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To perform this unique analysis, the team built a new cloud-computing system, Philips IntelliSpace Epidemiology, designed to collect clinical data from a hospital’s data feeds to trace a patient’s journey and encounters within the hospital, which is then overlayed with genetic data from the bacteria of the infected patient. This unique solution enables IP teams to establish likely transmissions to identify potential modes of transmission, all while keeping health information protected and secure.
Using the technology of IntelliSpace Epidemiology, the authors of the study uncovered 34 clusters of HAIs – of which only one had been previously suspected. The 33 unsuspected infection clusters not only represent transmissions not previously identified, but also missed opportunities to intervene and potentially stop HAIs from spreading. The authors concluded the new Philips IntelliSpace Epidemiology solution could enable clinicians to intervene and interrupt the chain of infection transmissions, potentially resulting in fewer infections. The algorithm’s inclusion of data from genomic sequencing of infecting bacteria, a unique feature of IntelliSpace Epidemiology, may help clinicians more accurately map and trace the movement of distinct bacterial strains that otherwise would appear to be similar.
In developing IntelliSpace Epidemiology, scientists at Philips studied slight changes in a bacteria’s genetic code. Bacteria of a single species from a single patient collected over a number of days differed only slightly in their genetic code due to normal bacterial mutation. But bacteria of the same species found on different patients showed much larger variations. Using this analysis, the researchers were able to define thresholds to determine related vs. unrelated bacterial strains. From these genetic data, integrated with other hospital-derived epidemiologic data, IntelliSpace Epidemiology aims to determine if a new bacteria isolated from a patient should be included or excluded in a grouping (or cluster) of related infections. This new method of combining diverse and rich clinical and genomic datasets in order to track and help control HAIs is termed Precision Infection Prevention in recognition of the hospital infection prevention team’s role in preventing HAIs.
Key implications of the study and article conclude:
To read the paper in its entirety, visit Hospital Control & Epidemiology. More information on Philips IntelliSpace Epidemiology is available here.
[1] Klevens RM, Edwards JR, Richards CL, Jr, et al. Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep. 2007;122(2):160–166.
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September 06, 2021
- By Henk van Houten
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Global (English)By clicking on the link, you will be leaving the official Royal Philips Healthcare ("Philips") website. Any links to third-party websites that may appear on this site are provided only for your convenience and in no way represent any affiliation or endorsement of the information provided on those linked websites. Philips makes no representations or warranties of any kind with regard to any third-party websites or the information contained therein.
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