If a newly-admitted patient contracts an infection, do you know if they caught it at your hospital, or in the community prior to admittance? If a unit has a C. diff outbreak, do you have a way to identify which providers interacted with those patients? Or which ones used soap and water? What about the next patient with a cough who ends up getting diagnosed with TB without being in an isolation room? Do you know which providers were exposed to that patient? Do you know which ones were in the rooms for a long period of time?
Our Concierge Contact Tracing™ service leverages our IoT network of sensors and advanced analytics capabilities to provide unprecedented insights into the activity of your healthcare workers. Here are real world examples from two of our hospital partners.
Hospital A had a patient test positive for CLABSI after being admitted for a few days. The hospital wanted to determine whether they could have prevented this infection or not. Of course, it’s impossible to pinpoint the cause with absolute certainty, but our system provided the hospital with insights that allowed them to feel comfortable that hand hygiene was not the likely cause.
Our team analyzed the data from the days leading up to the diagnosis and used our Performance Bubble Plot™ to identify clinical behaviors. Each circle represents a badged clinician that entered that patient’s room. The size of the circle shows the number of hand hygiene opportunities – the larger the circle, the more times they entered and exited the room. The color of the circle shows the hand hygiene performance of the individual provider, over those days, in that specific room. Darker green represents higher performance and darker red means lower performance.
The above shows that the clinicians most involved in this patient’s care had a very high rate of hand hygiene. Sure, there were a handful of providers that were in and out of the room a few times that didn’t do as well, but there’s no smoking gun here. The hospital concluded that this case of CLABSI was likely not caused by poor hand hygiene.
Next, hospital B had a C. diff patient in one room, and shortly thereafter, the patient in the room next door also tested positive for C. diff. The hospital wondered if they could have prevented this situation. The Performance Bubble Plot below shows hand hygiene performance for all the clinicians that cared for both patients over a three-day period.
This shows that a few of the clinicians most involved in this patient’s care had a low rate of hand hygiene. There’s no way to prove that they contributed to the second patient contracting C. diff, but it allowed the hospital to see that there was room for improvement.
While this data is useful after the fact, it’s even more valuable when it’s used in a predictive, forward-looking way. When our system detects data that’s outside of the norm – either in a positive or negative direction – it can send a text message to a unit manager or Infection Preventionist.
In the case of big green circles, this gives managers a reason to walk over and pat those providers on the back and tell them they’re doing a great job. In the case of big red circles, they can instead do targeted observation to find out what’s going on and intervene as appropriate. This can help stop problem situations before they start. And it’s all based on real-time data.
If you’d like to explore how our system typically doubles hand hygiene performance rates…and has reduced HAIs by between 45% and 81% in 100% of customers following our process for 6 months…here’s a brief video about how it works. Or here’s a white paper about The 4 Data Points You Need To Reduce HAIs.
