influenza A, 2 influenza B.  Electronic results compared favorably with data reported from the microbiology laboratory (100% accuracy). Of the 660 patients, 51 required admission (8 %). The IP's identified 5 patients who were not initially isolated. These patients were placed on isolation precautions only after the IP identified the patient through automated surveillance. Additionally, the IP staff identified 4 patients who had not been started on influenza specific medication prompting physician review.

Conclusions: Rapid Identification of clusters of acute illness in hospital and outpatient settings is a fundamental challenge for IP'S. Recent outbreaks of influenza type illness provide new impetus to implement automated surveillance methodologies that utilize a combination of laboratory, pharmacy and admission data to identify and alert IP'S to both individual and clusters of events such as influenza A. The implementation of such technology has afforded IP'S at this facility the ability to access real time patient specific data, ensuring timely identification of clusters and trends. Timely identification of such events is imperative to proactive infection prevention.