I believe that as infection preventionists (IPs), we are first and foremost patient advocates. Ensuring patient safety lies at the core of our passion in preventing HAIs. A basic tool of infection prevention is surveillance. Efficient electronic surveillance is critical to surveillance and prevention efforts.
Electronic surveillance has been around for quite a while. I hesitate to share this, but when I started my career in infection prevention, I was given a typewriter and had to learn the proper format for writing a memo! When the first desktop computer was delivered to the hospital, a crowd watched as it was taken out of the box.
Fast forward to the present: There remains a very wide continuum of electronic HAI surveillance capabilities in hospitals nationwide. This continuum ranges from a very manual, time-consuming process to a hybrid of semi-automated algorithmic identification of potential HAIs through the use of electronic surveillance systems, leaving the IP to make the final clinical determination of whether the infection meets the NSHN definition. We are also seeing some fully automated electronic algorithmic identification, such as Lab IDs.
Electronic HAI Prevention
Within the context of this electronic surveillance capabilities continuum and concomitant gaps, let’s focus on “electronic prevention.” It is important to keep in mind that NHSN surveillance definitions are epidemiologic definitions designed for trending and benchmarking, while clinical definitions are diagnostic and treatment-based definitions.
Take a step back and challenge your current surveillance process. Think about clinical scenarios you would ideally want to know about. Are there any ways in which you can use your existing system to identify patients who are exhibiting the earliest signs of infection or other clinical demise? Whether through an algorithmic approach or more simple alerting process, can you find those patients before they progress to a potentially life-threatening infection?
For instance, can you be alerted about a patient with a device that was admitted 2 days ago and now has an elevated WBC and temperature? Could you be alerted about a patient who, on day 4 of admission, now meets the criteria for systemic inflammatory response syndrome (SIRS) and is also hypotensive? How quickly can you be alerted that a patient just underwent a spinal tap in order to ensure immediate implementation of Droplet Precautions? I know that the pressure to identify NHSN defined infections is critical and time consuming. Just take a step back and think about what indications of early or potential infection a patient might demonstrate.
Infection Prevention has a Large Footprint
What do preventing Healthcare Acquired Conditions (HACs), Early Goal Directed Therapy (EGDT) for sepsis, antimicrobial stewardship, readmission risk reduction, core measure compliance, modified early warning scores (MEWS), and cost reduction have in common? You guessed it: infection prevention - and patient safety. As with infection prevention, the more effective your hospital’s electronic surveillance capabilities, the greater the likelihood for excellent patient outcomes beyond healthcare-associated infections.
You might already feel like you are breathing through a straw just keeping up with all of the surveillance you have now. Why would I bring up these other areas? By the nature of our work, infection preventionists have had to become more knowledgeable about electronic surveillance. We use it every day. Take a look at how you can contribute to improve patient outcomes in the areas I mentioned above.
But keep it simple! I am not suggesting that you attempt to take on additional work outside of your already busy role. Take a few minutes to assess whether patients at risk of poor infection-related outcomes could be more easily identified in a real-time or at least more timely basis. You might provide simple electronic surveillance approaches that had not been previously attempted, resulting in better patient outcomes.
I practiced infection prevention in Pennsylvania for many years. It was not until the state legislature mandated reporting of all HAIs that I discovered that there was a significant problem with non-ventilator related pneumonia. We created our own non-vent pneumonia prevention bundle. This included identification of patients with known pneumonia risks such as immobility, COPD, CVA, history of pneumonia, and surgery. Over time, this approach diminished the non-VAP pneumonias exponentially.
Prevention is the name of the game. Be the squeaky wheel when it comes to updating or acquiring more advanced electronic surveillance capabilities.
About the Author
Tom Jordan, RN, BS, CIC is an Infection Prevention Clinical Program Manager for Sentri7.
Tom previously served as the Director of Infection Prevention for a hospital that was one of the initial customers utilizing Sentri7 Infection Prevention. He brings this rich and practical experience along with his extensive infection prevention knowledge to customers adopting the Sentri7 software.
Mr. Jordan has more than 29 years of experience as a registered nurse during which time he provided clinical services in pediatric intensive care, trauma nursing and employee health. Tom served as Director of Infection Prevention for 21 years in acute care and long term acute care settings. His experience includes working in a large, multihospital system (100+ sites). Tom is a long standing member of the Association for Professionals in Infection Control and Epidemiology (APIC) member, an active SHEA member, has contributed articles to the publications Advance for Nurses and Nursing Spectrum, and has presented at national and regional conferences on a variety of infection prevention topics.
Tom holds a Bachelor of Science Degree in Social Work from the Loyola University and a Nursing Degree from Illinois Masonic Medical Center School of Nursing.
Woeltje, K. F., Lin, M. Y., Klompas, M., Wright, M.O., Zuccottti, G., Trick, W. E., Data Requirements for Electronic Surveillance of Healthcare-Associated Infections, Infection Control and Hospital Epidemiology, Vol. 35, No. 9 (September 2014), pp. 1083-1091