Excessive Resident Falls in Long-Term Care Facilities
This paper addresses the problem of excessive resident falls in long-term care facilities in the United States. A fall is defined as, “an event which results in a person coming to rest on the ground, floor or lower level” according to the World Health Organization in 2018 (World Health Organization, 2018). Researchers indicated that the prevalence of falls and the risk factors associated with resident falls in nursing facilities were significant (Rosen, Mack & Noonan, 2013). “Every 11 seconds, an older adult is treated in the emergency room for a fall; every 19 minutes, an older adult dies from a fall” (National Council on Aging, 2015, p.1). The most prevalent adverse incident reported by nursing staff in a facility was a resident fall (Wagner, Capezuti, Clark, Parmelee & Ouslander, 2008). Older adults, over the age of 65 years old, suffered the most significant number of fatal falls in the United States (National Council on Aging, 2015). The most common cause of injury and cause of death in Americans over the age of 65 was a fall (Center for Disease Control, 2016). The significance of the number of elderly falls was damaging to our economy (Florence, Bergen, Atherly, Burns, Stevens & Drake, 2018). The Center for Disease Control estimated in 2016, medical costs exceeded over fifty billion dollars for the treatment of adults, over the age of 65, who suffered a fall (CDC, 2016). Patients, families, Medicare and Medicaid have absorbed the overall financial costs of falls (Stevens, Corso, Finkelstein & Miller, 2006). Considering the predominance and the risk factors associated with resident falls in long-term care facilities, future interventions, technologies, and study are necessary to resolve this problem.
Review of Relevant Research
This literature review covers three topic areas that resulted from the review process. These topic areas are; various risk factors associated with falls, inconsistencies of incident reporting methods and current and future fall detection devices. This review focuses mainly on the relevant research application of literature pertaining to the problem of falls in long-term care facilities, for adults over the age of 65.
Researchers have shown that various risk factors predicted the fall risk of older adults living in long-term care facilities. Wallis and Campbell (2011) indicated that poor sensory input, physical disabilities, cognitive impairment, and infections caused falls in facilities (Wallis & Campbell, 2011). Also reported as major risk indicators were: extrinsic environmental factors such as unsafe flooring, poor lighting and the type and layout of furniture in a facility (Wallis & Campbell, 2011). Results reported in a study that analyzed architectural layouts of long-term care facilities and potential fall risk indicated that larger facilities with over 148 residents experienced more falls than smaller facilities (Hill, Nguyen, Shaha, Wenzel, DeForge & Spellbring, 2009). Researchers also studied the timing of falls during the change of shift of caregivers and nurses. Hill et al. (2009) reported that from 2 p.m. to 4 p.m., the incidence of falls increased (Hill et al., 2009).
Polypharmacy and psychotropic drug usage were also found to have increased the fall risk in older adults living in an institution (Sterke, Verhagen, van Beeck & van der Cammen, 2008). Research cited in a meta-analysis study in 2009 that antihypertensives, antipsychotics, anti-depressants, sedatives, and hypnotics increased the risk of falling by fifty percent for seniors (Woolcott, Richardson, Wiens, Patel, Marin, & Khan, 2009). The risk factors associated with falls in long-term care facilities and the number of falls reported by various modalities were critical to fall analysis. Inconsistencies associated with incident reports were essential to consider for data collection and root cause analysis of falls.
Inconsistent Incident Reporting Methodology
Wagner et al., (2008) found many inconsistencies in the reporting methods of falls across institutions (Wagner et al., 2008). Narrative reporting in nursing facilities lacked structure and did not provide consistent fall data for trending outcomes and post-fall analysis (Wagner et al., 2008). Researchers conducted focus groups of nurses in skilled nursing facilities, who indicated that incident reporting tools for documented falls did not address environmental causes for falls (Hill et al., 2009). Another issue with trending data of falls indicated that fall incident reports lacked organization in nursing facility documents such as; chart notes, general medical records, calendars, subject interviews and incident reports (Sterke, Huisman, van Beeck, Looman & van der Cammen, 2010).
Furthermore, a study indicated that incident reports from nursing facilities were questionable in their validity and number, due to possible employee personal interpretation of the definition of a fall (Haines, Cornwell, Fleming, Varghese, & Gray, 2008). In a review of incident reporting methods, a qualitative investigative report noted that employee blame and staff time pressure were most often reported as the reasons for staff to have not filed an incident report (Haines et al., 2008). Incident documentation of falls in long-term care facilities, due to various inconsistencies in reporting by employees, families, and the design of reports stipulated the necessity of standardized reporting systems for the documentation of falls in the United States. However, in recent years, fall detection technology and devices have aided practitioners with incident documentation, awareness, and safety analysis.
Fall Detection Devices and Alarms
Fall detection devices vary throughout the long-term care system by factors including; cost, demographics, and viability of each reporting system. In 2018, the California Department of Social Services approved camera monitoring in the residents’ bedrooms in long-term care facilities (California Department of Social Services, 2018, section 5). A pilot study by an engineering team at the University of California Berkeley in 2017, incorporated a “Safely You” camera system in a long-term care facility. This system analyzed falls from video monitoring in forty residents’ bedrooms. Immediate fall detection from camera software algorithms and motion movement alerted staff of a resident fall in the bedroom (Bayen, Jacquemot, Netscher & Noyce, 2017). The falls in the pilot study were recorded twenty-four hours a day, seven days a week, over three months. The data collected was reviewed by occupational therapists, who made recommendations for improved safety and documentation of unwitnessed falls. As a result of this input, the facility experienced a reduction of 40 percent in emergency hospitalizations due to falls (Bayen et al., 2017). Detection of unwitnessed falls of the cognitively impaired seniors, through the use of the ”Safely You” video system, continued to reduce hospitalization and contributed to more accurate incident reporting (Bayen et al., 2017). Researchers indicated that artificially intelligent, camera-based systems were vital as they detected falls quickly and reduced the time the older resident was on the floor (Ayotebi, Stewart, & Sampson, 2015). The modern usage of smartphones in conjunction with smartwatches detected movement before and during a fall. The watch and phone, while worn and connected to blue tooth technology, resulted in reports of less false negatives and gave accurate reports of body movement (Casilari & Oviedo-Jimenez, 2015). Numerous types of fall detection devices have existed over the last 20 years, yet more ongoing research is needed to assess which is most effective (Chaudhuri, Thompson & Demiris, 2014). Chaudhuri et al., (2014) distinguished types of fall detection devices into two types: wearable and non-wearable devices. Wearable devices helped to detect acceleration of the fall. Non-wearable systems, such as a stationary camera, were reliable because residents in the facility were not required to initiate the device or remember to wear the device (Chaudhuri et al., 2014). As a result of technological progress and the viability of advanced detection systems, newer safety monitoring options for the prevention of falls in long-term care exist. The importance of these advancements lies in addressing the problem of excessive falls for residents.
Importance of the Problem
It is essential to solving this problem of excessive falls in long-term care for a variety of reasons. Solving this problem is critical, according to trends in demographics, which show increases in the older adult population due to rising life expectancy throughout the United States. Life expectancy for adults has been predicted to reach 80.7 years for men and 85.2 years for women by 2033 (Gelbard et al., 2014). Leaving this issue unresolved would increase the number of excessive falls as the population ages, and thus, a higher mortality rate (Costello & Edelstein, 2008). Policymakers and economists also considered the importance of fall reduction and its impact on the economy. If this issue were left unattended, it would expand costs for Medicare, Medicaid, and private pay. Therefore, policy advocates look to research data and analysis on elderly falls as critical for the allocation of resources (Carrol, Delafuente, Cox & Naranyanan, 2008). According to the Center for Disease Control, 2016, elderly falls cost the United States over fifty billion dollars per year; however, there are intangible costs to the older adults themselves such as; pain and suffering, fear of injury, and quality of life (CDC, 2016). “Older adult falls are increasing and, sadly, often herald the end of independence,” according to CDC Director Tom Frieden (CDC, 2016, para. 4). Improvements in incident reporting, advancements in technology for detection of falls and further analysis of fall risk factors are needed to resolve this problem of practice for residents living in long-term care facilities, in the United States.
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