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


Quick Summary

  • Patient Falls are a significant and all-to-common problem for acute care hospitals. Fall events can result in physical injury and psychological distress for patients, as well as increased costs and damaged reputations for hospitals.

  • Multi-source alarm analytics can be used in hospitals to significantly reduce fall risk before an event occurs. Analytics help teams identify patients who are at a high risk of falling, as well as the most advantageous times to round.

  • Health care Analytics are also used after a patient fall event to review what happened and identify patterns and probable causes of falls. Investigative reporting tools make forensic research easier by correlating multiple data sources and detailing the actions taken by staff and patients.


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 better understand what’s happening with patients and staff in day-to-day operations. In the case of falls, alarm analytics allow hospital systems to make important connections between adverse events and their respective causal factors, which ultimately improves decision making. 

 

Multi-source alarm analytics can significantly reduce fall risk. The key to successfully addressing damaging and costly falls is to understand how to utilize analytics during falls, as well as 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 before — and with fewer false alarms. Implementing alarm analytics to reduce distraction effectively improves response times, allowing healthcare providers to respond to fall events in real time. 


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

 

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

 

  • Identify high-risk patients

  • Identify the most effective times and ways to round


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 prior to a staff huddle. 

 

Assessing the volume of nurse call alarms by bed, for example, 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 is also possible, allowing filtering by location, time period or priority. 


Incorporating analytics into shift huddles can help care teams: 


  • Effectively identify patients at risk of falling 

  • Manage a patient’s needs appropriately (e.g. implementing one-on-one care or moving a patient)

  • Manage staff resources

  • Plan admissions, discharges and transfers


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


Optimizing Rounding Activities to Increase Safety and Efficiency


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

 

Nursing staff can get a macro view, for example, by filtering a year’s worth of data by alarm priority, location and time. The ability to trend data over the length of 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 prior to 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. 

 

In an effort to decrease the rate of fall events, specifically, 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 alarm times occur in high volume, healthcare teams are better able to pinpoint the times of day patients are most at risk of falling. 


Assessing Analytics After Falls: Root Cause Analysis


When falls do take place, multi-source alarm analytics can help health systems better understand precisely what happened and why.


Root cause analysis allows managers to correlate multiple data sources over a specific period of time by using forensic research chronology reports. These reports get to the crux of which factors put patients at risk and determine what types of preventive action will be most beneficial. 


Root cause analysis breaks down historic data in a number of helpful ways: by bed, by day or by time. This allows managers to easily and comprehensively review a sequence of events from all sources, so that they can identify patterns and probable causes of falls. 

 

Nurse call data, when considered in tandem with patient monitoring data, for instance, captures a true picture of the events leading up to a fall. These combined analytics provide a view into the physiological events that occurred as well as 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 a number of ways by:

 

  • Calling attention to which patients are at risk of falling

  • Helping health systems understand patient behaviors for more purposeful rounding

  • Empowering teams to adjust schedules and manage resources

  • Decreasing overall noise and stress levels


While we may not have a way to completely prevent falls, there are steps we can take to better protect patients and support staff. Ultimately, integrating multi-source alarm analytics into our hospital systems results in better outcomes for nurses, patients and hospitals and 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.

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