A pilot study using a medical internet of things (IoT) system to reduce bed falls in acute-care hospitals shows promise for protecting a vulnerable patient population.
The integration of IoT technology in medical workflows has received increasing attention across the industry. In its survey "The Internet of Things—Connecting the Health Ecosystem," IDC Health Insights reported that 73.3% of providers and 84% of payers claim to be “prepared” or “very prepared” to make use of IoT.
A report released last month found that 60% of healthcare organizations are using the technology worldwide, driven in part by cost savings. But security concerns remain the biggest hurdle for healthcare organizations considering widespread application of the technology, according to another report last month.
Results from the new pilot, published in the Journal of Medical Internet Research, showed a significantly higher positive predictive value (PPV) for monitoring alerts using a sensor pad under a patient’s mattress to alert nurses when patients attempted to leave their beds. Bed falls represent a major patient safety issue, adding approximately $3,500 to $14,000 to hospital stays, according to the report, and typical alerting technologies also produce large numbers of false alarms.
The study’s authors cited studies showing that the low PPV of typical physiologic monitors leads to very low nurse response to alarms. In contrast, the study measured the PPV of its IoT system at 62.1%, and nurses reset the alarm about 46 seconds after it went off, on average. Over the course of the pilot, none of the 91 patients using the system experienced a bed fall.
Nurses in the pilot received alerts on their mobile devices, which meant they did not necessarily need to be at a nursing station to respond to a patient attempting to leave a bed unassisted but instead could be anywhere on the ward. Part of the increased speed of response likely stemmed from nurses’ greater flexibility in fine-tuning the system, the authors noted. Nurses could opt to get notifications before patients actually left the bed, for example, if they were moving around, or sitting up.