Protecting Patients and Supporting Staff: Health Care Alarm Analytics Reduce Fall Risk

Quick Summary

Falls are a serious and widespread problem for health care systems and patients, with physical injury to the patient as the most detrimental outcome. It’s estimated that up to 40% of fall events result in at least one type of physical injury. 

Beyond physical harm, patients may experience psychological side effects, such as increased anxiety about falling in the future. In many cases, a fall event extends the patient’s time in the hospital, which can be demoralizing and emotionally disruptive.

When a hospital patient falls, a number of negative consequences for hospitals and health systems occur. When a fall necessitates a lengthier hospital stay for the patient, the hospital must often absorb the associated additional costs, especially if the fall is considered preventable. Importantly, a hospital’s reputation may also be diminished over time as a result of frequent or serious patient falls. 

Because there are a number of potential contributing factors for falls, including the patient’s condition, medications, and the physical environment, it can be difficult to determine how and why falls happen, as well as best practices to prevent them in the future. 

That’s where multi-source alarm analytics can help. 

Optimized analytics empower health systems to decrease the likelihood of fall events and to facilitate prompt and effective care when falls do occur. 

Learn more about how Lone Star Communications’ multi-point alarm analytics can bring together data from various sources to help with decision-making to improve patient care and even prevent falls from occurring in this video.

Minimizing Falls: Analytics Make It Possible

Multi-source alarm analytics compile a hospital’s many data sources, organizing statistics into a usable format. Health systems then use this information to understand better what’s happening with patients and staff in day-to-day operations. In the case of falls, alarm analytics allow hospital systems to make essential connections between adverse events and their respective causal factors, ultimately improving decision-making. 

Multi-source alarm analytics can significantly reduce fall risk. The key to successfully addressing damaging and costly falls is understanding how to utilize analytics during falls and before and after a fall event.

Responding to a Fall Event: Prompt, Personalized Care

State-of-the-art, AI-powered visual sensor technology allows healthcare teams to detect fall events faster than ever, with fewer false alarms. Implementing alarm analytics to reduce distraction effectively improves response times, enabling healthcare providers to respond to real-time fall events.

Analytics facilitate prompt and effective care during fall events. However, analytics are even more powerful and valuable before and after a fall. Analytics enable forensic research that helps organizations better understand the causes of fall events. Health systems can then use these analytics to make informed patient care, protocols, and rounding decisions.

As health systems endeavor to prevent fall events altogether, they must prioritize effective, analytics-driven strategies to:

Determining Which Patients Are At-Risk of Falling

The first step in fall prevention is to identify the patients who are most at risk. Ideally, this crucial data would be disseminated to managers just before a staff huddle. 

For example, assessing the volume of nurse call alarms by bed can quickly show staff which patients are generating the most alarms and which are most at risk of falling. A customizable live view of nurse call alarms allows filtering by location, time, or priority. 

Incorporating analytics into shift huddles can help care teams: 

Because multi-source analytics are updated in real time, staff members can gain insight into patient activity from the prior shift or as recently as the last hour. 

Healthcare teams can filter data by priority to hone in on a particular type of alarm. For example, looking specifically at bed exit alarms over the previous shift gives healthcare teams a more detailed and nuanced view of patient activity within a unit. By utilizing real-time data, healthcare providers can identify patients at risk for a fall and proactively address their needs.

Optimizing Rounding Activities to Increase Safety and Efficiency

Assessing longer-view alarm and patient activity data helps nursing management understand behaviors and better plan rounding activities. 

For example, nursing staff can get a macro view by filtering a year’s worth of data by alarm priority, location, and time. Trending data over a year provides insight into key times throughout the day when the unit regularly experiences increased call volume. 

Healthcare systems can then tailor their rounding recommendations to help their teams proactively round during the hour before peak alarm activity. Additional analytics-driven measures, such as staggering shift times, adjusting medication schedules, or calling for all hands on deck during high-volume times, can be implemented to benefit patients and staff. 

To decrease the rate of fall events, staff may choose to analyze data collected for bed exit alarms. In many cases, bed exit alarms occur at different times of day than other alarms. By identifying times when bed exit alarms occur in high volume, healthcare teams can better pinpoint the times of day patients are most at risk of falling.

Assessing Analytics After Falls: Root Cause Analysis

When falls occur, multi-source alarm analytics can help health systems understand precisely what happened and why.

Root cause analysis allows managers to use forensic research chronology reports to correlate multiple data sources over a specific period. These reports identify which factors put patients at risk and determine the most beneficial preventive action types. 

Root cause analysis breaks down historic data in several helpful ways: by bed, by day, or by time. This allows managers to efficiently and comprehensively review events from all sources to identify patterns and probable causes of falls. 

When considered in tandem with patient monitoring data, nurse call data, for instance, captures an accurate picture of the events leading up to a fall. These combined analytics provide a view into the physiological events and the patient’s response to those events. 

Analytics-driven investigative reporting tools make forensic research easier by correlating multiple data sources — including real-time location systems (RTLS) and staff notifications — to determine what factors contributed to an event and detail subsequent actions taken by staff and patients. 

Improving Outcomes for Patients, Staff, and Hospital Systems

Multi-source alarm analytics are transforming how healthcare teams address the very real problem of frequent and dangerous patient falls. Not only does sensor technology allow staff to respond appropriately and promptly to fall events, but concurrently collected data can be used to reduce fall risk in many ways by:

While we may not be able to prevent falls completely, there are steps we can take to protect patients and support staff better. Integrating multi-source alarm analytics into our hospital systems results in better outcomes for nurses, patients, and hospitals. Lone Star Communications can partner with you to help you achieve these results.

Joel Coombs is a seasoned sales executive at Lone Star Communications, leading the charge in the Advanced Technology and Professional Sales Division. With an impressive track record spanning over 30 years, Joel brings a wealth of experience in sales and marketing leadership, particularly within the healthcare and public sector industries.

Throughout his career, Joel has cultivated strong relationships with manufacturers, systems integrators, and channel partners, specializing in advanced technologies tailored for mission-critical applications. His deep understanding of client needs, coupled with his strategic insights, has consistently propelled him to deliver exceptional results.