For years, companies have focused on how accurate their real-time location systems are. “We’re six-feet accurate,” some say. “We’re room-level accurate,” others say. Yes, accuracy is important. But when companies share this information, they’re only telling you half the story.
Accuracy is measured on two axes. The x-axis represents the margin of error of the system—that six-foot or room-level accurate statistic companies are eager to advertise. The y-axis refers to the confidence in that measurement—how often is the system six-foot or room-level accurate? Companies tend to gloss over the confidence measure because most RTLS are as accurate as they claim only a small percentage of the time.
In this post, we’ll unpack what confidence means and explain why it matters just as much as margin of error when it comes to achieving a maximum return on investment for an RTLS.
What does ‘confidence’ in RTLS mean?
A game of darts is a good analogy for the accuracy-versus-confidence discussion when it comes to RTLS. If you ask the average darts-thrower whether they can hit a bullseye if you give them 100 throws, that person will say, “Yes.” If you ask that person how often they can hit a bullseye in 100 throws, the answer—if they’re being honest—will be, “not a lot.” The difference between an average dart thrower and a professional is that second number: confidence. Confidence is also what distinguishes an average RTLS from one that will deliver the best ROI for a hospital.
You can chart this phenomenon on a graph, like the one pictured below. You’ll see the margin-of-error (x) and confidence (y) axes noted. The two curves drawn on the graph are called cumulative error probability curves; they show how often it’s likely for RTLS to be accurate within a particular margin of error. The higher a curve moves into the top left corner the better—that suggests the smallest margin of error with the highest degree of confidence. According to the chart below, the system represented by the blue line performs better than the system represented by the red line.
Why does high confidence matter for RTLS?
Let’s return to the dartboard analogy. Imagine you’re an average darts-player throwing 100 darts, and all your shots are tracked using a computer. A handful of red dots representing your shots fall within the bullseye, but dozens are scattered randomly around the dartboard.
If you place that scatter plot over the footprint of a hospital, you get a rough estimation of how the typical RTLS operates.
There’s an asset with a tag on it that’s being tracked by the system. The asset could be any one of those red dots. Every time you ask the system, “Where is my asset?” it will return one of these red dots at random. As you can see, the dots can be found in several different rooms and corridors.
If your hospital’s goal in using RTLS is simply to find assets, then this relatively low-confidence accuracy may be acceptable. But the greatest ROI RTLS can provide is by increasing visibility into your mobile equipment fleet to understand its utilization and optimize its workflow. This requires a high level of precision across your entire hospital.
Here’s an example: Imagine your hospital has a clean storage room and a dirty storage room right beside each other. The clean storage room has a minimum replenishment level for IV pumps at five units. Meanwhile, next door, the dirty storage room is at capacity with 20 IV pumps. If your RTLS is not room-level accurate with high confidence, you can bet that the tags on some of the dirty storage room’s IV pumps will show up as being in the clean storage room, misleading you as to how many clean IV pumps you have on hand. You don’t realize the problem until you get a frantic call from a nurse saying they’ve taken the last IV pump from the clean storage room, so there’s nothing available for the next staff member who needs one. This can quickly escalate into a major problem.
High confidence doesn’t have to mean high cost
Historically, RTLS offering both a low margin of error and high confidence would be very expensive. Examples include legacy systems like infrared or ultrasound RTLS, which require heavy infrastructure in every part of the hospital, including patient rooms. The alternative is a low-budget option, such as a Bluetooth Low Energy RTLS that operates on a Wi-Fi network. Although these RTLS are less expensive than legacy systems, they suffer from a low degree of confidence. In short, they sell you on a low margin of error but fail you on confidence.
To achieve maximum ROI for your hospital’s RTLS, you need the best of both worlds: low margin of error and high confidence. And you probably want a third world: an affordable cost. Cognosos delivers on all counts.
Using a proprietary artificial intelligence system housed in the cloud that uses machine learning algorithms, Cognosos’ RTLS can deliver enterprise-wide, room-level accuracy with extremely high confidence. The system runs on unobtrusive, lightweight infrastructure—translating to a much lower overall cost of ownership than legacy systems for hospitals.
Want to learn more about Cognosos’ industry-leading technology and how it can help your hospital? Watch our webinar which examines the key components to building a strong finance driven RTLS business case, as well as the larger impact that increasing equipment visibility delivers to your facility.
