What it is
A dashboard for nurse managers that shows, unit by unit, which patients are at high risk for a hospital-acquired pressure injury right now. It uses readings from two bedside sensors instead of relying only on periodic risk scores and manual checks.

How patients are flagged
A patient is added to the high-risk list automatically when either of these is true:

• Missed turn: a triaxial accelerometer shows the patient hasn’t been repositioned in more than 4 hours.
• Early tissue damage: subepidermal moisture (SEM) at the sacrum or either heel reads above 0.6. A high SEM reading can show damage under the skin before it becomes visible.

Both limits can be adjusted to fit local protocols.

What the manager sees

• Unit tabs: Medical-Surgical ICU, Cardiothoracic ICU and Stepdown, each with a count of high-risk patients.
• Summary counts: patients on the list, how many were flagged for a missed turn, for a high SEM reading, or both, and how many have been rounded on today.
• Patient list: name, MRN and room, what triggered the flag, time since the last turn, and SEM readings at each site, with values over the limit in bold.
• Filters: show only patients flagged for a missed turn, a high SEM reading, or both.

Leadership rounding checklist
Clicking a patient’s name opens a short Yes/No checklist for nursing leadership rounding:

1. If the patient is turned laterally, is the sacrum offloaded?
2. Are the heels offloaded?
3. Are all medical devices positioned away from the patient?
4. Is the patient’s skin clean and dry?

Any “No” is flagged to address with the bedside nurse. Once saved, the patient’s row shows the time and whether all four items were met.

Why it matters
It turns continuous sensor data into a short, ranked list of patients who need attention and a simple step for nursing leadership to follow. That closes the loop from “the sensor detected risk” to “a leader confirmed prevention is in place.”

Current status
This is a clickable prototype with fictional sample patients. It isn’t connected to real sensors or the EHR, and rounding answers aren’t saved yet.