Fall prevention strategy in an emergency department

Mwali Muray (Laurentian University, Sudbury, Canada)
Charles H. Bélanger (Laurentian University, Sudbury, Canada)
Jamil Razmak (College of Business Administration, Al-ain University of Science and Technology, Abu Dhabi, United Arab Emirates)

International Journal of Health Care Quality Assurance

ISSN: 0952-6862

Publication date: 12 February 2018

Abstract

Purpose

The purpose of this paper is to document the need for implementing a fall prevention strategy in an emergency department (ED). The paper also spells out the research process that led to approving an assessment tool for use in hospital outpatient services.

Design/methodology/approach

The fall risk assessment tool was based on the Morse Fall Scale. Gender mix and age above 65 and 80 years were assessed on six risk assessment variables using χ2 analyses. A logistic regression analysis and model were used to test predictor strength and relationships among variables.

Findings

In total, 5,371 (56.5 percent) geriatric outpatients were deemed to be at fall risk during the study. Women have a higher falls incidence in young and old age categories. Being on medications for patients above 80 years exposed both genders to equal fall risks. Regression analysis explained 73-98 percent of the variance in the six-variable tool.

Originality/value

Canadian quality and safe healthcare accreditation standards require that hospital staff develop and adhere to fall prevention policies. Anticipated physiological falls can be prevented by healthcare interventions, particularly with older people known to bear higher risk factors. An aging population is increasing healthcare volumes and medical challenges. Precautionary measures for patients with a vulnerable cognitive and physical status are essential for quality care.

Keywords

Citation

Muray, M., Bélanger, C. and Razmak, J. (2018), "Fall prevention strategy in an emergency department", International Journal of Health Care Quality Assurance, Vol. 31 No. 1, pp. 2-9. https://doi.org/10.1108/IJHCQA-09-2016-0122

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

Falls have a significant impact on society and the literature indicates that individuals over 65 years face a higher fall risk, which is increased once an individual is over 80 years (Clyburn and Heydemann, 2011; Hartholt Oudshoorn, Zielinski, Burgers, Panneman, van Beeck, Patka, and van der Cammen, 2011; Vu et al., 2014). Falling can be a life-altering, harmful event and can have an exponential impact on treatment costs (Vu et al., 2014; WHO, 2007). Falls occur in the community and in hospitals. If a fall occurs while a patient is at the hospital, depending on fall severity, then costs may increase owing to the need for additional tests, consultations with specialists, surgery and medications (Woolcott et al., 2012). Hospital stay is bound to increase, particularly if falls prevent patients from returning home. The patient may have an extended hospital stay as s/he waits for placement in a long-term care setting. By implementing fall prevention strategies, hospital staff can reduce unnecessary healthcare costs, while improving patient outcomes and service quality. Focusing on fall prevention strategies allows hospital staff to adhere to quality and safe healthcare accreditation standards in Canada (Accreditation Canada, 2013).

Objective

Our research documents the need to implement a fall prevention strategy in the emergency department (ED) at a hospital in Northern Ontario, Canada. Our objectives were to identify patients at a high risk of falling and to track ED patient falls incidence. We identify the strategies for preventing falls in hospitals and explain how a fall risk assessment tool is implemented. Two hypotheses were considered:

H1.

There will be no difference in the fall risk among men and women above 65 years in response to the fall risk assessment tool.

H2.

There will be no difference in the fall risk among men and women above 80 years in response to the fall risk assessment tool.

Background

Based on the American National Database of Nursing Quality Indicators (NDNQI), falls include accidents (e.g. when a patient slips on a wet floor); unanticipated physiological falls (e.g. when an individual faints); and anticipated physiological falls (i.e. falls that can be anticipated when a patient’s risk factors are known) (Lach, 2010; Trepanier and Hilsenbeck, 2014). We focus on anticipated physiological falls, which can be prevented by healthcare interventions. Falls vary in type and impact, and may result in no injury, minimal injury or discomfort (pain, cuts, contusions or swelling), moderate harm causing lacerations, bleeding, unconsciousness and often an increased hospital stay owing to fractures, hematomas and major trauma requiring surgery (Hartholt van Beeck, Polinder, van der Velde, van Lieshout, Panneman, van der Cammen, and Patka, 2011; Lach, 2010; Oliver et al., 2010; Roudsari et al., 2005; Vu et al., 2014; Zecevic et al., 2012). Lach (2010) suggests that falls are a leading cause of accidental death in older adults. Other consequences that may be difficult to measure include long-term disability, fear of falling, dependence, premature long-term placement, coping challenges and decreased quality of life (Lach, 2010; Trepanier and Hilsenbeck, 2014; WHO, 2007).

Risk factors

Researchers highlight fall risk factors that are associated with specific individual characteristics and health conditions, including: 65+ years (Alexander et al., 2013); osteoporosis (Hartholt Oudshoorn, Zielinski, Burgers, Panneman, van Beeck, Patka, and van der Cammen, 2011); drinking alcohol (Ministry of Health and Long-Term Care, 2002; Terrell et al., 2009); vision impairment (Lach, 2010; Public Health Agency of Canada, 2014); altered elimination patterns (Terrell et al., 2009); seizure history (Alexander et al., 2013); altered mental status (Fonda et al., 2006); mobility issues (Trepanier and Hilsenbeck, 2014); and previous falls (Ministry of Health and Long-Term Care, 2002). Vu et al. (2014) concluded that women tend to have a higher fall incidence, mostly attributed to their longer life expectancy. Certain medications, including sedatives such as benzodiazepines, increase a person’s fall risk (Terrell et al., 2009) as does polypharmacy (taking numerous medications) (Lach, 2010; Zecevic et al., 2012). Researchers also indicate that patients are at a higher risk when they have more than one potential risk factor (Hartholt van Beeck, Polinder, van der Velde, van Lieshout, Panneman, van der Cammen, and Patka, 2011). Lach (2010) reported that individuals presenting with four or more risk factors had 80+ percent chance of falling within a year.

Falls in hospitals

Researchers found that approximately 30 percent of the general population aged 65 and older falls each year, rising to nearly 50 percent annually for people over 80 years (Lach, 2010; Vu et al., 2014). While falls frequently occur in the community, falls are also the most common adverse event occurring in hospitals (Hutchinson et al., 2009; Mion et al., 2012; Terrell et al., 2009). Among people that may already have a complex medical history, evidence suggests that extended longevity may result in increasing falls and healthcare costs (Clyburn and Heydemann, 2011; Hartholt van Beeck, Polinder, van der Velde, van Lieshout, Panneman, van der Cammen, and Patka, 2011; Lach, 2010; Roudsari et al., 2005; Vu et al., 2014; Zecevic et al., 2012). Patients falling in hospitals is a concerning issue, evidenced by increased patient harm and greater financial resources needed to address unexpected treatment costs and potential litigation expenses (Oliver et al., 2008; Zecevic et al., 2012). Studies show that few patients completely return to their pre-hospital baseline levels after a fall (Roudsari et al., 2005; Zecevic et al., 2012). Patient fall costs in hospital can start at $44,203, which can be $30,696 greater than routine costs (Zecevic et al., 2012). Patient care in hospital can be costly for patients and healthcare system. However, implementing fall risk assessment tools and strategies can reduce these events, and thus improve patient care (Mordiffi et al., 2016; Teh et al., 2015).

Methods

This study was designed using the one-group pre-test/post-test quasi-experiment in which the pretest serves mostly as comparison with the after treatment (fall strategy implementation) results because the pretest typically lacks proper rigor. The fall risk assessment tool was based on the Morse Fall Scale (Morse et al., 1987), a reliable and validated tool, already uses throughout the hospital inpatient services prior to 2014 (except in the ED). With fall prevention strategy implementation, a modified assessment tool equivalent approved for use in outpatient and ED services. This modified tool has been simplified to rapidly assess incoming patients based on the most salient risk factors contributing to in-hospital falls (Figure 1). Patient outcome data allow conclusions to be made about determining the fall prevention strategy’s influence in the ED.

Pre-implementation

Data collected prior to the fall prevention strategy were based on ED medical records and incident reports (n=44) regarding falls between June 2011 to June 2014. While 44 patients in three years may seem minute, the fall prevention strategy emphasized incident reporting as an opportunity for process improvement and learning. Without previously collecting ED patient fall risk data, it is difficult to determine the total population from which 44 patients were extracted. However, fall prevention strategy implementation data will be gathered in the future. By using data sources, such as incident reports, we obtained secondary data, previously collected in the hospital and used them to meet the study’s comparative needs.

Post-implementation

Following the fall prevention strategy, data were collected on all patients above 65 years registered to the ED (n=5,371) through the hospital’s electronic health records (Meditech). Incident report data (n=45) about the patients falling in the ED between April 2015 and December 2015 were also collected once entered by staff members into the hospital’s incident reporting software: the compliance management and reporting system. As this research involves human participants, approval from the research ethics review boards both at the Laurentian University and at the hospital was obtained. Further authorization to perform this research and collect data was obtained from the hospitals’ ED managers.

Results

Between June 2011 and June 2014, prior to the fall prevention strategy, 44 falls were reported on patients aged 37-97 (Table I). In total, 12 were individuals of age 65 years or younger.

The ED fall prevention strategy was implemented on April 20, 2015. Between April and December 2015, the fall risk assessment tool was used to assess patients at risk. During this time, incident report data confirmed that 45 patients between 30 and 95 years old fell while in the ED. In total, 21 patients were older than 65 years. During the collection period, 9,292 patients qualified for a fall risk assessment, as they were over 65 years. However, 5,371 (44 percent male) patients were formally assessed using the fall risk assessment tool. For a patient to be deemed a fall risk, s/he needed to provide an affirmative answer to two from six assessment questions. As such, 56.5 percent who came to the ED were assessed at possible fall risk.

Each risk assessment question was tested (using χ2) against two overarching hypotheses, i.e., there will be no difference in the fall risk among men and women above 65 and 80 years, respectively. The hypothesis could largely be accepted for patients above 65, while largely rejected, except for medications (H2d, 3.019, 0.090), for patients aged 80 and above (Table II). When gender was considered (H1a and H2a), there was a significant difference between men and women in their fall risk regardless of age. Hence, Ho1a and Ho2a were both rejected. While no significant difference was observed on each risk factor question for the 65-year-old category, the reverse held true for the 80 and over category, except medications, where women and men had an equal falls risk (H2d).

A logistic regression analysis on assessment tool data revealed that between 72.9 percent (Cox and Snell R2) and 97.7 percent (Nagelkerke R2) of variation in the six-variable tool could be explained by the model. Furthermore, a logistic regression model showed the relationship between variables and the fall risk score (P/No) (Column B, Table III). The variables’ overall effect (p<0.0001) is statistically significant (Wald χ2). A positive coefficient associated with a predicting variable; e.g., 6.798 for vision, indicates that for every one-unit increase in vision score, we expect a 6.798 increase in the fall risk score log-odds: log(p/1−p) =−10.905+7.104×fall +7.699×fear +7.162×med +7.769×moving +7.834×conditions +6.798 vision.

Discussion

Identifying high risk patients in the ED

Fall risk assessment revealed that 56.5 percent of patients over 65 years sampled in the ED were at risk, which implies that at least every other older patient who comes to the ED may be at risk with ensuing potential consequences. It cannot be understated that understanding patients’ fall history and risk factors as early as ED triage can be critical to patients’ trajectory and care while allowing hospital personnel to exert extra vigilance and precautions. Prior to 2015, ED staff depended on incident reporting data to identify adverse events such as falls. Before the fall risk strategy, it seems evident that underreporting may have led to adverse events without ill intentions. In fact, our study shows that in a three-year period preceding the fall prevention strategy, minimal incidents were reported (n=44), compared to 45 such reports in a nine-month period when the assessment tool was tested and implemented. For comparison purposes, if this assessment period were extrapolated to the three-year period corresponding to the pre-implementation period, then approximately 180 falls would be reported. We found that women have a statistically overall higher incidence than men, whether they are above 65 (10 percent higher among women) or 80 years old (6 percent higher among women). When a univariate test was performed between gender and the six risk assessment variables: (i) fall in the last six months; (ii) fear of falling; (iii) taking medications; (iv) mobility issues; (v) medical conditions and (vi) vision problems, findings were clear cut: no significant gender difference was found for those above 65 years, while women above 80 years showed a higher falling incidence for each variable, except (iii) taking medications, which means that men and women on medications are equally at risk as they age, particularly 80 and above. When one considers that the six fall risk assessment variables used can predict who is at risk 73-98 percent of the time, it reinforces the need to apply the assessment tool as soon as a patient walks in the ED.

Implementing the fall prevention strategy

Universal fall prevention precautions put into place by ED staff were those already well reinforced throughout the hospital, based on Ontario Senior Friendly Hospital guidelines. These precautions include supporting a safe patient environment and physical status, and considering the patient’s cognitive status and medications (Morse et al., 1987; North York General Hospital, 2006). These were stressed to staff, which may have resulted in decreased patient falls and adverse outcomes. The fall prevention strategy involved extensive education and support for patients, families and staff. Nursing interventions and staff behavior were emphasized. Looking out for at-risk patients obliges the healthcare team to act accordingly for quality care and patient safety interests. Implementing the fall risk assessment tool based on the Morse Fall Scale (Morse et al., 1987) meant understanding the fall risk factors and recognizing visual signs, symptoms and patient characteristics. The biggest risk factor for patients was medications (58 percent), especially when many 65-year olds use numerous medications, both prescribed and over the counter. Combined with potential side effects, falling risk is increased. Visual cues were provided to staff via pop up triage assessment screens in Meditech for all patients over 65. Fall risk identification wristbands were provided to ED patients deemed at risk. This identifier benefited ED and other department staff as patients traveled for various treatments and diagnostic tests. This identifier even helped admitted patients once they were transferred to their receiving units.

Research limitations and implications for future research

Our study had two main limitations, which open the path for future research. The first deals with findings coming from only one ED, which may reduce their generalizability. Collecting outcomes from multiple centers and populations would contribute to a more complete picture. Future research could focus on areas such as determining the falls’ financial impact in various hospitals and on a patient’s life once discharged, identifying the most cost-effective fall prevention strategies using randomized control trials. The second limitation relates to the possibility that practitioners may have interpreted at-risk patients differently and hence, leading to underreporting patient fall risk. It may be difficult to eliminate this human element, but continued research on refined training and measures will be invaluable.

Conclusion

As the population is aging rapidly and the healthcare system is faced with increasing volume and challenges, it is essential to ensure that patients’ conditions do not deteriorate by means that could be prevented. By implementing a fall prevention strategy, there is much potential for improving patient outcomes, while meeting what will soon be recognized as a care standard for hospitals across the world. Implementing a fall prevention strategy is one more step toward improving service quality. The results indicate that a fall prevention strategy has the potential to alleviate harm to several patients and, by the same token, save precious resources. The six-variable model responds well to predicting at-risk patients and is relatively simple to administer. Implementing the fall prevention strategy is most effective if staff expectations are clearly communicated, while being actively supported by clinical managers.

Figures

Fall risk assessment tool questions and results

Figure 1

Fall risk assessment tool questions and results

Fall severity prior to and following implementing the fall prevention strategy

Level 1 No harm Level 2 need to assess and monitor Level 3 Treatment/temporary patient harm Level 4 Initial/prolonged hospital stay Level 5 Death All
Pre-implementation data
Age: Below 65 (n=12) 4 8 0 0 0 12
Age: Above 65 (n=32) 9 19 2 2 0 32
Total 13 27 2 2 0 44
Post-implementation data
Age: Below 65 (n=24) 6 13 4 1 0 24
Age: Above 65 (n=21) 6 14 1 0 0 21
Total 12 27 5 1 0 45

Hypothesis testing for the fall risk assessment tool based on age and gender

Hypotheses χ2 p-value Decission at p<0.05
H1a: Age 65/gender/fall risk 47.853 0.000 Reject H1a
H1b: Age 65/gender/fall in 6 months 0.510 0.481 Accept H1b
H1c: Age 65/gender/fear of falling 0.256 0.617 Accept H1c
H1d: Age 65/gender/medications 0.807 0.378 Accept H1d
H1e: Age 65/gender/mobility 0.001 1.000 Accept H1e
H1f: Age 65/gender/medical conditions 0.312 0.584 Accept H1f
H1g: Age 65/gender/vision 0.034 0.871 Accept H1g
H2a: Age 80/gender/fall risk 7.860 0.005 Reject H2a
H2b: Age 80/gender/fall in 6 months 19.180 0.000 Reject H2b
H2c: Age 80/gender/fear of falling 34.543 0.000 Reject H2c
H2d: Age 80/gender/medications 3.019 0.090 Accept H2d
H2e: Age 80/gender/mobility 8.158 0.005 Reject H2e
H2f: Age 80/gender/medical conditions 8.173 0.004 Reject H2f
H2g: Age 80/gender/vision 9.575 0.002 Reject H2g

Logistic regression analysis on the fall risk assessment tool, variables in the equation

B SE Wald df Sig. Exp(B)
Fall 7.104 0.498 203.694 1 0.000 1,216.479
Fear 7.699 0.531 210.137 1 0.000 2,206.166
Med 7.162 0.440 264.768 1 0.000 1,289.583
Moving 7.769 0.523 220.346 1 0.000 2,367.013
Conditions 7.834 0.491 254.708 1 0.000 2,525.061
Vision 6.798 0.733 85.965 1 0.000 895.730
Constant −10.905 0.514 450.082 1 0.000 0.000

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Supplementary materials

IJHCQA_31_1.pdf (3.4 MB)

Acknowledgements

The authors thank ED patients, nurses and staff. The authors give special thanks to Crystal Pitfield, ED Clinical Manager and Jayme Watson, ED CQI Manager.

Corresponding author

Mwali Muray can be contacted at: mx_muray@laurentian.ca