Reeder et al. Perceived usefulness of a distributed community-based syndromic surveillance system: a pilot qualitative evaluation study. BMC Research Notes.

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Presentation transcript:

Reeder et al. Perceived usefulness of a distributed community-based syndromic surveillance system: a pilot qualitative evaluation study. BMC Research Notes :187. CA FETP 27 Sept 2012

Background Distribute Project: Distribute is a community- based, population-level public health information system for syndromic influenza- like illness (ILI) surveillance that displays aggregated, deidentified, public health surveillance data collected from emergency departments (EDs) by state and local health jurisdictions

Currently receives data from 43 health jurisdictions that represents over 50% of the US population and summarizes more than 35% of all ED visits nationwide. currently has participation from all ten Health and Human Services (HHS) surveillance regions, includes data from over one million ED visits each week, and displays updated visualizations of ILI trends in the US on Public and Restricted access web sites.

The Public site provides public access graphs of weekly trends in ratios of ILI syndromes to all ED visits. The Restricted site provides data for data providers from existing surveillance systems at local and state health jurisdictions along with provider-specified syndrome definitions  Table 1 shows selected metadata elements available.

Discription of the Study Conducted at the Washington State Public Health Association Joint Conference on Health on October 11-12, 2010 in Yakima, WA USA. Participants  Table 2. Method: Brief project background  views of graphed data from the Public site and Restricted site (Figure 1)  FGD (n=6) and interviews (n=3) about perceived usefulness of views of graphed data (for those familiar with Distribute) and to discuss how they use surveillance data in their work.  Tabel 3.

Themes were identified from focus group and interview notes by grouping similar responses and creating names and descriptions of the groupings  Table 4. Limitations: restricted time frame for data collection and the regional population from which the sample is drawn

Conclusion Themes can be further explored and used to guide future evaluation and design iterations to improve support for public health surveillance. the application of qualitative methods at a public health practice gathering can be a simple way to solicit feedback for the improvement of a working public health information system. Efforts of this type can be useful in recruiting new users to participate in the system and expand the community of practice.

to maintain local utility existing data providers need not abandon prior syndrome definitions, but rather should submit an additional common definition while continuing to send data aggregated by existing syndrome definitions that have local meaning. Use of Syndromic surveillance data: as early indicators of outbreaks; to triangulate disease prevalence in conjunction with other data sources; and, to aid decision-making for interventions that help contain outbreaks.

Three contexts of use for syndromic surveillance information systems: anticipated threat; present threat; and, metadata related to hospitals, patients, data providers, ED visit counts, etc.

Local coding of two separate syndromes: 1) narrow and specific definition – traditional clinical definition of ILI. 2) broad and sensitive definition – febrile, respiratory & influenza-like syndrome. Pilot: data using locally applied syndromes were better correlated with population-level viral surveillance data.

Utility (perceived usefulness) = the extent to which an information system or its output provides benefit or value. Usability (perceived ease of use) = the degree of effort required to use an information system or its output. Focus of Distribute on the usefulness of the system and its data output. Purpose of the Study: to collect info from public health practitioners regarding their needs and proposed system features to mitigate common informatics risk factors for failed system adoption.