CDC - Blogs - Safe Healthcare – Automating Infection Detection — Improving Surveillance Efforts in an Era of Public Reporting of Healthcare Associated Infections

Automating Infection Detection — Improving Surveillance Efforts in an Era of Public Reporting of Healthcare Associated Infections
Categories: Infection Control in Healthcare, NHSN

November 9th, 2010 4:00 pm ET -

IMAGE
http://blogs.cdc.gov/safehealthcare/files/2010/03/scottfridkin.jpg
Dr. Scott Fridkin
Author – Dr. Scott Fridkin
Deputy Chief of Surveillance Branch
CDC’s Division of Healthcare Quality Promotion

A tenet of public health practice is that public health surveillance systems evolve in response to ever-changing needs of both society in general and the public health community in particular. In the case of healthcare-associated infections (HAIs), the needs of patients, providers, other consumers, and payors of healthcare have become drivers of recent evolution—although sometimes they are driving in different directions. Prior to this shift, objectives of HAI surveillance have been to provide local data for local action combined with the facilitating smart policy based on national HAI trends. Recently, the needs of HAI surveillance have changed to include state-wide tracking; state-wide, regional, or national prevention assessments; and importantly, public reporting of facility-specific HAIs.

It has been challenging to revise the system to meet these newer objectives, but success has occurred as national and state summary statistics have been published for some HAIs. However, as reporting of certain HAIs becomes mandatory for facilities to receive payment as part of CMS’s IPPS, two critical issues arise. First, there is a risk of losing the balance between the burden of data collection and the benefit of having local data for action. Programs with scarce resources risk spending substantial time on surveillance and less time using data to evaluate HAI efforts. Secondly, NHSN methodology includes inherent differences in the way infection prevention teams implement NHSN operations; despite tremendous efforts at standardizing case finding approaches and applying standardized definitions, there remains subjectivity.

This subjectivity has been highlighted by recent research funded through the CDC’s Prevention Epicenters Program. Published by Dr. Mike Lin and colleagues in the Journal of the American Medical Association today, investigators demonstrated that the agreement between central line-associated bloodstream infection (CLABSI) rates obtained by infection preventionists and a proxy rate obtained by applying an algorithm to electronic data varied from excellent to poor across medical centers. Importantly, the authors conclude that the inter-institutional variability is due to infection preventionist subjectivity; this variability likely explains some of the differences in CLABSI rates between facilities.

These findings are critically important. First, when used for public reporting or comparing facilities, eliminating variability is ideal to level the playing field and allow meaningful interpretation of the data. Secondly, relying on proxy measures obtained through an automated process would free up valuable time for infection preventions to focus on interpreting data and promoting infection prevention activities.

Although, the infection prevention community will unlikely rely completely on proxy measures, if some HAI measures could be automated, reliably applied across all facilities, and remain credible to the provider community (and other drivers of HAI surveillance), then many more objectives of HAI surveillance can be fulfilled. Towards this end, NHSN is committed to exploring the utility of algorithmically detected CLABSI and moving to full scale implementation once method credibility reaches critical mass and once the technology is widely available in healthcare. Support and publication of further research like Dr. Lin should help the field move in this direction.

full-text and related information:
CDC - Blogs - Safe Healthcare – Automating Infection Detection — Improving Surveillance Efforts in an Era of Public Reporting of Healthcare Associated Infections


Prevention Epicenter Program | CDC Infection Control in Healthcare
Prevention Epicenter Program | CDC Infection Control in Healthcare


JAMA, the Journal of the American Medical Association, a weekly peer-reviewed medical journal published by AMA
JAMA, the Journal of the American Medical Association, a weekly peer-reviewed medical journal published by AMA