Healthcare Business and Technology: Using Internet of Things Data to Reduce Fall Risk

Keeping patients safe by preventing falls is a persistent challenge. It’s estimated that between 700,000 to 1 million hospitalized patients fall each year. More than one third of these falls cause injuries. Falls also lead to hospital stays that are six to 12 days longer on average. The average cost to a hospital of a fall with injuries (which is not reimbursed by CMS) is $14,000. In addition, falls also have the potential to lead to lawsuits related to malpractice or negligence. These data points all point to the growing need for hospitals to reduce fall risk.

It’s important to look at the culprit. Why do patients fall in the first place?

There are five main risk factors:

• Recent falls: Patients with a previous fall history are often at a greater risk of falling again.
• Leg weakness and gait problems: Loss of balance or atypical gaits can be age- or disease-related. Bedrest and lower levels of activity during hospitalization can also lead to weaker lower limb muscles.
• Disorientation or impaired judgment: Confused or agitated patients may overestimate their ability to be mobile.
• Medication: Medication, especially sedatives, may cause dizziness and unsteadiness in patients, particularly if they’re not used to taking them at home.
• The need for toileting: Medication and intravenous fluids given in the hospital can increase the need for urination. Research shows that over 50% of all hospital inpatient falls were elimination-related[i].

Patient overestimation of ability appears to be common. In the research referenced above, “many patients did not use the call light because they believed that they did not need assistance.” The study also found that “elimination-related falls increased the risk of suffering a fall-related injury, even after controlling for gender and mental status.”

An obvious solution is for nurses to check on patients more frequently and help them walk to the bathroom before there’s an urgent need. In many hospitals, nurses are required to check on patients in hourly rounds. One of the major components of rounding is to ensure that patients aren’t getting up by themselves to go to the restroom.

In theory, implementing hourly rounding makes perfect sense and is an easy concept, but are nurses actually rounding when they’re supposed to? Do you know which staff members forget? Do you know which patients haven’t been checked on in over an hour? Most hospitals have no way to answer these questions. However, the IoT is helping bring this information to light, and is a simple and effective way to help reduce fall risk.

IoT sensors can track which patient room a provider is in and at what time. The sensors gather the data, which is used to identify patients who haven’t been checked on in real-time. Predictive analytics can then trigger an alert to clinical leadership that a fall may occur, before it happens and when it can still be prevented.

With this clinical intervention data, hospitals can identify patterns and causes of provider behavior, and drive interventions that will reduce risk and lead to better clinical outcomes. Better outcomes and fewer falls will not only decrease costs from unreimbursed falls, but should also increase patient satisfaction.

Click here to read the original article in Healthcare Business and Technology.

Chris Hermann, MD, is the founder and CEO of Clean Hands-Safe Hands. He has a PhD in Bioengineering, an MS in Mechanical Engineering and a BS in Biomedical Engineering.

[i] Hitcho, E. B., Krauss, M. J., Birge, S., Claiborne Dunagan, W., Fischer, I., Johnson, S., Fraser, V. J. (2004). Characteristics and circumstances of falls in a hospital setting: a prospective analysis. Journal of general internal medicine, 19(7), 732–739. doi:10.1111/j.1525-1497.2004.30387.