Fall prevention in hospitalized patients

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Fall prevention in hospitalized patients

Category: Research Proposal

Subcategory: Nursing

Level: College

Pages: 5

Words: 1375

Fall Prevention in Hospitalized Patients.
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Patient falls are defined as the frequency at which patients fall per a thousand patient days during their hospital stays (Griffin & Auler, 2011). Falls and injuries resulting from falls in hospitalized patients are one of the most frequently reported events among people in an inpatient setting. A fall might cause internal bleeding, fracture, lacerations, or tears, leading to and increased rate of health care utilization. According to a study conducted the by Bell (2010), patient falls are listed as the third most frequent cause of injury death in inpatients and the leading cause for patients over the age of 65 years. Patient falls are a quality indicator in inpatient service delivery and can tell a lot about the quality of nursing in a particular hospital. In other words, nurses are liable and assume the fundamental responsibility if a patient in their hospital falls in an inpatient setting. Patient fall prevention in inpatient setting involves the management of the underlying fall risk factors affecting a patient and optimizing the environment and physical design of a hospital to reduce the frequency of patient falls considerably. This paper will cover a framework for applying the phenomena of fall prevention in hospitalized patients, including discussions of how this practice enhances patient safety, impact, effective and legal aspect of providing nursing care in the clinical setting. It will also analyze the Morse Fall Scale (MFS), Heindrich II Fall Risk Model (HFRM) and St. Thomas Risk Assessment tools (STRATIFY) to determine the most valid for identifying patients who are at a high danger of falling in a clinical setting among the three.
Falls are a major problem in hospitals around the world. According to a study by Cristian (2012), falls are among the top adverse events in a hospital setting with about 3% to 20% of hospitalized patients falling at least once during the period they were in the hospital. Of these falls, 30% to 51% result in some form of injury. Of the latter, 6% to 45% experience identical types of injuries that may result in death. If one adjusts these to dollar figures, a fall with no serious injury costs the hospital housing the patient around $3500, one with two or more falls with no serious injuries cost $17000 and those with serious injuries costs the hospital at least $27000 (Hoffman, 2015). Accordingly, many methods to prevent falls in hospitalized patients and the injuries accompanied by these falls have been tested. Nonetheless, these methods require multidisciplinary support for it to be considered for adoption and implementation in the hospital setting. Examples of such methods include the Morse Fall Scale, Heindrich II Fall Risk Model and St. Thomas Risk Assessment tools in Falling Elderly Inpatients. There three methods are the commonly used risk assessment tools in fall prevention in hospitalized patients.
Of all the fall risk assessment tools, only a small number of them have been tested for their implementation in a hospital setting. Tool developers often conduct an evaluation of these tools in the same setting where the device was established. This assessment approach limits generalization of the developer’s findings. The reason behind this phenomenon is that when the tools are evaluated in other different hospital settings, the results substantially change and vary from hospital to hospital (Griffin & Auler, 2011). Consequently, it becomes difficult to establish a universal set standard for an all-round fall-risk tool. Because the assessment of patient fall risk contributes in determining the allocation of resources in a clinical setting, an instrument that is reliable is essential. Rowe (2012) stated that the for a tool to be considered appropriate, it has to be subjected to psychometric testing, requires reasonable administration time, has been implemented in an identical population, has possessed a cutoff score that indicates the need for intervention.
The STRATIFY and the MFS are tools that were developed through the utilization of meticulous research design. These tools have been validated in some settings; not just a single setting (Tzeng & Yin, 2014). The findings have then been displayed in reviews by various academics. On the other hand, the final tool in this paper was developed and tested in an acute clinical setting with a high number of diverse patients (Tzeng & Yin, 2014). Moreover, the risk factors highlighted in the HFRM tool has similarities to the risk assessment and prevention tool currently being used in the hospitals; notwithstanding, the latter statement has not yet been validated.
The Morse Fall Scale mainly consists of six categories. Namely, these categories are the history of falling, secondary diagnoses, the usage of ambulation aids, therapeutic sessions, types of gait and mental status (Bell, 2010). Risk factors for the instruments are normally allocated scores that depend on some calculated risk factors. The patients also undergo an evaluation in the presence and absence of the risk factors. The scores are later written in the spaces provided whenever the risk factors are present. Further on the predictive validities of the Morse Fall Scale have undergone an evaluation in various diverse studies with numerous populations. All these play a part in the study of sensitivities that eventually yield percentages in the inter-rater reliabilities.
The fall in the elderly patients comprises the following main categories. These categories are part of the St. Thomas risk assessment tools that include: the history records of falls per patient according to their complaints, the current mental conditions of the patients focused on confusion, the levels of disorientation and mental agitations, visual impairments, frequency of bowel issues, transfer, and finally mobility (Christian, 2012). The prediction according to the variation of strata is examined in different studies as well as in the cutoff scores. The sensitivities achieved in the cut-off scores are presented according to given percentages.
The Hendrich II Fall Risk Model comprises of several risk factors that include the following. Confusion or disorientations, symptomatic depression, altered eliminations, dizziness as well as vertigos, the sex of the patient, prescriptions on drugs to do with anti-epileptics or benzodiazepines, and the frequent normal tests given to a patient (Hoffman, 2015). The patient’s ability to stand up and walk are also examined in this system of assessing the patient needs. The risk factors, in this case, are allocated scores according to the calculations achieved on the relative risks. The patient like in the first method is also examined for the presence or absence of the risk factors. The scores are written in the spaces provided for the risk factors. Higher scores indicate the presence of the risk factors while the sensitivities are presented according to given percentages. In this case, inter-rater reliabilities are not reported.
To establish the most valid toll among the three, two nurses with no previous interaction with the patients in the study were selected to act as independent accessors of the patient fall risk using the three mentioned assessment tools. The accessors were provided with detailed information on the purpose and aim of the study and received training in each of the three tools and what they were to look for. The nurses were then tasked with the responsibility of assessing ten patients on the risk.
The findings of the study demonstrated that all the three mentioned tools were relatively easy to use and to evaluate in a clinical area. Nevertheless, the Morse Fall Scale tool had a more user-friendly impression. Moreover, it was more scientifically accurate and practical. Other advantages of the Morse Fall Scale tool are that it is research driven and the interventions encountered are standardized according to the level of risk (Rowe, 2012). This statement means that the patients who are at a high risk of falling receive the amount of attention they require and as a result, the rate of patient falls in an inpatient hospital substantially reduced and patients are safer. Moreover, this method is effective based on the fact that it is scientifically accurate as well as user-friendly. This minimizes the risk of not applying the tool properly as room for error is reduced because the practitioners find it easy to use. On the other hand, the legal aspect of preventing falls in inpatients through MFS is that the frequency of lawsuits because of patients falling would be reduced; accordingly, the hospital will incur less cost.
In conclusion, it is clear that the Morse Fall Scale provides the best predictive, reproductively, reliability and validity compared to STRATIFY and HFRM fall-risk tools. Moreover, it is effective, improves safety in inpatients and reduces legal cost because of reduced lawsuits. Nonetheless, more study is required to endorse the application of this method in other settings.

Bell, J. (2010). Slip, trip, and fall prevention for healthcare workers. Cincinnati, Ohio?: Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health.
Cristian, A. (2012). Patient safety in rehabilitation medicine. Philadelphia, PA: W B Saunders.
Griffin, H., & Auler, A. (2011). Sequential compression devices related to falls in the inpatient setting.
Hoffman, G. (2015). Risk predictors, outcomes, and costs of falls among older adults living in the community. Los Angeles: University of California, Los Angeles.
Rowe, R. (2012). Preventing Patient Falls: What Are the Factors in Hospital Settings That Help Reduce and Prevent Inpatient Falls? Home Health Care Management & Practice, 98-103.
Tzeng, H., & Yin, C. (2014). The Extrinsic Risk Factors for Inpatient Falls in Hospital Patient Rooms. Journal of Nursing Care Quality, 233-241.