The Stopping Elderly Accidents Initiative

Topic: Geriatrics
Words: 19832 Pages: 5


Falls in elderly patients are a common safety concern, resulting in increased healthcare costs and risks of fall-associated morbidity and mortality. At the project site there was no effective fall risk screening mechanism. The purpose of this quantitative quasi-experimental quality improvement project was to determine if or to what degree the implementation of the of Stopping Elderly Accidents, Deaths and Injuries (STEADI) algorithm, developed by Centers for Disease Control and Prevention (CDC) for Fall Risk Screening, Assessment, and Intervention, would impact the identification and subsequent referrals for the identified fall risk when compared to current practice among adults aged 65 or older in a primary care clinic in Florida over four-weeks. The issue at hand will be examined through the lens of Dorothea Orem’s Self-Care theory.

Introduction to the Project

Numerous older individuals are subject to falls, making it a significant topic in the health care industry (Durgun et al., 2021; Shahrbanian et al., 2021). The issue occurs in multiple settings, and it is associated with injuries and reduced quality of life, as well as significant healthcare expenses (Perrot et al., 2019). In 2019, the Centers for Disease Control and Prevention (CDC) reported that around 25% of older people experience a fall every year, but this problem can be prevented, for example, through the Stopping Elderly Accidents, Deaths and Injuries (STEADI) program. It incorporates an assessment algorithm by CDC (2019) that is supposed to be used by healthcare providers to prevent falls in older adults. Over the past years, STEADI was shown to be a well-established and reliable tool (Eckstrom et al., 2017). The STEADI approach’s application is a topical area in that more research is being introduced in the recent years (Casey et al., 2017). As a result, the introduction of STEADI within a specific setting that does not use it appears to be a suitable topic for a quality improvement project.

There is a significant need for studying the means of fall prevention, including the methods of risk screening (Patterson et al., 2019). Indeed, risk screening can be considered a primary element of ensuring the safety of patients (Lohman et al., 2017; Vincenzo et al., 2020). Given that older patients have specific needs, especially in terms of fall risks (Durgun et al., 2021), the need for the investigation of useful and effective interventions for them is especially significant. There is significant recent research on the topic (Gell & Patel, 2019), which shows that it is an essential and quickly developing, topical field.

The present quality improvement project focuses on the introduction of a method of fall risk assessment (STEADI Algorithm) in primary care. It follows specific guidelines that are represented in its structure. Background information will be presented to understand the scope of the problem. This section will be followed by theoretical foundations, the introduction of the problem statement, purpose, clinical question, advancing scientific knowledge, and the project’s significance. Furthermore, Chapter 1 will present the information on methodology, project design, terms used, and limitations. After that, the points will be summarized. Consequently, Chapter 1 will explain why it is necessary to address the problem of patient falls and how it is possible to identify the effectiveness of the STEADI intervention.

Background of the Project

Patient falls have always been a significant issue in the health care industry, which is especially important for older patients who are demonstrably more likely to fall than younger ones (Shahrbanian et al., 2021). According to the CDC (2019), “more than one out of four people of 65 and older fall each year” (p. 1). Perrot et al. (2019) explained that multiple settings are forced to deal with this issue. For this project, specifically the STEADI risk assessment algorithm is important (Lohman et al., 2017). The STEADI Algorithm for Fall Risk Screening, Assessment, and Intervention by the Centers for Disease Control and Prevention (2019) contains five elements.

The first one is screening, for which it is specifically highlighted that it should be done yearly or when a person falls. STEADI recommends using specific questions and a tool for assessment. The next element is the interventions for persons not at risk of falls; they are meant to be educated on the matter and provided with relevant referrals and reassessments as required. The third element (which is considered the second step for at-risk individuals) discusses the assessment of the modifiable risk factors, which may include comorbidities, issues with vitamin D intake, as well as environmental concerns, and so on.

The next element is intervening, which is meant to reduce the risks through specific strategies like referrals to the assessment of visual impairments or physical therapy and others (the STEADI Algorithm recommends nine specific categories of interventions along with the development of a care plan and health goals). Finally, there is the follow-up section with the recommended intervals of one to three months. The STEADI Algorithm by CDC (2019) is a part of the many STEADI tools for older patient fall prevention, which is the first information that the Algorithm provides.

At the site, which is a small outpatient clinic in Florida, the issue of falls outside of the clinic is among the common concerns for healthcare professionals. Specifically, the problem of patients experiencing risk of falls and, as a result, experiencing falls in their lives, is examined. The patients will be asked whether they have fallen in the previous 30 days, and the data will be synthesized to demonstrate the need for the direct practice improvement project.

The data on the falls will be collected from the practitioners’ reports and interviews of the patients visiting the facility. According to the currently available information, which is based on routine questions directed at older patients, specifically among patients over 65, any number of falls outside of the clinic had been reported at the rate of 21-23% for each of the years 2017-2019, which is lower than it is reported as an average by CDC (2019). However, the number is still fairly large, especially since it is based on self-reporting, and the people reporting the issue might be omitting some of the instances of falls.

Falls have been identified and officially recognized as a problem since the site’s institution, and the healthcare providers, particularly nurses, dedicate time to assisting with risk identification and related referrals. The need for risk assessment is determined on a per-case basis, but for older adults, it is mandatory, and intervention plans along with scheduled follow-ups are offered to check the changes in the patients’ well-being. The clinic does not use CDC’s STEADI materials for that purpose, which calls for an introduction of the latter both due it being supported by evidence and the need for a uniform approach to the issue (Casey et al., 2017). STEADI materials are regularly adapted and reviewed, and they are offered for download from the CDC website for everybody, including healthcare providers. The openness of the source makes it easier to use for this project.

Risk assessment has been identified as a crucial element of fall prevention for different populations, including older adults (Patterson et al., 2019). In the proposed project, specifically the STEADI Algorithm of screening, planning and referral is important. The intended outcome is the increase in relevant referrals in the older population who visit the project’s site. Therefore, by offering the providers STEADI for risk assessment, the resources offered to older patients are meant to be improved.

Problem Statement

The age-related changes, multiple comorbidities, polypharmacy, impaired vision and balance and other aspects put the elderly patients at increased risks of falls and related injuries. The use of screening algorithms can assess the risks of individual patients and develop individual strategies to decrease the risks. The choice of the screening algorithm and program depends on the needs and potential of a particular site.

It was not known if or to what degree the implementation of the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Algorithm would impact the identification of and subsequent referrals to physical therapists to improve gait and balance for fall risk when compared to current practice among adults 65 and older. Thus, the project attempts to identify whether the STEADI Algorithm can affect the referrals of patients to physical therapists meant to assist in the cases when there exists a risk of falls. If yes, the project will assess the effectiveness of this intervention compared to the consequences of implementing usual standards of care at the project site, contributing to the literature on the topic (Johnston & Reome-Nedlik, 2020; Mark, 2019). The results will be conclusive, and they will offer additional insights on the topic while introducing an evidence-based intervention into a healthcare setting (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020).

It is worth mentioning that the problem affects a specific population significantly. It refers to patients of an outpatient clinic since they appear in unusual conditions that subject them to potential threats; specifically, it involves patients appearing outside of a tightly controlled environment (the clinic) while still running rather high risks of falling as a result of numerous causes (Dhalwani et al., 2017; Gomez et al., 2017; Haines et al., 2015; Kiyoshi-Teo et al., 2017; Kuhirunyaratn et al., 2019; Mota de Sousa et al., 2017; Yoo et al., 2015). Therefore, the cases under analysis require greater insight.

Furthermore, the focus is placed on the general population of people 65 years old and older. It is so because many of these individuals’ health conditions are deteriorated because of natural processes (Durgun et al., 2021; Shahrbanian et al., 2021), which subjects them to more falls (Dhalwani et al., 2017; Gomez et al., 2017; Haines et al., 2015; Kiyoshi-Teo et al., 2017; Kuhirunyaratn et al., 2019; Mota de Sousa et al., 2017; Yoo et al., 2015). Every fall can have more dangerous consequences for older people than for younger patients (Durgun et al., 2021; Shahrbanian et al., 2021). For this reason, strategies for managing falls in the elderly must be designed.

That is why it is rational to look for ways how to address the problem under consideration. The given project tries to cope with this task, and the project results can contribute to solving the problem. It is so because the analysis of the STEADI’s Algorithm can confirm that this intervention is effective in protecting older patients from falls, which would suggest that fall risk assessment is important for patient health (Mark, 2019). Overall, the goal of the project is to contribute to the wealth of data on a particular evidence-based intervention while implementing it at a site that recognizes the importance of the issue of falls. The main positive outcome of this project being successful would consist of the providers of the site having a new tool for risk assessment that is evidence-based (Lohman et al., 2017).

Purpose of the Project

Risk assessment is important for choosing the most appropriate prevention strategies. Screening of the patients’ health parameters and living conditions allows estimating the potential risks of falls. The implementation of STEADI algorithm in the chosen setting will depend on a variety of co-factors.

The purpose of this quantitative, quasi-experimental quality improvement project is to determine if or to what degree the implementation of the CDC’s STEADI Algorithm would impact the identification of and subsequent referrals for the identified fall risk when compared to current practice among adults 65 and older in an urban Florida primary care clinic. The STEADI Algorithm (independent variable) will be defined as the Algorithm for Fall Risk Screening, Assessment, and Intervention. Referrals will be defined as the instances of referring patients to fall preventions services as a result of fall risk assessment. The relationship between them will be determined based on a quasi-experimental project. The population is specifically Florida (south of the state) outpatients who are older people (over 65) and who have been assessed for risk of falls.

This purpose indicates that the project will try to improve the services provided older patients in one outpatient clinic in Florida. By analyzing the STEADI Algorithm for Fall Risk Screening, Assessment, and Intervention, the project will show whether it is possible to affect the referrals to relevant services for patients who are defined to be at risk of falls. The findings will also demonstrate whether other clinics and health care settings should draw their attention to fall assessment to address the problem under analysis. The findings, therefore, will have immediate value for the patients while also contributing some data to the field of study, as well as providing the healthcare workers of the site with new tools and signaling to other healthcare workers about the opportunities of CDC’s STEADI.

Clinical Question

To evaluate the potential benefits of STEADI for the patients and healthcare practitioners, the project includes the analysis of measurable variables. The project establishes the relationship between the independent and dependent variables (if any). In this way, it will be estimated if the relationship is statistically significant, and the project will answer the main clinical question.

To what degree does the implementation of the CDC’s STEADI Algorithm impact the identification of and subsequent referrals to physical therapists for the identified fall risk when compared to current practice among adults 65 and older in a primary care clinic in urban Florida? The CDC’s STEADI Algorithm will be used as an evidence-based intervention. The standard fall prevention does not use STEADI as its means of assessing the risks of falling and educating patients or caregivers on the topic. The population includes patients over 65 in the outpatient clinic. The selected timeframe is the most feasible one for the project.

Like the problem statement, the clinical question implies different variables. The STEADI Algorithm for Fall Risk Screening, Assessment, and Intervention is an independent variable, meaning that its implementation needs no measurement. Referrals is the dependent variable, which denotes that it is necessary to measure them. The Electronic Health Record data will be used to quantify the referrals, which will use ratio data. The predictive statement is that there might be a statistically significant difference between the instances of referrals before and after the intervention.

Advancing Scientific Knowledge

The project will result in some improvements concerning population health outcomes. The project findings will demonstrate whether it is adequate to rely on the STEADI Algorithm in improving referrals to relevant services. In case of positive results, this advancement will be a small step forward in a line of current quality improvement projects, which demonstrate that STEADI can be used effectively in practice (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019). Additionally, the project will be contributing to the covering of an important research topic, that is, fall risk screening in patients, especially older patients, which is a rapidly developing field (Patterson et al., 2019; Phelan et al., 2017 Snooks et al., 2017; Vincenzo et al., 2020). Overall, the project can contribute to the improvement of practice in the studied clinic and advance knowledge in an important field.

This project proposal focuses on Orem’s (1985) self-care theory. Younas (2017) stipulates that worsened health outcomes are often the result of self-care deficit. This concept implies that a person lacks sufficient ability, knowledge or desire to take care of themselves. From the perspective of nursing, Orem’s (1985) self-care theory implies that healthcare providers are capable of affecting self-care deficits through diverse interventions. It cannot be denied that fall risk assessment is one of such interventions: it provides the patients with information about their health (Patterson et al., 2019; Phelan et al., 2017 Snooks et al., 2017; Vincenzo et al., 2020), which assists them in reducing self-care deficits. Furthermore, CDC’s (2019) Algorithm is explicitly aimed at reducing self-care deficits in that it works to identify the specific fall risk issues at hand and offer solutions to them through referrals and other changes. Therefore, Orem’s (1985) self-care theory can be used to frame the key elements of this project, and it provides a justification for the selected intervention. The project will be able to demonstrate the theory’s use in practice and its viability.

Significance of the Project

A quality improvement project gap explains the project’s importance. There are empirical studies that would support the effectiveness of the STEADI program (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019), which makes it an evidence-based intervention that is currently studied very extensively. It is a topical area that can benefit from additional exploration, especially in the form of a quality improvement project that implements this evidence-based intervention within a site that does not use it. In turn, risk assessment is a common approach to fall prevention which is often considered a requirement for the programs that aim to prevent falls; it is another topical area that is extensively studied (Gell & Patel, 2019; Patterson et al., 2019; Phelan et al., 2017 Snooks et al., 2017; Vincenzo et al., 2020). The given project might contribute to the current literature on the topics, and it will fit into the literature on the STEADI program and the effects of fall risk assessment on relevant referrals. The results will not be able to offer a conclusion on whether it is reasonable to conduct additional research on the matter of fall risk assessments, but they will contribute toward highlighting the importance of the studied topics as well.

The project is also significant because it may generate crucial theoretical implications. It relates to the connection between patient falls and Orem’s (1986) self-care theory. In particular, the project will investigate the elimination of self-care deficit through a referral- and fall risk assessment-based interventions. While no direct relationships between the intervention and falls will be offered, the project will demonstrate the ability of the self-care theory to conceptualize an investigation like this one.

Finally, it is reasonable to comment on the project’s practical implications. The results will be beneficial for practitioners because they will understand whether it is useful to use this fall prevention method (the STEADI Algorithm). The project’s results will influence a way of health care delivery within the stated setting. Attention to the need for risk assessment, referrals, as well as follow-ups, will be brought. In the long run, the project might contribute to the improvement of the whole medical industry since it tries to improve older patients’ health outcomes. By demonstrating the ability of STEADI to improve the well-being of patients, the relevant literature on the topic will be supported (Johnston & Reome-Nedlik, 2020; Lee, 2017), and as a result, the project will be able to highlight and promote the use of STEADI in outpatient settings. Thus, the project’s main ability is the application of STEADI within a specific setting and potentially the demonstration of its usefulness overall.

Rationale for Methodology

The proposed quality improvement project tries to identify the extent to which the specific intervention influences the referrals to various services meant to assist people experiencing a risk of falls. It means that it will be necessary to work with figures and make appropriate calculations to identify whether the proposed solution is significant, which requires a quantitative methodology. Creswell and Creswell (2017) explain that this methodology type is necessary when there is a need to test for a relationship between different variables. Furthermore, Rutberg and Bouikidis (2018) stipulate that the reasons to choose this methodology are “if a lack of quality improvement project exists on a particular topic, if there are unanswered quality improvement project questions” (p. 210). As for the given project, the quality improvement project gap and the unanswered clinical questions are present, denoting that the methodology is correctly chosen.

In addition to that, a quantitative methodology seems the best option because other variants, including qualitative and mixed methods, are not suitable. A qualitative methodology is used when it is necessary to explore a problem that is not well understood (Rutberg & Bouikidis, 2018). Quality improvement projects tend to organize semi-structured interviews to allow participants to disclose their feelings and attitudes to the problem under analysis. Qualitative investigation would not be able to respond to the stated clinical questions, which seek to establish relationships between variables, although it can be helpful in other circumstances (Polit & Beck, 2017). A mixed methodology combines the features of the previous two, which is why it is not suitable either: it incorporates qualitative approaches which would not be helpful in this instance. Mixed methodology is used in those cases when scientists want to calculate statistical indicators and identify participants’ feelings (Rutberg & Bouikidis, 2018). Finally, Polit and Beck (2017) admit that quantitative methods are more feasible since they require less time to answer clinical questions in comparison with qualitative and mixed approaches. In conclusion, it is possible to mention that the given project’s methodology is chosen according to the problem statement, clinical questions, and purpose.

Nature of the Project Design

This project follows a quasi-experimental design to reach the purpose and answer the clinical question. This design includes the intervention (STEADI Algorithm) and one group observed to determine the referrals that it experiences before and after the intervention. Such studies are suitable to identify a relationship between an intervention and its outcomes (Polit & Beck, 2017; Rockers et al., 2017). Furthermore, this design is appropriate to assess interventions’ effectiveness while also being relatively easy and quick to implement (compared to, for example, an experiment) (Polit & Beck, 2017). That is why a quasi-experimental design is the best approach for the given project as compared to other options: it is more feasible than an experiment and implies fewer ethical concerns than it, but it is also sufficiently capable of responding to the clinical questions, which makes it more appropriate than, for example, post-test project only.

The given project does not have an extended sample because it considers a Florida outpatient clinic that is not large. That is why a sample size includes 150 patients based on a GPower analysis. The inclusion criteria are the age older than 65 and assessment for fall risks with the use of a STEADI tool. The patients will not be contacted (only their Electronic Health Record data will be used), which is why no signed consent is required.

The project will use one group (comparative and implementation); all the practitioners of the clinic will be provided with the STEADI Algorithm (Polit & Beck, 2017). The data about referrals will be extracted from the Electronic Health Records. The baseline data will consist of the previously reported referrals for the patients who will be assessed during the project as long as there is reported data. Polit and Beck (2017) explain that pre- and post-test data are sufficient to assess the effectiveness of an intervention. Consequently, the report will use referrals to identify whether the STEADI Algorithm is useful in reducing falls.

Definition of Terms

This section will explain terms, variables, and other specific terms that may be unknown to a layperson.


Falls are completed events that occur when patients have collapsed (Hill et al., 2015). These events imply adverse health consequences but can be preventable. Various factors, including health conditions, external factors, the lack of education, and others, can make patients collapse.


The CDC (2019) program recommends introducing referrals to varied services meant to promote the healthcare of individuals based on their needs identified through STEADI, which is the definition of referrals within this project. They are the dependent variable to be considered.


The concept of self-care refers to a person’s ability and desire to take care of themselves (Younas, 2017). Self-care deficit can result in the fact that individuals neglect their health condition and well-being. The current project considers Orem’s self-care theory and its relation to the occurrence of falls, specifically from the perspective of healthcare providers who can reduce self-care deficits, among other things, through risk assessments.


The CDC (2019) program meant to prevent falls in older patients, which offers providers important tools. For this project, the Algorithm for Fall Risk Screening, Assessment, and Intervention is the independent vairable. STEADI guides providers on how to assess the risks in older patients and intervene through care plans and relevant referrals, and the implementation of the program has been studied comprehensively (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019), which makes it an evidence-based solution.

Assumptions, Limitations, Delimitations

The given quality improvement project implies several assumptions that deserve attention. The participants’ interest in the project is an underlying assumption. This fact denotes that the staff will be willing to undergo the intervention (that is, learn to use the STEADI Algorithm). Furthermore, it is assumed that providers will use STEADI algorithm correctly, using all of its stages in their practice and report the data. Research shows that the rates of adhering to interventions vary, but they tend to be rather high; specifically for STEADI, they are estimated to be at around 75% (Vincenzo & Patton, 2019). It is believed that the STEADI algorithm will be applied to all patients meeting the criteria.

It is also reasonable to comment on the project’s limitations. On the one hand, a small sample size of 150 patients is objectively a weakness, but it should be sufficient for the specified methodology based on GPower analysis. On the other hand, the fact that the participants will only represent one clinic is important; it is a limitation because patterns need to be studied across different environments (Polit & Beck, 2017). The providers’ workload may impact the visit time and thus, affect the time spent on the use of the algorithm and its effectiveness. The short timeframe of four weeks is admittedly a limitation (Polit & Beck, 2017), but it is explained by feasibility considerations.

Finally, a delimitation also deserves specific attention in the given project. This project focuses on older patients because they are more subject, compared to younger individuals, to falls. The individuals who are younger than 65 years old, but are at increased risks of falls due to certain circumstances, such as working in risky environments, or due to certain conditions, not related to ageing changes, including substance abuse, will not be included into the sample.

Summary and Organization of the Remainder of the Project

Patient falls at healthcare facilities and outside of them are a significant issue in the health care industry. According to the CDC (2019), numerous individuals older than 65 years old are subject to this problem, making it necessary to find an effective intervention. Risk assessment is a crucial element of fall preventions, and a few scientific articles assess the impact of the STEADI Algorithm (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019). This fact determines the significance and purpose of the project, which focuses on the falls outside of the clinics.

An appropriate quality improvement project piece is necessary to proceed to study the effect of CDC’s STEADI program. That is why a quantitative quasi-experimental study of 150 participants with the pre- and post-intervention group seems suitable for the current project (Rutberg & Bouikidis, 2018; Polit & Beck, 2017). Orem’s (1985) self-care theory will guide it. Over four weeks, the project’s findings will lead to significant theoretical and practical advancements to the health care industry through the investigation of the effects of an evidence-based intervention. The small sample will be a major limitation, as well as the timeframe.

Since the introduction to the project is completed, it is reasonable to proceed to reviewing the literature in detail. Thus, Chapter 2 will present a detailed background and literature section and comment on the project’s theoretical foundations. Chapter 3 will focus on purpose, clinical question, and methodology to cover all these details in precision. Chapter 4 will describe the results of data analysis, which is necessary to understand how the project will reach and interpret its results. Chapter 5 will offer a conclusion along with recommendations based on the project.

Literature Review

This quality improvement project focuses on fall prevention in older populations, especially the education of caregivers based on the STEADI program (Centers for Disease Control and Prevention [CDC], 2017; 2019). This section is a literature review, which will present the results of a systematic review of the literature on the topic of falls education, as well as some findings of non-systematic reviews of the literature on falls in general.

Thus, the project involved two sections; the first one was based on systematically reviewing all the literature on the topic of education-based fall prevention that was published within the past 5 years in peer-reviewed journals found in large healthcare databases, including MEDLINE, CINAHL, and PubMed. The decision to limit the literature to recent articles was made to ensure the most pertinent information about the up-to-date approaches to the issue was included. The named databases are among the sources that are most often employed in healthcare research (Polit & Beck, 2017).

The second section involved finding predominantly recent articles on the topic of falls, including fall prevalence, fall prevention and fall education, as well as caregiver involvement in fall prevention. This literature was required because the systematic review produced a limited number of sources, and expanding the years of source search helped to include more information. Additionally, some seminal works were incorporated (in particular, for the theoretical frameworks of the project), but in the majority of cases, the project looked into recent literature.

For the systematic part of the literature review, inclusion criteria were English language, availability and published date (past 5 years). The keywords included “fall prevention,” “education,” “older patients.” Articles could be excluded for not being relevant to the project.

This chapter will be structured in the following way. It will include themes and subthemes, which will be supported by several sources. The first theme is the importance of falls, which is highlighted in all the reviewed literature but will be exemplified through a limited number of sources. The second theme is the possibility of reducing falls, which is illustrated through empirical recent and less recent literature, including the articles by Carlucci et al.’s (2018), Hill et al. (2015), Jie and Deng (2019), Meyer et al. (2016), Nakagami-Yamaguchi et al. (2016), Ueda et al. (2017), and some others. Some conflicting data will be presented as well (Hill et al., 2019), and it will be demonstrated that significantly more research on the topic is required, especially since recent sources on the topic are rather scarce, and certain subtopics (especially the education of caregivers) are rarely considered. It will be highlighted that the presented literature supports the proposed methodology of the current project, although it can be used to showcase and legitimize different approaches, especially quantitative ones.

The present project does not really uncover a starting point of the studying of the problem of falls in or outside of healthcare settings, but it shows that it has been identified extensively; it is currently recognized as a serious issue, especially among older patients (those above 65 years old) (Durgun et al., 2021; Shahrbanian et al., 2021). Falls can cause injury, resulting in additional healthcare expenses and reduced quality of life, and the issue is associated with multiple factors that can direct efforts aimed at preventing falls (Dhalwani et al., 2017; Gomez et al., 2017; Haines et al., 2015; Kiyoshi-Teo et al., 2017; Kuhirunyaratn et al., 2019; Mota de Sousa et al., 2017; Yoo et al., 2015). Research shows that educational solutions meant for patients, caregivers and professional caregivers are an option (Chang et al., 2019; Cho & Jang, 2020; Frith, 2017; Lyons & Hall, 2016; Ott, 2018; Radecki et al., 2018; Shim & Kim, 2019; Turner et al., 2020; Ximenes et al., 2019; Zhao et al., 2019), but specifically the interventions for patients are rarely researched, and those for informal caregivers are almost never studied. The need for them is established, though, as patients and caregivers ask for additional information on falls (Schoberer et al., 2016; Xu et al., 2019). Therefore, the topic of caregiver education in fall prevention is a clear research gap with few recent sources even mentioning it without trying to contribute. The present project will attempt to contribute by introducing STEADI interventions with a focus on family caregivers.

Theoretical Foundations

Orem’s (1985) self-care theory is used by this project. The concepts of self-care and self-care deficits have explained and justified educational interventions for patients (Younas, 2017), which explains its choice for the current project. Since it is focused on the education of patients and caretakers, Orem’s (1985) theory might be able to frame the clinical question and assist in understanding the mechanism behind the work of the project’s intervention.

Orem’s (1985) general idea is that nursing is the act of assisting someone with self-care, which, in turn, is the ability to take care of oneself, meaning the ability to maintain one’s well-being. To be a nurse, a person needs to have the knowledge and training required to understand and assist other people with their self-care. Humans are the object of nursing, as a result, and the subjects are nurses. The environment is also taken into account, including physical and social aspects. Orem’s (1985) view of health as soundness is slightly outdated, but it took into consideration both the health of individuals and groups, which is helpful for the present project, which considers the unit of patients and caretakers. A self-care deficit implies that a person cannot ensure effective care of themselves for any reason, which often includes a deficit of resources, but might also incorporate a variety of factors, including demographics (for example, age), cultural and social factors, and environmental factors (for example, access to healthcare) (Orem, 1985). One of the primary reasons that justifies the present project is the lack of knowledge. The same can be said about the lack of knowledge in caring for another person. Improved knowledge should reduce a self-care deficit in the participants, providing them with improved means for self-care, which, in turn, should lessen the negative outcomes of self-care deficits (falls). Thus, the theory explains the variables and the reason for the possible relationship between them.

Self-care is a complex concept, which incorporates universal requisites (something that is required for a person to be able to perform self-care, including the intakes of air, water, food, excretion, rest, interaction, development and management of hazards), as well as the requisites that become important when a person is in the state of “health deviation.” Teaching another person how to self-care and ensure the fulfilment of all these requisites is one of the methods of helping a person to self-care (Orem, 1985).

It is important that for Orem’s (1985) theory, the interactions between humans are critical. According to the theory, humans form connections for the sake of their well-being and to exchange the means of self-care, as well as organize the care for the people who cannot take care of themselves. This perception of self-care and human attempts to provide care to those who cannot perform it is also helpful in understanding the variables of the project, in particular, the ideas of exchanging knowledge for improved self-care and the concept of caretakers. Caretakers are the people assigned by the society to perform caring for people with self-care deficits, and knowledge exchange is one of the instruments they can employ.

Finally, a change theory for advancing the suggested solution in the target environment and encouraging a positive shift in the management of falls in the elderly, specifically, in the drop in the referrals for falls, will be needed. To ensure that the proposed change is integrated promptly and seamlessly into the specified clinical context, Kotter’s 8-Step Change Model will be needed. Allowing one not only to integrate change into the clinical context successfully but also sustain it and enforce it as a form of continuous improvement, Kotter’s 8-Step model should be seen as a template for incremental change in the healthcare setting. The theory in question suggests that change should be advanced in the manner that will allow creating a cycle of continuous improvement. Specifically, the first stage of Kotter’s framework involves establishing the sense of urgency required for the change. In the target context, the assessment of the current healthcare setting, with the focus on self-care deficits observed in it, will help accomplish the specified goal.

Afterward, a team of experts will have to be built in order to promote change. Specifically, a multidisciplinary group of experts deploying the STEADI approach to promote a more effective management of falls in the target setting will be required. The described step should be followed by building a vision of an environment in which collaboration and the adoption of an advanced data management system will allow reducing the threat of falls and improving the quality and efficacy of patient referrals within the current healthcare system.

Afterward, the key goals for the participants to align their actions with and become engaged in the process of change will have to be communicated. The specified objective will be accomplished by improving the current communication channels between nurses and patients, as well as among nurses and healthcare experts. As a result, comprehensive data for each patient will be collected and arranged accordingly so that proper decision-making could be launched and so that effective fall prevention strategies could be deployed. Specifically, all participants will be informed and instructed about the application of the STEADI model in the clinical context to address the observed fall issue and improve the quality of patient referrals, while also working toward reducing the number of referrals in question.

To empower action as the fifth step of Kotter’s Model, incentives for improved performance will have to be introduced. Namely, nurses will receive active support and training options so that they could apply the newly developed skills to managing the issue of falls and address the referrals situation. To create short-term wins as the sixth phase of the model implementation, minor milestones such as the reports identifying improvements in the public health levels within the target community will be introduced. Finally, to ensure that the change continues and is accepted, thus, implementing the seventh and the eight steps, one will need to establish a system of reporting for nurses to use to offer feedback concerning the efficacy of the system’s performance and the challenges in applying the STEADI model. Thus, the suggested changes will be applied successfully.

Review of the Literature

This section will present the themes and subthemes noted in the literature that were found with the help of a systematic literature review, as well as a non-systematic review on the topic of educational interventions for falls and falls overall respectively. The approach consisted of reviewing the major healthcare databases according to Polit and Beck (2017) and finding the literature that could be found to test or discuss an intervention, especially STEADI, although all other information-based interventions regardless of their application (to patients, nurses or caregivers) were considered as well. The articles were only included if they were in English; also, the goal was to exhaust the literature on educational fall interventions within the past 5 years, but since the project has been going on for a while and since certain literature from other years proved to be very helpful, some additional older literature was included. Thus, the literature includes a systematic review of the sources in English in the past 5 years dedicated to educational fall interventions, as well as some older sources and general fall sources.

Interventions Needed for Falls

Falls are a common problem in elderly population, which may lead to serious injuries and complications. Due to the age-related changes in the body systems, older individuals may suffer from impaired balance and poorer body control, leading to not only higher risks of falls, but also higher risks of fall-related injuries (Shahrbanian et al., 2021). Furthermore, the psychological factors, including the fear of falling and additional tension caused by that fear, may also contribute to the incidences of falls in individuals age 65 or older.

Falls can be prevented or at least minimized through the use of risk screening in primary care and other settings. Whereas all individuals older than 65 years old are at risk of falls, the individuals who have additional risk factors, such as impaired vision or orthopedic disorders, are at increased risk. Timely detection and referrals to physical therapists, conducted in the frames of a comprehensive screening offered by the STEADI algorithm is essential for reducing the risks of falls in high-risk groups of elderly patients.

Falls and Reduced Quality of Life

First and foremost, falls are dangerous and capable of diminishing a person’s quality of life (Durgun et al., 2021; Shahrbanian et al., 2021). Multiple quantitative studies demonstrate that falls can cause issues, based on the fact that they are associated with injuries. This information is critical in ensuring that the project is justified.

Burns et al. (2020) presents a statistics study based on the Vital Statistics and Behavioral Risk Factor Surveillance System data, which demonstrates the importance of falls for older men and women in the US. Specifically, the authors highlight the importance of falls (713/1000 in 2018), fall injury (171/1000 in 2018), and fall deaths (increased 16% between 2012 and 2018 to 64/1000, which was a statistically significant increase). Additionally, the authors found an increase in the fall injuries among men, who, however, report fewer falls than women. In summary, the article offers very useful, if broad, findings on the topic of falls in older adults in the US. There are no findings in this article regarding Florida, but the US rates are still important, and they show the rates and changes in rates of falls, which justify the present project as one dedicated to an important issue that is growing in importance.

Gazibara et al. (2017) carried out an epidemiological study, which aimed to determine the frequency of falls in older people (n=354 older Serbians), as well as a number of other characteristics of falls that would be useful for fall analysis and prevention. The sample consisted of people older than 65, and they came from one community health center. The main method of data collecting was detailed interviews along with the Falls Efficacy Scale. The frequency of falls amounted to 15.8%, and half of them occurred during walking; moreover, almost half of all falls were injurious (49.1%). The most common injuries sustained included head hematomas, as well as “soft tissues contusions”; the majority of the people experiencing falls were female and had a fear of falling (Gazibara et al., 2017, p. 215). It should be pointed out that the article is not very generalizable because it only studied the population of one Serbian health center, but still, the findings provide an idea about the characteristics of falls that make them dangerous.

Hill et al. (2015) carried out a randomized controlled trial (RCT) with 3,606 inpatients from Australia to determine the effectiveness of a fall prevention intervention, finding that roughly 30% of the falls were injurious. The authors justified their attention by highlighting the fact that falls were very common events in hospital settings and established the aim of examining the effectiveness of a specific program meant to reduce falls in patients through the education of both patients and staff. The project was multicenter, which improves the generalizability of the findings; it also took place over the course of 50 weeks, which makes it approach a longitudinal research method. The allocation to the control and intervention groups was random (as is required for an RCT), and their comparison showed that no significant differences between the groups could be found. The only important difference was that the number of falls (based on a patient-days fall registration system) was smaller in the intervention group, which implied that the rates of falls were reduced in that group and that the program was effective. The project only involved Australian patients, but the number of patients was large, and they came from different centers, which improves the generalizability of the results. Furthermore, the use of RCT as a research design improves generalizability as well. Overall, this is a very solid source that assists in improved understanding of the mechanisms behind reducing falls in hospital settings. In addition, the article showed that the number of injurious falls was also reduced by the intervention.

Moreover, in an effort of making the project more longitudinal, Hill et al. (2019) performed a follow-up to an RCT to determine the effects of a fall intervention on post-discharge falls, finding that more than half of all the falls were injurious. The goal of the project was to determine the effects of an educational program on post-discharge falls, which the authors explained by the fact that after hospital discharge, older people tend to experience falls. The methodology was an RCT once again, and the settings were Australian; the duration of the study involved 6 months of following the participants. The intervention presupposed using a video and a workbook, as well as a discussion. This time, only 382 participants were involved (over 65 years of age), with 188 people in the control group, assigned randomly. The results suggested that significant differences between the groups in terms of fall rates did not exist, but falls were reported by the participants, and half of them were injurious (49.7%). The program was tested with a rather small number of participants coming from one country; as a result, the findings are not very generalizable. Still, the findings regarding the number of injurious falls seem to be in line with the rest of the cited research, although, admittedly, more investigation is required to determine the effectiveness of this specific program and other programs. This source can be considered counter to the rest of the articles on fall prevention interventions in that it does not find a statistically significant difference between the two groups. However, these findings should only be applied to the specific intervention used by Hill et al. (2019).

To summarize, the literature on the topic suggests that falls, especially in older adults, are likely to be injurious and reduce the quality of life of patients, which is why it is important to address the issue. This subtheme includes a very important source which contradicts the findings that will soon be discussed, and it also uses an older source that was included because of its connection with the critical contradicting source. Overall, the literature that is presented in future themes supports the exact same idea: all the authors who write on the topic in qualitative or quantitative sources demonstrate that falls are a significant issue that should be addressed.

Complex Causes of Falls

Falls are associated with very numerous factors, and they are especially commonly related to a combination of factors working in concert. Both qualitative and quantitative studies provide the relevant information, suggesting that knowledge might be among the factors of interest (Dhalwani et al., 2017; Gomez et al., 2017; Haines et al., 2015; Kiyoshi-Teo et al., 2017; Kuhirunyaratn et al., 2019; Mota de Sousa et al., 2017; Yoo et al., 2015). In this section, the potential risk factors of falling in elderly patients are going to be reviewed with a focus on psychological factors and other ones that can be affected by education.

Hopewell et al. (2018) in a Cochrane systematic review, included 62 trials with almost 20,000 community-dwelling older adults, most of them women. The research involved multifactorial interventions that typically incorporated exercise, technological interventions, psychological ones, as well as medication review, and multiple component interventions, that typically included exercise and education. The authors concluded that either option could be effective, and importantly, all of them addressed diverse fall risk factors, including medication, frailty, instability, gait and balance problems, as well as problems with vision and the presence of a number of chronic diseases; additionally, environmental factors were identified, for example, insufficiently secure rails, or slippery surfaces and bad footwear. Typically, an interaction of factors was relevant, and additionally, knowledge of risk factors was shown to be effective in reducing falls. It is also noteworthy that among the studied outcomes were fall rates, number of people experiencing falls, number of people sustaining several falls and/or fall-related injuries, as well as number of people experiencing hospital admission and/or requiring medical attention as a result of their fall. Overall, this project is very helpful in contextualizing the current project, with its quality being Cochrane-level. However, the limitations of the project are noteworthy: specifically, the authors highlight the fact that many of the projects have issues with quality, which may have affected the quality of the results of their review. Additionally, not many countries are represented in the project, but still, the findings are particularly important.

Pua et al. (2017) studied a connection between falls efficacy, as well as postural balance, and fall risks in the elderly, and they recruited 247 adults, who were visiting a specific emergency department. The outcomes were fall rates and gait speed, as well as falls efficacy and postural balance. The participants were observed for 6 months. Based on their findings (which were determined with the help of a multivariable proportional odds analysis, with confounding variables taken into account), the authors were able to conclude that falls efficacy affected the relationship between balance and fall risk, and low falls efficacy was also associated with reduced gait speed. In other words, falls efficacy was considered among the modifiable (for example, through education) features that could reduce the risk of falls in older adults. Admittedly, the sample was not very large, and it was mostly drawn from one location, which may have affected the outcomes. However, the analysis was very rigorous, with confounding variables taken into account, and the methodology was that of a prospective cohort study, which guarantees a high level of quality. Overall, the findings can be used to highlight the complexity of the interactions of risk factors for falls, as well as to demonstrate that education which targets specific risk factors can be useful.

Mota de Sousa et al. (2017) used the approach of a systematic literature review to identify risk factors for falls in older adults who reside in a community. The authors highlighted that they identified 50 risk factors, of which only 38 were listed on the Taxonomy of the NANDA International. The authors explicitly meant to update that taxonomy. The following key risks were identified: physiological factors (most typically, difficulties with seeing, walking, balancing, as well as pains, lowered strength and mobility and a number of specific illnesses or conditions, for instance, urinary urgency), environmental ones (for example, the absence of grab bars or anti-slip materials in strategic areas), pharmacological agents (various medications, especially polymedication), cognitive factors (different reasons for changes in cognitive function), psychological factors (for instance, the fear of falling and depression), socioeconomic factors (in particular, low education), and personal factors (most commonly, age and gender). This is a very comprehensive, nurse-oriented review that provides findings based on 62 sources, all of which have their own limitations but can still be used due to the author’s focus on primary sources with predominantly cohort or correlational and descriptive studies, as well as a few RCTs and quasi-experiments. It is noteworthy that the evidence was gathered from all over the world, but, admittedly, the US and Australia were the most well-represented ones. Overall, the review provides some very good grounds for understanding the causes of falls, and it can be used to contextualize this project, as well as its intention of considering and adjusting the psychological factors of falling.

Schoene et al. (2019) provide a systematic review of specifically the issue of the fear of falling. The authors investigated the association between this factor and the quality of life in older people. A total of thirty studies were identified with roughly 29,000 people, the majority of them women, and through a literature review, the authors demonstrated that fear of falling did appear to be in an association with quality of life, lowering it, while falls themselves did not have a similarly profound effect on quality of life. Fear of falling is commonly associated with falls as one of the relevant risks (Perrot et al., 2019), but this study exists to specifically demonstrate that fear of falling in itself is a major issue that deserves to be addressed. Falls were not shown to have an impact on fear of falling. In the end, the authors highlight the importance of interventions that address fear of falling, which would be expected to affect the risks of falling while also improving the quality of life in older patients.

In summary, the number of sources on the topic of the causes of falls is substantial, and it suggests that there are diverse causes, several of which can act simultaneously. As a result, the primary intervention that the sources, which are very high-quality, suggest is a holistic intervention. However, admittedly, the intervention that targets education would also be appropriate based on the presented findings.

Addressing Falls through Information

Falls need to be addressed, and they often need to be addressed through information (Chang et al., 2019; Cho & Jang, 2020; Radecki et al., 2018; Turner et al., 2020). There exist sufficient amounts of information on education-based interventions, which assist through the development of understanding in participants. This subtheme is critical in justifying the project.

Mamani et al. (2019), in an interview-based study (n=97; Brazil), investigated caregiver practices and found that their knowledge on the topic was superficial. In fact, almost half of the participants exhibited little to no knowledge on the topic, and only one fourth of them practiced different methods of fall prevention. Despite using qualitative methods, the project was focused on quantitative analysis, and it showed that more knowledge and improved attitudes toward fall prevention could be developed within the population. The authors concluded that insufficient knowledge and inappropriate perceptions of fall prevention might have been among the reasons for the low percentage of practicing fall prevention methods, but their study was not correlational, so they could not assert that based on their data. The project had a small sample of only Brazilian people; however, the research on caregivers is so limited that these findings provide at least some information on the topic.

Singh et al. (2020) evaluated the state of fall prevention at Canadian facilities, and while this topic is not directly connected to the topic of community-dwelling adults, it still demonstrates interesting conclusions about the educational component of fall prevention. The project studied a number of specific facilities, and it used the documentation to report the results, finding that pre-fall policies and procedures were intelligence focused. To be more specific, they included understanding what a fall meant, establishing risks assessments and, eventually, fall prevention approaches, which incorporated environmental and educational components. Overall, the authors concluded that a lot of similarities were found between the different facilities, but a need for more evidence-informed approaches was required. In future articles, direct evidence of the usefulness of education-based interventions will be introduced, but here, it is important to state that educational interventions are already being used, which makes them evidence-based practice.

Similar to Mamani et al. (2019), Lim et al. (2018) prepared an interview-based study (n=100; patients from Singapore) that looked into patient fall experiences and uncovered multiple negative attitudes that could increase patient risks and might need unlearning. It should be pointed out that the Lim et al. (2018) study was qualitative, not quantitative, and it uncovered several key themes. For one, many of the participants appeared apathetic and uncaring about falls, in which they showed the perception of the inevitability of falls, as well as considering them less important than having hygiene issues. Furthermore, the participants blamed themselves for falls, did not want to “impose” on nurses, and were prone to overestimate their abilities, all of which resulted in falls. They also experienced difficulties with remembering fall-related advice. The participants of the project are not representative of any specific population, although most of them were male and over 60 years old. Additionally, the sample was relatively large for a qualitative research but not enough for generalizations. Overall, the research is not generalizable, but it provides some information about the possible specifics of fall experiences, including the attitudes that need unlearning and the fact that more attention needs to be paid to teaching fall prevention.

The fact that falls are a major concern justifies the proposed project (Müller et al., 2019). Research into the causes of falls is important for their prevention. As can be seen from the qualitative studies, patients and caregivers recognize the need for education as well, and while there are limitations to qualitative research, in terms of the needs of participants that are subjective, they are capable of producing relevant data. Since the voices of patients and caregivers are important to consider, the presented literature is critical for understanding the issue.

Educational Interventions Work

Educational interventions work, which is a conclusion made after their scientific investigation (Frith, 2017; Lyons & Hall, 2016; Ott, 2018; Shim & Kim, 2019; Ximenes et al., 2019; Zhao et al., 2019). The investigation is based on a number of different outcomes, some of which will be used in the current project. Additionally, it is important that there are aspects of educational interventions that are not studied very extensively. That includes caregiver education (Schoberer et al., 2016; Xu et al., 2019).

Educational Interventions and Positive Outcomes

Educational interventions work (Frith, 2017; Lyons & Hall, 2016), although that is a simplistic statement. It might be more accurate to state that a lot of educational interventions for falls appear to be having positive outcomes specified by the relevant literature. However, it is also important to remember the work by Hill et al. (2019), which suggests that different interventions might have different levels of effectiveness. This section will present the findings related to educational interventions that do appear to be working.

Perrot et al. (2019) carried out an RCT of a complex fall prevention intervention (n=30), and they found that it reduced the fear of falling. It is important that the intervention was not just education; it was augmented with physical activities. However, the RCT was carried out specifically to compare the education program with physical activities against physical activity alone, which means that it was the educational program that made the difference between the intervention and control groups. Another issue is that the project had a very small sample, with only thirty people involved, most of them women. The final issue was significant differences between the control and intervention group pre-test. The assignment was random, though, which increased the quality of the project and allowed to call it an RCT. The participants were specifically older adults who had fallen at least once in the year prior to the project, and it used the Timed Up and Go test, the Tinetti test, and the Falls Efficacy Scale, all of which are accepted methods. The findings suggested multiple outcomes, but the most important one for the purposes of this project is that patient education was shown to affect the fear of falling in a positive way (by reducing it). More research with a bigger sample would be required, but the project still offers some insight into the effects that training can do in terms of fall prevention.

Ueda et al. (2017) conducted a pilot RCT to test an educational fall prevention program (n=51), and it was effective in reducing fall rates. To be more specific, the program was meant for older orthopedic patients who had experienced at least one fall in the year preceding the project, and it was based on tailored interventions (educational programs) that incorporated floor plans. The sample size was very small for an RCT, but it should be pointed out that the project was a pilot study. Even with the small sample, it was obvious that the intervention group had fewer falls and near-falls, but still, the limitations of the sample are crucial, as well as the fact that the project employed an innovative and never-before tested program. More research is definitely required to make conclusions on the topic.

Carlucci et al.’s (2018) quasi-experimental research (n=215) showed that the intervention, which was studied in the project, improved falls efficacy and functional reach. To clarify, the intervention incorporated “joyful movement,” which is a complex intervention, incorporating education, psychological intervention and retraining of biomechanics. Only one group of older adults was involved in the project, with only 86 who provided feedback and only 102 who completed all the assessments before and after the intervention. Still, the results suggested that the intervention improved both the mobility and falls efficacy of the participants. It should be highlighted that the project incorporated both educational and other elements, which may have affected the outcomes of the study; furthermore, the sample is admittedly small and drawn from one facility. Still, the research clearly suggests that there are benefits to an intervention with an educational component, which is relevant for the present project. The authors also recommend more research on the topic to determine the effects of the program on fall rates.

Ott (2018) focused on community-dwelling patients, which makes the article very suitable for the project. It was a pilot study meant to investigate an educational session and its effect on knowledge, fall prevention behavior and falls. The design was quasi-experimental, which is helpful for the project, but the sample was very small (only 8 participants completed all activities related to the project), which made the tracking of falls difficult. In the end, the authors could only report one fall in the group, which was not enough for statistical analysis, but fall knowledge and prevention behaviors were both demonstrated to increase after the intervention. Overall, the article is very similar to the current project design-wise, and it also provides some information on the ability of educational interventions improve fall knowledge and prevention.

Kiyoshi-Teo et al. (2019) developed a multimethod research that focused on Veteran inpatient fall prevention recommendations and the ways in which they were implemented based on the Plan-Do-Study-Act cycle. Using the fall rates as their outcome, as well as multiple qualitative and quantitative methods, the authors studied two units within one healthcare system (Portland). The use of fall rates can be considered a recommendation for the project on selecting an appropriate outcome. As for the findings, it is particularly relevant for the project that despite the carrying out of relevant educational interventions, which were mostly connected to communicating information on the topic, the patient participants reported a lack of knowledge on falls. From this perspective, it is apparent that there are more and less effective methods of education, and simple communication of information might not be very effective.

Kuhirunyaratn et al. (2019) focused on urban elderly, which makes the article especially relevant for the project. The aim consisted of studying the effects of an education program, and the settings were urban Thailand. The study was quasi-experimental in nature, with 2 different communities involved; two groups were formed with roughly 100 people in each, all of them over 60. They were all registered at one healthcare unit, and the intervention group was subjected to an educational buddy intervention. The pre-experimental fall risks were slightly smaller for the intervention group, but the intervention helped to further decrease it to a statistically significant level. The findings imply that educational interventions can affect fall risks through medicine usage and other factors, which is a helpful finding for the present project.

Lyons and Hall (2016) focused on Latin America and the Caribbean, which may have limited the applicability of the article to the project, but which also shows that the issue of falls is spread worldwide. Furthermore, the article focuses on community-dwelling older adults, which is aligned with the project’s purpose. The goal of the article was to determine the feasibility of an educational intervention implementation meant to prevent falls in Grenada. The sample of the article was small (62 older people), but it shows that older people in Grenada may be interested in learning more about falls and require falls knowledge, which further supports the idea that educational interventions are a helpful and empowering option of fall prevention.

Ferreira et al. (2019) aimed to construct a fall prevention nursing process to be applied to the elderly with Parkinson’s disease. Immediately it is obvious that the target population is rather specific and different from the target population of this project. However, it is clear that both projects aimed to study the older population (over 65), which brings them close. Only nine older people with the disease were involved, and through interviews and workshops, they helped to produce two games and one educational booklet meant for fall prevention. The authors highlighted the fact of empowerment of the patients, as well as the improvement of their ability to exercise self-care with the help of the specified new interventions. This article is very much in line with the present project, particularly from the perspectives of improving self-care. It also introduces the ways in which caregivers, specifically formal caregivers, can assist patients with fall prevention. However, the population of the article is specific, and while the approach toward the development of the materials can be used outside of the article, the materials themselves have not been tested in terms of their effectiveness. Thus, the article has its limitations, but it shows that educational materials can be developed in ways that empower the target population.

To summarize, there exists sufficient research on the topic to suggest that different educational interventions for fall prevention work. Admittedly, it should be highlighted that the studies typically have small samples or otherwise limited samples, which are also used to test individual interventions. It is rare that one and the same intervention is tested several times. In this light, the work by Hill et all. (2019) is especially important, which is particularly true because it contradicts the rest of the evidence that suggests the usefulness of educational interventions or interventions with educational components (see below). Overall, it can be suggested that more research is required, especially dedicated to one particular intervention. Additionally, it is clear that different methods of establishing intervention effectiveness can be discovered, and the next subtheme is going to discuss this topic in detail.

Methods of Establishing Intervention Effectiveness

Effectiveness of prevention interventions can be established with the help of fall numbers, likelihood, frequency, and so on. Overall, there are a lot of valid methods of determining the quality of an intervention. In this section, some quantitative options will be presented; qualitative ones are outside of the realm of this project because it is quantitative.

Nakagami-Yamaguchi et al. (2016) conducted a pre- and post-test study, in which an animation movie-based prevention method reduced fall frequency and likelihood. The animation was supposed to provide education, which explains the relevance of the study for this project. The study was a pilot one, and it involved both patients and caregivers, which is why this not very recent study was included in the review. However, the project mostly implied nurses as caregivers, which makes it less relevant. Still, the research showed a clear need for educational intervention, with only 30% of the patients understanding the instructions on nurse interaction in terms of fall prevention before the intervention. Furthermore, the findings implied that the 269 patients who had been involved and 304 nurses who had been involved managed to reduce the patient falls to 8.6% from over 15%, which proved to be statistically significant. Furthermore, the likelihood of falls decreased in older patients in a statistically significant way. Overall, the animation proved to be an effective educational tool, and it was specifically more effective for older patients. The study was a pilot one, which explained its limited sample. More research on the topic may be required for additional conclusions.

Dykes et al. (2017) studied falls in acute hospitalization facilities (two of them located in the US), with a focus on an intervention called TIPS (Tailoring Interventions for Patient Safety) and its effects on mean fall rate and injury fall rate. The goal was to test the intervention, which can be considered a clinic decision support tool, and the findings showed that one of the facilities showed significant improvements in both desired outcomes, while the other one only showed improvements in one of them. The study’s strong feature is that it had tracked the compliance of the intervention, but other confounding variables were not considered in this pilot implementation project. More research is clearly required for conclusive statements, especially research in different settings. Still, the project clearly shows that certain measures of falls can be used in the research for fall prevention interventions.

Jie and Deng (2019) tested a fall education (n=178 female patients with osteoporosis) in a pre- and post-test study, which showed a reduction in fall risks and fall likelihood. The topic was rather specific in that the authors focused on osteoporosis patients receiving zoledronic acid, which was explained by a high rate of falling in the population, as well as the negative outcomes associated with falls in the population (specifically, morbidity and mortality). The project involved controls and random assignment to the respective groups (86 intervention cases with a fall prevention education at the time of zoledronic acid administration, as well as a telephone follow-up and another session a month later). The data were collected with the help of a survey before the intervention and after it (one year later). The project has a relatively small sample, and it only recruited female patients with osteoporosis, which limits the findings’ applicability, but still, the results imply that it is possible to reduce fall risks with the help of a fall prevention education.

Bargmann and Brundett (2020) report the findings of a practice project meant to test an intervention bundle for fall preventions with two educational elements (out of five), including daily education of patients and an educational handout. Other elements included patient risk assessment, ensuring the carrying out of previously established fall prevention strategies, and a “safety agreement” with the patients meant to facilitate their interactions with nurses. The fall rate decreased significantly (by a half), and in addition to that, the authors highlighted an increase in staff compliance with fall prevention strategies. They indicated issues with staff turnover as an obstacle to the project but pointed out that communication between the nurses and patients was of utmost importance. The findings suggest that interventions with an educational component can be helpful and that fall rates can be used to assess such interventions and their success.

Shim et al. (2019) carried out a quasi-experiment aimed to investigate the effect of fall prevention education in older patients. The project had an experimental and control group, both about 30 people, which is a small sample as the authors highlight. It still allowed for the use of parametric tests meant to compare the fall knowledge and fall prevention behavior in patients. The intervention consisted of an educational DVD, as well as leaflets; they demonstrated improved scores in both parameters. The information about falls in either group was not provided. The authors highlight that the desired outcomes were achieved with the help of this complex education system, which implies that it can be further researched. From the perspective of the current project, the article is relevant methodology-wise, as well as in terms of demonstrating the effects of educational fall prevention interventions.

In summary, there are a lot of sources which can be used to help direct a new project on the topic of educational fall prevention interventions. It is especially true since they are sufficiently diverse, with different approaches to measuring the same variables. Overall, this literature assisted in producing the methodology for the present project

STEADI Studies

The STEADI intervention has been studied and written about in professional literature (Casey et al., 2017; Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019; Mark & Loomis, 2017; Nithman & Vincenzo, 2019; Sarmiento & Lee, 2017; Vincenzo & Patton, 2019; Urban et al., 2020). However, not all the projects are relevant for the current project. In fact, none of them considers the educational component of STEADI from the perspective of caregiver education. Among these, the article by Eckstrom et al. (2017) can be considered the basis for the CDC guidelines concerning the implementation pf STEDI. The following projects can be used for gaining some information about the intervention.

Eckstrom et al. (2017) incorporated STEADI into a clinic’s routine. The tools that were used to that end included EHR, training, as well as workflow tools. Three-fourth of the providers were involved, and they screened more than a half of the patients (773) throughout six months, most of which required and, therefore, received STEADI interventions. The authors concluded that the screening burden was reduced by STEADI, although the number of patients considered high-risk increased as a result. The only intervention that was not carried out often was the reduction in the medications that affect fall rates. Overall, the project provides an example of STEADI implementation, which will be used during this project’s attempt as well.

Vincenzo and Patton (2019) aimed to determine the adherence to STEADI among older patients. They performed a set of semi-structured interviews, showing that only half of the forty participants who fully participated in the study followed the STEADI recommendations after 6 months, and only about three-fourth remembered any recommendations. Additionally, 32% of them fell, and out of these people, more than half did not follow STEADI recommendations at all. Overall, the authors unearthed an important issue of long-term STEADI effects. While the project will not be able to track long-term effects, this issue will inform STEADI implementation.

Casey et al. (2017) carried out a study meant to apply STEADI in one specific clinic with the help of the Kotter framework. Over 400 people were screened during the entire project, and the researchers reported that the successful implementation needed relevant EHR tools, as well as a workflow that would incorporate STEADI, and appropriate leadership. The researchers describe the project as successful, but there is no direct assessment of STEADI in the project, which was not its aim. Therefore, more research on the topic is required.

Taylor et al. (2019) focused on using STEADI through an interprofessional education approach. The justification for the research consisted of highlighting the importance of being trained in working with older adults and fall prevention for healthcare providers. The article involved healthcare students, faculty and older adults (all below 32 people) with the goal of training the former and involving the latter in carrying out a STEADI intervention. Student knowledge increased in a statistically significant way, especially with regards to fall prevention and STEADI, showing that the latter could be used for geriatric health education. The sample size is not very large, but it can still be used to demonstrate STEADI utility, especially in fall prevention education.

Wongrakpanich et al. (2019) carried out a quality improvement project meant to investigate the STOP-FALLING checklist, which was developed specifically for one long-term care facility. The project used a pre- and post-test approach (three months before and after checklist introduction) with a rather small sample of 32 patients. The findings showed an improvement in fall rates and staff satisfaction, a reduction in falls with minor and major injuries, and a reduction in frequent fallers. The approach of the project could be considered a pilot study, which is why the small sample is acceptable, but the authors propose further advancing the study of the checklist. This article is relevant for the present project because it shows the ways in which a fall prevention intervention can be studied while also highlighting the importance of tools like the ones included into STEADI in fall prevention.

Urban et al. (2020) commented on the lack of the literature on fall prevention in primary care and proceeded to introduce the STEADI program (with a focus on its education materials) in a primary care clinic. The staff involved included 29 people, and they were educated on the topic of falls with the help of the STEADI materials, which resulted in a statistically significant increase in the mean knowledge score before and after the test, although no statistically significant difference between the control group and education group in terms of STEADI use was found. The authors concluded that the use of STEADI is recommended from the perspective of knowledge improvement, but the project was not successful in implementing the toolkit. The article can be used to demonstrate the value of STEADI kits, but it does not provide directions on how to implement them.

Lohman et al. (2017) focused on investigating STEADI and its validity and adaptability. The article involved using the National Health and Aging Trends Study to gather information about a large, representative sample of older adults (over 7000 people). The information was used to apply the STEADI toolkit to the adults and determine its ability to predict outcomes, including falls and mortality. The findings suggested that the STEADI toolkit was valid in that it was able to predict adverse outcomes.

Mark and Loomis (2017) focused on the implementation of STEADI toolkit into clinical practice. The article is theoretical, and it consists of a literature review and a commentary on STEADI tools. It concludes that the main issue that STEADI implementation is likely to involve is the actual utilization of the toolkit, which is evidenced to be effective based on the literature review. The authors highlight that STEADI is capable of preventing falls and saving funds, but they recommend more research on the validity and reliability of the toolkit. Overall, the article can be used to support the use of STEADI and introduce information about it into the project.

Johnston et al. (2019) implemented STEADI to determine its impact on a primary care system. The article is especially relevant for the project since it was carried out in the US and since it involved outpatient clinics (n=14). The number of people involved exceeded 12000 of people older than 64, which is very large for a study that only involved a part of New York as its setting. The STEADI was used to develop Fall Plan of Care for a group of participants who were at fall of risks; a control group with fall risks and no plan of care was introduced, as well as a group that was classified as having no risk of falls. The plan of care based on STEADI was capable of reducing the likelihood of fall-related hospitalizations in a statistically significant way compared to the group with no plan of care; in fact, the people with a plan of care showed similar likelihood of fall-related hospitalization as people who were classified as having no risk of falling. Therefore, the STEADI toolkit was shown to be effective in preventing fall-related hospitalization, which implies that it was also helpful in reducing the likelihood of falls. Naturally, the article described a study that took place only in one region of the US, which implies that the results for Florida might be different, but still, this article offers a lot of evidence for the present project.

In summary, the presented literature suggests that STEADI is a valid and helpful tool, and even though there is little investigation of its effectiveness, research suggests that it is very capable of bringing along positive outcomes in terms of fall prevention. Very recent sources show that the issue is topical, but the findings also suggest that more research is required. Overall, the literature can be considered justifying the current project by indicating that the intervention is an evidence-based solution.

Caregivers and Fall Prevention. Caregivers are rarely considered in fall prevention, especially informal and domestic caregivers, which makes every recent article on the topic critical and relevant for the current project (Schoberer et al., 2016; Xu et al., 2019). However, there is also a limited number of relevant sources. A number of relatively recent articles have been identified that considered caregivers, although their contribution to the topic is not equal.

Xu et al. (2019) provided a qualitative project meant to develop a fall prevention program for stroke survivors based on the survivors’ own opinions (9 people), as well as caregivers and helpers (8 people). Through interviews and thematic analysis, the project determined that both survivors and their caregivers would benefit from fall prevention interventions, and they determined that caregivers were often overprotective of the survivors they cared for. These findings suggest that caregivers are an important element of fall prevention, but it is merely a qualitative study, which means that its level of evidence is not very high (Polit & Beck, 2017). Overall, this study is only included because there is little to no information on caregivers.

Schoberer et al. (2016) is a relatively outdated source, but it provides a qualitative study of the perspectives of patients and caregivers, including family caregivers, regarding an educational fall prevention intervention (specifically, a fall prevention brochure). The participants (25 residents, 12 family caregivers and 14 nurses) reported that they were interested in learning about fall risks, as well as methods of recovering after one, and family members also wanted to know more about strategies meant to prevent falls. This information suggests that education for caregivers would have been very helpful, which justifies the present project.

Hoffman et al. (2018) presented an analysis of different categories of caregivers from the perspectives of their association with falls and injurious falls. The findings suggest that different levels formal and informal care are associated with a reduced risk of falls, as well as injurious falls (although to a smaller degree). The total sample was almost 8000 people, 20% of them with caregivers. The project also took place over several years, which further boosts its quality. The sample does have the limitation of being only applicable to the US, but still, the project suggests that caregivers are important for fall prevention and that the amount of effort their offer along with their qualifications can improve the quality of that effect.

Nakagami-Yamaguchi et al. (2016) carried out a study that was aimed at developing an educational movie as a method of fall prevention. The evaluation involved implementing the movie in a pre- and post-test fashion. Both patients and caregivers were involved in movie evaluation, with pre-intervention surveys showing that before the movie, not all patients understood the fall prevention instructions correctly (for one type of instruction, only 33% of the surveyed patients had a correct understanding). After the intervention, understanding improved, and the number of falls decreased significantly in older patients. The findings suggest that novel methods of education can improve the participants’ understanding of nurse instructions, and it also suggests involving caregivers in the process of fall prevention.

Wilkinson et al. (2018) presented a literature review on the strategies of fall prevention outside of hospitals exhibited by older adults and their caregivers. The 17 studies, only 2 of which explored exclusively caregiver perspectives (four additional ones explored those of caregivers and patients), included a sample of only about 600 people, 102 of them caregivers. The findings suggested that the caregivers were mostly using the strategy of discouraging independence, which highlights that they might be ill-equipped to provide care and require additional education on the matter.

Zhao et al. collected information about acute care hospitals, which limits the article’s applicability to this project, but it should be highlighted that the article is a very thorough and well-documented investigation of implications of factors associated with falls (including injurious falls) for the prevention of falls. A major issue of the paper is that it is a literature review and not a primary source, but literature reviews are also useful in the development of arguments (Polit & Beck, 2017). Regarding the present project, the article justifies it by discussing educational interventions as an existing method of both preventing falls and engaging patients and their caregivers in fall prevention. It is noteworthy that the authors conclude that there is a need for more than single-component interventions, but they recognize the usability of educational programs as well.

Vonnes and Wolf (2017) focused on falls in oncology, which limits the applicability of the findings. Still, the project tested a specific fall prevention technique (Fall Prevention Agreement), which involved formal caregivers and older adults. The falls demonstrated a reduction over the course of two- and eight-quarters, the former demonstrating a sharper decrease of 37%, and the latter still showing a substantial decrease of 58.6%. The authors’ explanation concerned itself with the ability of the Fall Prevention Agreement to improve adherence and participation both for the team and patients. Additionally, the article comments on the ability of Fall Prevention Agreements to involve informal caregivers and families. Thus, the article exists mostly to highlight the importance of actual participation in fall prevention activities of informal and formal caregivers and patients, demonstrating that when a measure meant to enhance it is implemented, a reduction in falls is observed. From this perspective, the article can be used to confirm the importance of the contribution of different groups to fall prevention, including informal caregivers.

Ximenes et al. (2019) presented a study that began with the construction and ended with assessment of a booklet developed by nurses and patients and aimed at the prevention of falls in medical institutions. As a result, the topic is not fully consistent with the one selected for the project, but it can still be useful from the perspective of educational materials being useful in fall prevention. The booklet was validated and demonstrated Content Validity Index of 0.98-1. Patients deemed it understandable. The main weakness of the paper is the lack of actual testing of the booklet, but the authors justify the need for educational materials in fall prevention and additionally, they highlight the ability of patients to contribute information relevant for educational material design. Thus, the article supports the importance of educational materials and offers some information about the empowerment of patients and their families through their involvement in fall prevention.

Ang et al. (2019) carried out a qualitative study to gather information about the concerns exhibited by caregivers regarding the risk of falls in their patients. The size sample was small (22), and the authors only used one tertiary hospital from Australia. The goal was to interview (semi-structured) the participants and determine the themes emerging from the collected data. The authors found that there were issues related to caregiver awareness of fall risks, as well as their knowledge and ability to support their patients. Furthermore, a separate theme was connected to the support provided by healthcare professionals. The authors concluded that caregiver knowledge and awareness are critical in fall prevention, as well as their own well-being, as far as the concern about the patient falling is determined. Their recommendation is for healthcare professionals to offer caregivers sufficient information about falls to improve fall prevention and their well-being.

Black et al. (2018) offers a model of fall prevention that focuses on several elements, including patient mobility, injuries and fall prevention, as well as ensuring caregiver safety, especially from the perspective of musculoskeletal injuries. It should be highlighted that the article focuses on critically ill patients and ensuring their mobility, but still, the article is relevant to older people and fall risks since those are taken into account. Additionally, the caregivers in question are predominantly formal caregivers, but informal ones are also considered. Finally, the authors’ methodology consists of a literature review; the article is not a primary source. All of these features should be taken into account, but it can be suggested that the article demonstrates the importance of viewing the safety and other features of the well-being of caregivers while preparing fall prevention strategies. The numerous issues with the relevancy of this article can be used to also highlight the lack of the literature on the topic. Caregivers are not studied as extensively as patients when it comes to fall prevention.

Juckett and Poling (2020) focused on home- and community-based services; specifically, on their ability to perform fall risk screens and referrals to relevant services. The article used a qualitative approach, involving 26 staff members and administrators with the help of interviews, including focus group interviews. The results suggested that the staff of such services was able to perform fall-related services but was not trained to do it and had even less knowledge on actual fall prevention. Additionally, the service administrators reported a lack of connection with fall prevention providers. The authors recommended rectifying the named issues. It should be highlighted that this article discusses a form of professional caregivers, but it highlights the different types of services that such caregivers can offer. Additionally, it shows that even professional caregivers can require help in the form of fall prevention education. Therefore, the article is useful for the project by demonstrating the importance of fall prevention education.

Meyer et al. (2019) focused on people with dementia and the difficulty of engaging them into fall prevention strategies, highlighting the importance of caregiver involvement in such instances. The authors developed a discussion tool meant to engage both patients and caregivers in fall prevention. Aside from carrying out a literature review to support the tool, the authors also trialed it for six months with 25 patients, who had increased fall risks, and their caregivers. The findings allowed to conclude that the tool facilitated communication and engaged participants (both patients and caregivers) in fall prevention. It should be highlighted that this research was aimed specifically at people with dementia, which provides additional motivation for including caregivers in fall prevention. However, it is noteworthy that the goal of the authors was to engage the patients as well. The project is, therefore, capable of highlighting the importance of empowering both patients and caregivers through communication of fall prevention information.

Montgomery et al. (2020) focused on a program meant to training non-clinical caregivers to prevent falls outside of clinics, which is why this article is especially relevant for the project. The authors justified the approach based on the importance of falls for older adults, as well as the lack of routine incorporation of fall prevention into nursing practice. The solution that the authors proposed was the involvement of non-clinical caregivers with the help of the STEADI tool, which makes the article further relevant for the project; the use of pre- and post-test design is additionally able to direct the project’s design. Statistically significant increases in knowledge and confidence were identified, and the participants showed good retention of the information during the follow-up. However, the sample was small (less than 30 people), and it was only from the US, which is relevant for the present project but might not be very generalizable. The findings are still important, and crucially, they highlight the importance of caregivers in fall prevention, as well as the gaps in knowledge they had exhibited before the project.

In summary, there are a few sources on caregiver involvement in fall prevention, including prevention that is meant to be carried out with the help of education, that are published in the past few years and are in English. This seems to be a major drawback of the current literature on the topic, especially since the presented sources, as well as the previously discussed qualitative sources, suggest that these actors in fall prevention are important. Thus, it is critical to produce more literature on the topic, which justifies the present project. Additionally, the need for education and the potential positive outcomes of educating caregivers does receive some support in the recent literature. Therefore, the project can be considered justified.

Data Source

To justify the project’s data source, three recent articles will be reviewed to demonstrate the appropriateness of using EHR for research purposes. Thus, Penning et al. (2020) highlight that EHRs data is helpful in multiple types of studies, especially retrospective ones, but they also suggest that prospective studies should also employ the data source, in particularly, through quick access to EHRs that allows near real-time data utilization.

International Research

Khairat et al. (2018) highlighted the importance of EHRs being accessible to researchers, possibly internationally, with the aim of disseminating data for research and related findings. With the help of a survey, the authors demonstrated that EHR accessibility remains an issue, which has implications for research. Bruland et al. (2018) focused on technical solutions in pseudonymization of EHR data and demonstrated that the integration of relevant services would facilitate research activities by reducing the time required to extract the data by more than 50%. Overall, it is well-acknowledged that research and EHRs can coexist, and that the latter are a very good source of research data.


Research suggests that the specific topic of the project is not very well-researched. Indeed, fall prevention education is a common topic in research, but specifically STEADI education is less often investigated. Fall prevention education for caregivers is rather rare. The topic can be considered a form of a research gap, which is exacerbated by the fact that most studies present individual interventions that need to be studied more than once and preferably with a large sample. Since sample limitations were the most common limitations for the literature described so far, it is a major issue. However, other limitations were also noteworthy, such as the specifics of the methodology (for example, the lack of randomization), the drawbacks of the methodology (for example, statistically significant differences between the two groups being compared pre-test), and other issues. Overall, the research is based on the literature that suffers from limitations and is occasionally not very recent specifically because there is not enough literature.

However, the findings still produce themes within this literature that can be helpful for the project. Thus, it is apparent that falls are a dangerous issue; that much is established in every piece of literature, but it is especially clear from the statistical sources included precisely for this reason. Consequently, it is apparent that falls need to be addressed, and one of the methods of doing so is fall prevention education, which follows from some of the causes and risk factors of falls. Education has been studied in a variety of circumstances, showing that it can achieve positive outcomes, including a reduction in falls, near-falls and fear of falls, among a few other things. STEADI as an intervention that is focused on education is generally supported by the literature, although more research would be helpful that would not focus on its implementation above all else.

It is also noteworthy that the current project will be using some of the parameters that have been established as potential outcomes of educational interventions for falls. Orem’s (1985) theory and the IOWA model are going to be used in concert, with the former explaining the key terms and variables, and the latter assisting in the process of STEADI implementation, which, as is shown in the literature, is not an easy endeavor. Research clearly demonstrates that the topic can be investigated in a variety of ways, including qualitative and quantitative ones. Controlled trials and randomized controlled trials were among the options that could be considered. They were employed to figure out the effectiveness of an intervention, which makes the suggestion appropriate for the present project. The details of the methodology will be discussed in the next chapter.


Falls during hospitalizations are an important safety concern, leading to increased healthcare costs, higher risks of fall-related morbidity and mortality. According to CDC (2019), about 25% of elderly patients experience at least one fall per year, with more than 50% of those who fell not telling their provider about the incident. As one of the most common incidences after hospitalizations, falls and following traumatic injuries can and should be prevented. The STEADI program has proven to be an effective tool for screening elderly patients, assessing their risks of falls, managing the underlying reasons whenever possible and reducing the number of falls in elderly patients in recent years. However, STEADI algorithm has still not been adopted in some healthcare settings for a number of reasons, such as lack of resources or competing tasks, also requiring time and attention of the administration and practitioners. The significance and the potential risks of falls in the elderly patients and following consequences can be underestimated and underreported. The objective of this project is to evaluate the potential benefits of STEADI algorithm, compared to the currently used practices, and collect the data which could provide rationale for direct practice improvement (if appropriate).

Statement of the Problem

Falls are a common problem among elderly individuals both in the hospital and at home. The significance of the problem is often underestimated, as elderly individuals do not report their falls to their doctors and consider these accidents as insignificant. Addressing the problem of falls in elderly patients through effective risk screening in clinical settings can potentially reduce the risks, but effective practices and protocols are necessary.

It was not known if or to what degree the implementation of the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Algorithm would impact the identification of and subsequent referrals to physical therapists to improve gait and balance for fall risk when compared to current practice among adults 65 and older. The implementation of a STEADI program in a healthcare setting requires continuing education of health professionals and additional resources for making fall prevention a part of clinical practice. Therefore, before changing protocols and operations, the efficiency of STEADI program should be assessed, compared to the existing clinical practice that is currently used. The measurement and assessment of the patient outcomes and updated clinical practices is important for evaluating the effectiveness and appropriateness of the chosen interventions in the clinical setting.

Clinical Question

To demonstrate the statistically significant relationship between the intervention and practice improvement (if any), the project collects numerical data on the intervention implementation and patient outcomes. It is hypothesized that risk screening and following referrals to physical therapists and other specialists aimed at addressing the detected impairments can reduce the risks and fall incidence. The effectiveness of risk screening is measured through the detected risks, following referrals and rates of following falls.

The following clinical question guides this quantitative project:

  • Q1: To what degree does the implementation of CDC’s STEADI algorithm impact the identification and the following therapist referrals regarding the threat of falls in patients aged 65 or older, compared to the current practice, in a primary care clinic in the urban Florida setting?

Compared to the STEADI algorithm, the current practice does not include the risks assessment of falls in patients or education on fall prevention for healthcare professionals or patients. The independent variable in this project is the clinical practice change in the form of STEADI algorithm implementation and the dependent variable is the number of referrals concerning the instances of falls reported in elderly patients after the fall prevention program is put into practice. The number of post-intervention falls will be compared with the baseline data on outpatient falls, reported during the same period of time before the intervention. The baseline data will be collected from the electronic health records (EHRs), whereas the post-intervention data will be collected from the reports filled by the practitioners working with the elderly patients participating in the study. The post-intervention number of falls is an outcome variable, with nominal level of measurement. Additionally, to evaluate the efficiency of the program implementation, the researchers should consider the variable of health professionals’ knowledge on the most important steps and principles of STEADI program (ratio level of measurement). The analysis of this dependent variable will allow evaluating viability of the study and its possible limitations.

The data will be collected within group, from EHRs for baseline evaluation and from practitioners’ reports after the intervention. The choice of this approach to collecting data is explained with the researchers’ convenience and similar risks of experiencing falls in the same individuals, which makes them an appropriate choice to be their own control group. Thus, opportunities for minimizing costs associated with the data collection process while also avoiding biases in data collection will be pursued.

Project Methodology

The quantitative research method is used in this project to collect measurable data and establish a causal relationship between the independent and dependent variable if any. The choice of quantitative method is explained with the nature of the variables tested, as the number of falls can be quantified, and the needs of the project to establish a causal relationship, which would require using statistical analysis and measurable variables. The emphasis of this projects is placed on the measurement and establishing a relationship through variables as Pope and Mays (2020) suggest. The clinical question is narrowly worded and the answer requires numeric data that can be further statistically analyzed. The study uses deduction, first conducting a preliminary literature search, selecting the existing theoretical framework and further testing its applicability in the real life hospital setting (Zhao et al., 2021). By contrast, qualitative research method implies the use of induction, in which the analysis starts from collecting data and only later looking for the appropriate theoretical explanations, which could potentially justify the collected evidence.

Quantitative research uses deductive reasoning and generalization. Therefore, an established theory of STEADI algorithm is used to analyze the process of fall prevention, its effectiveness and outcomes. The concepts are refined into dependent and independent variables and then evidence is collected to conclude whether the theory of STEADI effectiveness is supported in the given clinical setting (Frieson et al., 2018). Next, generalization will be made to conclude to what extent the findings of this project can be applied to larger population.

Project Design

A quasi-experimental design was chosen for this project due to the impossibility of randomization of participants and a limited sample size. The project is implemented in a partially controlled setting, in which the practice change is initiated but some other criteria, such as patients’ and providers’ engagement and setting schedule cannot be manipulated. Whereas this quasi-experimental design is useful in testing the effectiveness of STEADI as a fall prevention method, the lack of randomization of participants increases the risks of exposure to a great number of internal and external validity threats (Rockers et al., 2017).

The relationship between an independent (predictor) variable – the implementation of STEADI algorithm and the dependent (outcome) variable expressed in the number of falls after the intervention is quantified and further evaluated in order to conclude whether it is statistically significant. The causal relationship between the intervention and the number of reported falls in the participants can be established through this design, but the collected evidence and its analysis can be insufficient for making generalizations. Furthermore, STEADI method is recommended by CDC, and thus, there is evidence that the approach is beneficial and assigning a control group not using this method would be unethical. Instead, the participants and their data before the intervention will be their own control group.

Pre-implementation data will be collected from EHRs to evaluate the patients’ risks of falls before the intervention (Khairat et al., 2018; Durgun et al., 2021). The three stages of STEADI approach include screening, assessment of risks and further intervening to decrease them. The data on the main risk factors, including poor gait, strength and balance, vitamin D deficiency, visual impairment and other comorbidities will be collected from EHRs and practitioners’ reports from physical examinations.

The key measurable outcome will involve the change in the number of referrals for falls in elderly patients, which, in turn, will signify a notable drop in the instances of falls among the target population members.

Population and Sample Selection

The project will be conducted in an outpatient clinic in urban Florida. The total population of this clinic is estimated at 4000 patients. The clinic size and convenience are the main factors explaining the choice of non-randomized convenience sample, which is associated with several limitations, but which can be applied in this setting. The total population of the clinic is comprised of middle-class urban citizens, living in the clinic neighborhood. The total clinic population includes diverse ethnic and age groups, including non-Hispanic whites, Hispanic citizens, and African American citizens as the most substantial groups. The older patients aged 65 or older comprised over 40% of the total clinic population. The data on 215 patients were included into the sample, but later only 200 patients were left, because 15 of them failed to come for a follow-up.

As a result, the project sample will include 150 participants, both males and females, aged 65 or older. The only inclusion criteria used for this sample were the participants’ age and risks of fall (Dhalwani et al., 2017; Meyer et al., 2019). Whereas convenience sampling was used for this project and it is considered as non-representative, by including all elderly patients who were available for the experiment, without excluding individuals based on comorbidities, this study presents results which can be further generalized for larger population.

Based on the power analysis of this sample, it can be concluded that this sample size, namely 150 participants, allows making generalizations only for the 65+ population of this clinic, but not all the elderly population of the country in general. The use of a convenience sample is an essential limitation of the project, associated with restricted opportunities for generalizations. Therefore, the proposed method needs to be replaced since it will not allow delivering generalizable and trustworthy results. Instead, the cluster random sampling approach will have to be deployed as the tool for conducting the research. The specified approach will help minimize the resources and, thus, reduce costs, while offering accurate information in the analysis output.

Population and Sample Selection

The project will be conducted in a small outpatient clinic in urban Florida. This direct practice improvement will occur over 4 weeks beginning with a 20-minute educational presentation, presenting the STEADI algorithm to providers, followed by implementation of STEADI fall risk assessment during an elderly patient’s visit and following referrals if necessary. The total population of this clinic is estimated at 4000 patients. The clinic size and the researchers’ convenience are the main factors explaining the choice of non-randomized convenience sample, which is associated with a number of limitations, but which can be applied in this setting. The total population of the clinic is comprised of middle-class urban citizens, living in the clinic neighborhood. The total clinic population includes diverse ethnic and age groups, including non-Hispanic whites, Hispanic citizens, and African American citizens as the most substantial groups. The older patients aged 65 or older comprised about 15% of the total clinic population.

The project sample includes 150 participants, both males and females, aged 65 or older. The only inclusion criterion used for this sample was the participants’ age. The participants were not excluded based on their past experiences of falls, mental conditions, substance abuse status or impairment of musculoskeletal system, which could increase the risks of falls. Whereas random cluster sampling was used for this project and it is considered as non-representative, by including all elderly patients who were available for the experiment, without excluding individuals based on comorbidities, this project presents results which can be further generalized for larger population.

Based on a power analysis of this sample, it can be concluded that this sample size, namely 150 participants, allows making assumptions concerning the efficacy of the suggested measure for the 65+ population of this clinic, but not all the elderly population of the country in general.

Instrumentation or Sources of Data

The data to be collected for this project includes data on the number of falls before the intervention, retrieved from EHRs, and data on post-intervention number of falls, reported by physicians in special reports. Demographic data and health history of the participants are collected from EHRs for analysis and assessment of the risks of falls. Additionally, risk assessment requires physical exam and balance tests, including a 30-second chair stand test and 4-stage balance test (CDC, 2019).

On the stage of risk assessment, practitioners will need to fill in the risk factors form (Appendix I). This form, complimented by physical balance tests, allows evaluating the risks of falls in patients and further referring them to specialists, who can help reduce their risks through therapy addressing their weak points. The detected problems with gait and balance will require physical rehabilitation, which would allow improving patient’s physical fitness and reducing the risks of falls.

Fall risk patient referral (Appendix II) is a questionnaire, which specifies the patient’s deficits and recommended therapies. This documentation should be used to assess the amount of prevention strategies implemented in the program. These questionnaires are an important source of information on the stage of evaluating the quality of the intervention and its outcomes.

Major outcome of this project is evaluated though counting the number of patient’s referrals and number of falls after the STEADI intervention was used and comparing it to the number of baseline fall incidences, as reported in EHRs. The independent variables are the STEADI algorithm educational presentation and the use of STEADI fall risk screening tool upon a geriatric patient visit. The dependent variables are represented by the measurable variables that are affected by the intervention, which in this project will be the number of referrals and the number of falls.


The internal validity, referring to the integrity of experimental design, assesses whether the intervention has impact on the outcome or not. In terms of internal validity, this project collects substantial data on the quality of risks assessment and reduction, which will allow determining whether the intervention played any role in changing the outcome. Factors threatening the internal validity include the factors other than the project intervention that could influence the independent variables. Potential internal validity threats in this project include mortality and response bias, when patients can be unwilling to share the information on falls or fall-related risk factors or forgetful about instances, considering falls or impairments increasing their risks of falls as insignificant and not worth mentioning (Farelly, 2013).

The external validity is defined by the degree to which the intervention can be generalized and used in other settings (Farelly, 2013). The external validity of this project is restricted due to non-randomized sampling method and collection of data in only one isolated primary care setting. Therefore, the project has only limited potential in terms of its external validity, because its findings can be applied to non-project patients in this clinic, but not other patients outside the clinical setting under analysis.

Methods to control for validity threats in this project will include extracting data from the same team of medical providers over the course of four weeks. All provider will have identical access to STEADI algorithm reminder template. The quality improvement intended will be increased fall risk screening and prevention.

STEADI fall risk algorithm has demonstrated predictive validity for fall risk among US older adults. The specificity of the STEADI fall risk algorithm was based on differentiating between risk factors specific to fall prediction versus risk factors attributed to poor physical health in general. There is evidence that STEADI fall risk categories have specificity in predicting risk of falls, independently of mortality, disability and other health-related risk factors (Lohman et al., 2017). Therefore, STEADI has proven to be valid in measuring what it aims to measure.


The reliability of the project refers to its consistency in measurements and data collection (Farelly, 2013). The reliability of this project’s findings depends on the appropriateness of the experiment conditions and presence of non-controlled and unpredicted factors and variables. One of the major disadvantages of a field experiment is the inability to control and predict every variable. Unlike lab experiments, experiments in real life settings are associated with continuous changes and unpredictable circumstances, which can interfere with the data reliability. For example, one on the important factors interfering with the project outcomes is the patient’s failure to report a fall, considering it insignificant, but seriously affecting the data, necessary to measure outcomes.

Chi-square test of independence can be applied for evaluating distributions in data (Shi, 2018). This test is robust and informative and it offers detailed information to be extracted from this statistical method. The strength of association is evaluated on a scale of 0 to 1, with values closer to 0 signaling weak association, and values closer to 1 signaling stronger association (Shi, 2018).

Data Collection Procedures

This project will start from a brief educational presentation on the importance of fall risk screening and the main principles of STEADI algorithm, conducted during a lunchtime. In the next step, within four weeks, patients age 65+ visiting a healthcare facility for any health concerns, will be screened for their fall risks, using a STEADI algorithm. Next, patients, who are screened for risks of falls, will be assigned to the different groups, according to the risk level and the most appropriate intervention for decreasing the risks (Chang et al., 2019). Whenever appropriate, high and moderate-risk patients will be referred to physical therapists for addressing the risk factors. Next, baseline measurements will be completed for the patients who have been screened with the STEADI tool. The information on pre-intervention falls is retrieved from EHRs. After the risks are assessed, the high-risk patients are referred to specialists and therapists who can help them address their deficits and reduce the risks (Cho & Jang, 2020). Next, during after-intervention period, cases of physical therapy referrals due to incidences of falls and their more detailed circumstances need to be written in a daily report. Pseudonymization should be used for all data to prevent bias on the stage of data analysis (Bruland et al., 2018).

Data Analysis Procedures

After 4 weeks of project implementation, data extraction will be carried out with a chart review. The results will be collected and numerically coded within an Excel Spreadsheet. The data will be stored on the primary investigator’s personal laptop, protected with logins and passwords. After the project is completed, the data will be deleted from the hard drive.

To analyze the collected data and detect the relationships between the intervention and outcomes if any, this project will use a t-test and analysis of variance (ANOVA), which would allow detecting causalities, relationships between phenomena, identifying systematic and random factors (Farrely, 2013). The independent variables in this project include the intervention, demographic data, such as age, ethnicity, past history of falls. The dependent variables include health knowledge on fall prevention and post-intervention number of falls.

Potential Bias and Mitigation

The use of a convenience sampling method can be a source of bias and a threat to internal validity of the project (Hopewell et al., 2018). Used for the convenience purposes and due to the limitations of a single setting used, this method can be associated with a number of restrictions, such as inability to generalize findings to other locations or general population (Shi, 2018). Another important limitation and source of bias could be sampling response bias. The incidents of falls can be underreported by the patients, who may consider them as insignificant or in fear of practitioners’ criticism. Specifically, the choice of wording for a particular question in the questionnaire may cause the participants to respond in a certain way. Moreover, the practitioners’ reports may be lacking information on falls due to the lack of time necessary for filling the reports or technical errors.

Ethical Considerations

The evaluation of potential ethical issues in this project will require considering levels of risk, coercion, and rationale of the project. The project will meet the key principles of the Belmont Report, including those of respect, justice and beneficence, implemented to not cause any harm to patients or providers, celebrating fairness and trust and respecting the rights of all the participants (Hopewell et al., 2018). All personal identifiers were coded with numbers, which also allowed to reduce the possible bias.

Due to ethical considerations and the fact that the effectiveness of STEADI algorithm was proven in the previous studies, it would be unethical to have a control group, for which fall prevention is not used (Gomez et al., 2017). For this reason, subjects from the main group are their own control. Additionally, some of the risk assessment procedures can lead to patient’s discomfort and stress.

The principles of ethical research practices and ethical protections were of paramount importance in this quality improvement project. There were no added risks associated with research participation. All personal health identifiers, variables and project data were coded, protected and shared only with primary investigators involved in the project implementation and data analysis.


This project will be guided by quantitative methodology with quasi-experimental interventional design. Possible limitations can be caused by threats to internal and external validity, because the data was collected in only one primary care site by a limited number of medical care providers, who voluntarily agreed to participate in the project. Other limitations included the failure to randomize the project sample and the selection of a pretest-posttest design. The project can also be weakened by the lack of a control group, as the patients will be their own control group.

Major limitations of this project are the use of convenience sample, relatively small sampling size and the patients’ failure to report their falls. The use of convenience sample, lack of its randomization and sample size were discussed above and were dictated mainly by the outpatient clinic size and type of the clinical question. The study has only limited potential for generalization of the study findings to broader patient population.


This chapter presented a detailed discussion of quality improvement, data collection, and analysis methods to be used in this project. The method chosen for this project is a quantitative quasi-experimental study design. The choice is explained both by the project needs and convenience. The numbers and statistical data will be used for establishing whether statistically significant relationships exist between the intervention and project outcomes, that is the application of STEADI algorithm and reduced number of outpatient falls (if any). Due to the limited size of healthcare facility, only a limited number of patients aged 65+ can be invited to participate in this practice improvement, which however, is sufficient. However, manipulation of variables is restricted and occurrence of additional variables, such as unwillingness to report falls, interfering with the study outcomes is possible. The data is retrieved before the intervention from EHRs, and from practitioners’ reports after the intervention is over. A t-test and ANOVA will be used to analyze data and detect relationships between dependent and independent variables. Despite major limitations of non-randomized sample and possible failure of some patients to report their falls, this study is expected to answer the clinical question whether STEADI algorithm is effective for fall prevention in this given facility and whether the practice should be improved by using this algorithm. Chapter 4 will follow to present systematic details of the data collection and data analysis procedures in this direct practice improvement project.


American Psychological Association [APA]. (2019). Publication manual of the American Psychological Association (7th ed.). Washington, DC.

Ang, S. G. M., O’Brien, A. P., & Wilson, A. (2019). Understanding carers’ fall concern and their management of fall risk among older people at home. BMC Geriatrics, 19(1), 1-12.

Armijo-Olivo, S. (2018). The importance of determining the clinical significance of research results in physical therapy. Brazilian Journal of Physical Therapy, 22(3), 175-176.

Bargmann, A. L., & Brundrett, S. M. (2020). Implementation of a multicomponent fall prevention program: contracting with patients for fall safety. Military medicine, 185(Supplement_2), 28-34.

Black, J. M., Salsbury, S., & Vollman, K. M. (2018). Changing the perceptions of a culture of safety for the patient and the caregiver: Integrating improvement initiatives to create sustainable change. Critical Care Nursing Quarterly, 41(3), 226-239.

Bruland, P., Doods, J., Brix, T., Dugas, M., & Storck, M. (2018). Connecting healthcare and clinical research: workflow optimizations through seamless integration of EHR, pseudonymization services and EDC systems. International journal of medical informatics, 119, 103-108.

Burns, E., Kakara, R., Moreland, B., & Henry, A. (2020). Changes in the age-adjusted rate of older adults dying from a fall and reporting a fall and fall injury, 2012–2018. Innovation in Aging, 4(Suppl 1), 774.

Carlucci, C., Kardachi, J., Bradley, S., Prager, J., Wyka, K., & Jayasinghe, N. (2018). Evaluation of a community-based program that integrates joyful movement into fall prevention for older adults. Gerontology and Geriatric Medicine, 4, 233372141877678.

Casey, C., Parker, E., Winkler, G., Liu, X., Lambert, G., & Eckstrom, E. (2017). Lessons learned from implementing CDC’s STEADI falls prevention algorithm in primary care. The Gerontologist, 57(4), 787–796.

Centers for Disease Control and Prevention. (2017). Checklist.

Centers for Disease Control and Prevention. (2019). Algorithm for fall risk screening, assessment, and intervention.

Chang, K. O., Lee, T. J., & Jung, M. Y. (2019). The effect of knowledge, attitude and perceptions of patient safety culture on fall prevention activities in mental hospital nurses. Journal of the Korea Academia-Industrial cooperation Society, 20(5), 372-383.

Cho, M. Y., & Jang, S. J. (2020). Nurses’ knowledge, attitude, and fall prevention practices at South Korean hospitals: A cross-sectional survey. BMC nursing, 19(1), 1-8.

Collaborative, I. M., Buckwalter, K. C., Cullen, L., Hanrahan, K., Kleiber, C., McCarthy, A. M., Rakel, B., Steelman, V., Tripp-Reimer, T., & Tucker, S. (2017). Iowa model of evidence-based practice: Revisions and validation. Worldviews on Evidence-Based Nursing, 14(3), 175-182.

Creswell, J.W. & Creswell, J.D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.

Department of Health, Education, and Welfare. (1979). The Belmont Report

Dhalwani, N. N., Fahami, R., Sathanapally, H., Seidu, S., Davies, M. J., & Khunti, K. (2017). Association between polypharmacy and falls in older adults: A longitudinal study from England. BMJ open, 7(10), e016358.

Durgun, H., Turan, N., & Kaya, H. (2021). Relationship between fall behavior and quality of life of elderly individuals. Psychology, Health & Medicine, 1-8.

Dykes, P. C., Duckworth, M., Cunningham, S., Dubois, S., Driscoll, M., Feliciano, Z., Ferrazzi, M., Fevrin, F. E., Lyons, S., Lindros, M. E., Monahan, A., Paley, M. M., Jean-Pierre, S., & Scanlan, M. (2017). Pilot testing fall TIPS (tailoring interventions for patient safety): a patient-centered fall prevention toolkit. The Joint Commission Journal on Quality and Patient Safety, 43(8), 403-413.

Farrelly, P. (2013). Issues of trustworthiness, validity and reliability. British Journal of School Nursing, 8(3), 149-151. Web.

Ferreira, J. M., Hammerschmidt, K. S. D. A., Siewert, J. S., Alvarez, A. M., Locks, M. O. H., & Heidemann, I. T. S. B. (2019). Gerontotechnology for fall prevention of the elderly with Parkinson. Revista Brasileira de Enfermagem, 72, 243-250.

Frieson, C. W., Tan, M. P., Ory, M. G., & Smith, M. L. (2018). Evidence-based practices to reduce falls and fall-related injuries among older adults. Frontiers in public health, 6, 222.

Frith, J. (2017). Fall prevention: empowering people through online education. The Annals of Family Medicine, 15(5), 482-482.

Gazibara, T., Kurtagic, I., Kisic-Tepavcevic, D., Nurkovic, S., Kovacevic, N., Gazibara, T., & Pekmezovic, T. (2017). Falls, risk factors and fear of falling among persons older than 65 years of age. Psychogeriatrics, 17(4), 215-223.

Gell, N. M., & Patel, K. V. (2019). Rehabilitation Services Use of Older Adults According to Fall‐Risk Screening Guidelines. Journal of the American Geriatrics Society, 67(1), 100-107.

Gomez, F., Wu, Y., Auais, M., Vafaei, A., & Zunzunegui, M. (2017). A simple algorithm to predict falls in primary care patients aged 65 to 74 years: The international mobility in aging study. Journal of the American Medical Directors Association, 18(9), 774-779.

Haines, T. P., Lee, D. C. A., O’Connell, B., McDermott, F., & Hoffmann, T. (2015). Why do hospitalized older adults take risks that may lead to falls? Health Expectations, 18(2), 233-249.

Hill, A. M., McPhail, S. M., Waldron, N., Etherton-Beer, C., Ingram, K., Flicker, L.,… Haines, T. P. (2015). Fall rates in hospital rehabilitation units after individualised patient and staff education programmes: A pragmatic, stepped-wedge, cluster-randomised controlled trial. The Lancet, 385(9987), 2592-2599.

Hoffman, G. J., Hays, R. D., Wallace, S. P., Shapiro, M., Yakusheva, O., & Ettner, S. L. (2017). Receipt of caregiving and fall risk in US community-dwelling older adults. Medical Care, 55(4), 371.

Hopewell, S., Adedire, O., Copsey, B. J., Boniface, G. J., Sherrington, C., Clemson, L.,… Lamb, S. E. (2018). Multifactorial and multiple component interventions for preventing falls in older people living in the community. Cochrane Database of Systematic Reviews, (7), 1-262.

Jie, E., & Deng, J. (2019). Fall prevention education reduces the falling rate on the osteoporosis patients treated with zoledronic acid. Case Reports in Clinical Medicine, 08(08), 222-230.

Johnston, Y. A., Bergen, G., Bauer, M., Parker, E. M., Wentworth, L., McFadden, M.,… Garnett, M. (2018). Implementation of the Stopping Elderly Accidents, Deaths, and Injuries Initiative in primary care: An outcome evaluation. The Gerontologist, XX(XX), 1-10.

Juckett, L., & Poling, R. (2020). Staff and administrator perceptions of fall risk management in home-and community-based service settings. Innovation in Aging, 4(Suppl 1), 237.

Khairat, S., Coleman, G. C., Russomagno, S., & Gotz, D. (2018). Assessing the status quo of EHR accessibility, usability, and knowledge dissemination. eGEMs, 6(1), 9.

Kiyoshi-Teo, H., Carter, N., & Rose, A. (2017). Fall prevention practice gap analysis: Aiming for targeted improvements. MedSurg Nursing, 26(5), 332-335.

Kuhirunyaratn, P., Prasomrak, P., & Jindawong, B. (2019). Effects of a health education program on fall risk prevention among the urban elderly: A quasi-experimental study. Iranian Journal of Public Health, 48(1), 103–111.

Lee, R. (2017). The CDC’s STEADI Initiative: Promoting older adult health and independence through fall prevention. American family physician, 96(4), 220.

Lim, M., Ang, S., Teo, K., Wee, Y., Yee, S., Lim, S., & Ang, S. (2018). Patientsʼ experience after a fall and their perceptions of fall prevention. Journal of Nursing Care Quality, 33(1), 46-52.

Lohman, M., Crow, R., DiMilia, P., Nicklett, E., Bruce, M., & Batsis, J. (2017). Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample. Journal of Epidemiology and Community Health, 71(12), 1191–1197.

Lyons, B., & Hall, R. (2016). Outcomes of a falls prevention education program among older adults in Grenada. Journal of Community Health, 41(5), 1021-1026.

Mamani, A., Reiners, A., Azevedo, R., Vechia, A., Segri, N., & Cardoso, J. (2019). Elderly caregiver: Knowledge, attitudes and practices about falls and its prevention. Revista Brasileira De Enfermagem, 72(suppl 2), 119-126.

Mark, J. A., & Loomis, J. (2017). The STEADI toolkit: Incorporating a fall prevention guideline into the primary care setting. The Nurse Practitioner, 42(12), 50-55.

Meyer, C., Dow, B., Hill, K., Tinney, J., & Hill, S. (2016). “The right way at the right time”: Insights on the uptake of falls prevention strategies from people with dementia and their caregivers. Frontiers in Public Health, 4, 1-10.

Meyer, C., Hill, S., Hill, K. D., & Dow, B. (2019). Inclusive decision making for falls prevention: A discussion tool for use with people with dementia and their caregivers. Journal of Aging and Physical Activity, 27(5), 711-718.

Montgomery, E. E., & Smith, Y. H. (2020). Stall the fall: training non-clinical caregivers to prevent falls in community-dwelling older adults. Journal of Community Health Nursing, 37(4), 179-188.

Mota de Sousa, L. M., Marques-Vieira, C. M., Caldevilla, M. N., Henriques, C. M., Severino, S., & Caldeira, S. M. (2017). Risk for falls among community-dwelling older people: Systematic literature review. Revista gaucha de enfermagem, 37(4), e55030.

Müller, C., Lautenschläger, S., Dörge, C., & Voigt-Radloff, S. (2019). A feasibility study of a home-based lifestyle-integrated physical exercise training and home modification for community-living older people (Part 2): The FIT-at-Home fall prevention program. Disability and rehabilitation, 1-11.

Nakagami-Yamaguchi, E., Fujinaga, K., Batard, A., Baba, N., Nakamura, K., Miyazaki, K.,… Nakatani, T. (2016). The effect of an animation movie for inpatient fall prevention: A pilot study in an acute hospital. Safety in Health, 2(1), 1-10.

Newcastle upon Tyne Hospitals. (n.d.). Post fall assessment checklist. Web.

Orem, D. (1985). A concept of self-care for the rehabilitation client. Rehabilitation Nursing, 10(3), 33-36.

Ott, L. (2018). The impact of implementing a fall prevention educational session for community-dwelling physical therapy patients. Nursing Open, 5(4), 567-574.

Patterson, B. W., Engstrom, C. J., Sah, V., Smith, M. A., Mendonça, E. A., Pulia, M. S., Repplinger, M. D., Hamedani, A. G., Page, D., & Shah, M. N. (2019). Training and interpreting machine learning algorithms to evaluate fall risk after emergency department visits. Medical care, 57(7), 560-566.

Penning, M. L., Blach, C., Walden, A., Wang, P., Donovan, K. M., Garza, M. Y., Wang, Z., Frund, J., Syed, S., Syed, M., Del Fiol, G., Newby, K. L., Pieper, C., & Zozus, M. (2020). Near real time EHR data utilization in a clinical study. Studies in health technology and informatics, 270, 337-341.

Perrot, A., Ayad, A., Gernigon, M., & Maillot, P. (2019). The impact of therapeutic patient education and physical activity programs on the fall risk of elderly people. Movement & Sport Sciences – Science & Motricité, 2019, 1-8.

Phelan, E. A., Pence, M., Williams, B., & MacCornack, F. A. (2017). Telephone care management of fall risk: A feasibility study. American journal of preventive medicine, 52(3), S290-S294.

Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Lippincott, Williams & Wilkins.

Pope, C., & Mays, N. (Eds.). (2020). Qualitative research in health care (4th ed.). Wiley Blackwell.

Pua, Y. H., Ong, P. H., Clark, R. A., Matcher, D. B., & Lim, E. C. W. (2017). Falls efficacy, postural balance, and risk for falls in older adults with falls-related emergency department visits: prospective cohort study. BMC geriatrics, 17(1), 1-7.

Radecki, B., Reynolds, S., & Kara, A. (2018). Inpatient fall prevention from the patient’s perspective: A qualitative study. Applied Nursing Research, 43, 114-119.

Rockers, P. C., Tugwell, P., Røttingen, J. A., & Bärnighausen, T. (2017). Quasi-experimental study designs series — Paper 13: realizing the full potential of quasi-experiments for health research. Journal of clinical epidemiology, 89, 106-110.

Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research. Nephrology Nursing Journal, 45(2), 209-213.

Sarmiento, K., & Lee, R. (2017). STEADI: CDC’s approach to make older adult fall prevention part of every primary care practice. Journal of safety research, 63, 105-109.

Schoberer, D., Breimaier, H., Mandl, M., Halfens, R., & Lohrmann, C. (2016). Involving the consumers: An exploration of users’ and caregivers’ needs and expectations on a fall prevention brochure: A qualitative study. Geriatric Nursing, 37(3), 207-214.

Schoene, D., Heller, C., Aung, Y. N., Sieber, C. C., Kemmler, W., & Freiberger, E. (2019). A systematic review on the influence of fear of falling on quality of life in older people: Is there a role for falls? Clinical interventions in aging, 14, 701.

Shahrbanian, S., Hashemi, A., & Hemayattalab, R. (2021). The comparison of the effects of physical activity and neurofeedback training on postural stability and risk of fall in elderly women: A single-blind randomized controlled trial. Physiotherapy theory and practice, 37(2), 271-278.

Shim, S. M., & Kim, E. (2019). Effect of fall prevention education for older patients in comprehensive nursing care service ward. Journal of Korean Public Health Nursing, 33(2), 200-213.

Singh, H., Flett, H. M., Silver, M. P., Craven, B. C., Jaglal, S. B., & Musselman, K. E. (2020). Current state of fall prevention and management policies and procedures in Canadian spinal cord injury rehabilitation. BMC health services research, 20, 1-10.

Snooks, H. A., Anthony, R., Chatters, R., Dale, J., Fothergill, R., Gaze, S.,… Russell, I. T. (2017). Support and Assessment for Fall Emergency Referrals (SAFER) 2: a cluster randomised trial and systematic review of clinical effectiveness and cost-effectiveness of new protocols for emergency ambulance paramedics to assess older people following a fall with referral to community-based care when appropriate. Health technology assessment, 21(13), 1-218.

Speroni, K. G., McLaughlin, M. K., & Friesen, M. A. (2020). Use of evidence‐based practice models and research findings in magnet‐designated hospitals across the United States: National Survey Results. Worldviews on Evidence‐Based Nursing, 17(2), 98-107.

Taylor, D., McCaffrey, R., Reinoso, H., Mathis, M. W., Dickerson, L., Hamrick, J.,… & Klein, C. M. (2019). An interprofessional education approach to fall prevention: preparing members of the interprofessional healthcare team to implement STEADI into practice. Gerontology & Geriatrics Education, 40(1), 105-120.

Titler, M. G., Kleiber, C., Steelman, V. J., Rakel, B. A., Budreau, G., Everett, L. Q., Buckwalter, K. C., Tripp-Reimer, T., & Goode, C. J. (2001). The Iowa model of evidence-based practice to promote quality care. Critical Care Nursing Clinics of North America, 13(4), 497-509.

Turner, K., Staggs, V., Potter, C., Cramer, E., Shorr, R., & Mion, L. C. (2020). Fall prevention implementation strategies in use at 60 United States hospitals: A descriptive study. BMJ quality & safety, 29(12), 1000-1007.

Ueda, T., Higuchi, Y., Imaoka, M., Todo, E., Kitagawa, T., & Ando, S. (2017). Tailored education program using home floor plans for falls prevention in discharged older patients: A pilot randomized controlled trial. Archives of Gerontology and Geriatrics, 71, 9-13.

Urban, K., Wright, P. B., Hester, A. L., Curran, G., Rojo, M., & Tsai, P. F. (2020). Evaluation of an education strategy versus usual care to implement the STEADI algorithm in primary care clinics in an academic medical center. Clinical interventions in aging, 15, 1059.

Vincenzo, J. L., & Patton, S. K. (2019). Older adults’ experience with fall prevention recommendations derived from the STEADI. Health promotion practice, 1524839919861967.

Vincenzo, J. L., Shubert, T., Brach, J. S., Tripken, J., Schrodt, L., Sidelinker, J. C., Hazan, P., Hergott, C., Shirley, K., & Rhorer, B. (2020). 4025 Fall risk screening and referrals to community-based programs among physical therapy professionals. Journal of Clinical and Translational Science, 4(s1), 132-132.

Vonnes, C., & Wolf, D. (2017). Fall risk and prevention agreement: Engaging patients and families with a partnership for patient safety. BMJ Open Quality, 6(2), e000038.

Washington Health Care Association. (n.d.). Falls management – post fall assessment tool. Web.

Wilkinson, A., Meikle, N., Law, P., Yong, H. J., Butler, P., Kim, J.,… & Hale, L. (2018). How older adults and their informal carers prevent falls: An integrative review of the literature. International Journal of Nursing Studies, 82, 13-19.

Wongrakpanich, S., Danji, K., Lipsitz, L., & Berry, S. (2019). STOP-FALLING: A simple checklist tool for fall prevention in a nursing facility. Journal of the American Medical Directors Association, 20(7), 916-918.

Ximenes, M. A. M., Fontenele, N. Â. O., Bastos, I. B., Macêdo, T. S., Neto, N. M. G., Caetano, J. Á., & Barros, L. M. (2019). Construction and validation of educational booklet content for fall prevention in hospitals. Acta Paul Enferm., 32(4), 433-441.

Xu, T., O’Loughlin, K., Clemson, L., Lannin, N., Dean, C., & Koh, G. (2017). Developing a falls prevention program for community-dwelling stroke survivors in Singapore: Client and caregiver perspectives. Disability and Rehabilitation, 1-11.

Yoo, J., Kim, C., Yim, J., & Jeon, M. (2016). Factors influencing falls in the frail elderly individuals in urban and rural areas. Aging Clinical and Experimental Research, 28(4), 687-697.

Younas, A. (2017). A foundational analysis of Dorothea Orem’s Self-Care Theory and evaluation of its significance for nursing practice and research. Creative Nursing, 23(1), 13-23.

Zhao, Y. L., Bott, M., He, J., Kim, H., Park, S. H., & Dunton, N. (2019). Evidence on fall and injurious fall prevention interventions in acute care hospitals. JONA: The Journal of Nursing Administration, 49(2), 86-92.

Zhao, P., Ross, K., Li, P., & Dennis, B. (2021). Making Sense of Social Research Methodology: A Student and Practitioner Centered Approach. SAGE Publications, Incorporated.

Appendix A

The 10 Strategic Points
Broad Topic Area Broad Topic Area:

Effects of Stopping Elderly Accidents, Deaths and Injuries Initiative on Patients’ Referrals

Literature Review Literature Review:
  • Background of the Problem/Gap:
    • About 25% of older adults experience at least one fall per year, leading to injuries and reduced quality of life (CDC, 2019).
    • The risks of falls in elderly patients can be reduced through implementation of Stopping Elderly Accidents, Deaths and Injuries (STEADI) algorithm (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020).
    • There is a significant need to study the means of fall prevention and risk screening in a chosen clinical setting (Patterson et al., 2019; Phelan et al., 2017).
  • Theoretical Foundations (models and theories to be the foundation for the project):
    • The Orem’s self-case theory can be used to implement STEADI algorithm in an urban clinic (Younas, 2017).
    • The project will investigate the elimination of self-care deficit through the intervention based on fall risk assessment and referrals for the risk reduction.

Review of Literature Topics with Key Themes and Subthemes:

  • Outpatient Clinic Clients’ Risks of Falls:
    • The risks of falls are higher in clients of outpatient clinics than in in-hospital patients due to health-associated risks and lower control of the environment outside of clinic (Dhalwani et al., 2017; Gomez et al., 2017; Haines et al., 2015; Kiyoshi-Teo et al., 2017; Kuhirunyaratn et al., 2019; Mota de Sousa et al., 2017; Yoo et al., 2015)
  • Risks of Falls in Older Patients:
    • The patients aged 65 and older are at increased risks of falls due to the natural processes of aging, deteriorating their health status (Dhalwani et al., 2017; Gomez et al., 2017; Haines et al., 2015; Kiyoshi-Teo et al., 2017; Kuhirunyaratn et al., 2019; Mota de Sousa et al., 2017; Yoo et al., 2015).
    • The consequences of falls can be more dangerous for older patients than younger patients (Durgun et al., 2021; Shahrbanian et al., 2021).
    • The implementation of a STEADI algorithm can be effective for reducing the risks of falls in elderly patients (Eckstrom et al., 2017; Lohman et al., 2017).
  • Settings:


  • Gap/Problem: There is a need to implement a fall prevention program to reduce the risks of falls and their consequences in a small outpatient clinic in Florida.
  • Prior studies: Prior studies show that the use of STEADI algorithm can be effective for reducing the risks of falls in elderly patients.
  • Quantitative application: Sources of data exist to collect numerical data on the rates of falls in patients aged 65 or older in a small outpatient clinic in Florida.
  • Significance: Decreasing the risks and incidences of falls in elderly patients of an outpatient clinic.
Problem Statement Problem Statement:

It was not known if or to what degree the implementation of the Centers for Disease Control and Prevention’s STEADI algorithm would impact the identification and subsequent referrals for the identified fall risk when compared to current practice among adults aged 65 or older in a primary care clinic in Florida.

Clinical or
PICOT Questions
PICOT Questions or Clinical Question:
  • (P) Among adult patients 65 or older in a small outpatient clinic in Florida,(I) how does the implementation of the Centers for Disease Control and Prevention’s STEADI algorithm (C) compared to current practice (O)would impact the identification and subsequent referrals for the identified fall risk (T)over a period of four weeks?

Clinical Question:

  • To what degree does the implementation of the Centers for Disease Control and Prevention’s STEADI algorithm compared to current practice impact the identification and subsequent referrals for the identified fall risk among adults 65 and older in a primary care clinic in urban Florida?
  • Sample (and Location):
    • Location: urban Florida
    • Population patients aged 65 or older
    • Sample: 200 patients
  • Inclusion Criteria
    • All adult (over 18 years of age)
      Alert and oriented to person, place, time, and situation
      English speaking
  • Exclusion Criteria
    • Under the age of 18
    • Are not alert and oriented to person, place, time, and situation
    • Are not English speaking
Define Variables Define Variables:
  • Independent Variable (Intervention): STEADI algorithm.
  • Dependent Variable: Fall incidences after the intervention.
Methodology and Design Methodology and Design:

This project will use a quantitative methodology with a quasi-experimental design.

Purpose Statement Purpose Statement:

The purpose of this quantitative, quasi-experimental quality improvement project is to determine if or to what degree the implementation of the Centers for Disease Control and Prevention’s STEADI algorithm will impact the identification of fall risk patients and subsequent referral when compared to current practice among adults 65 and older in a primary care clinic in Florida over 4 weeks.

Data Collection Approach Data Collection Approach:

After GCU IRB approval, this project will start by educating the clinical staff on how to use the STEADI algorithm to identify high fall risk patients and how to use the algorithm to refer patients to the appropriate discipline using the algorithm. Data will be collected from the EHR the 4 weeks prior to the project start date on identified high fall risk patients and referral rates. During the project implementation periods, fall risk will be identified using the STEADI algorithm and referral rates will be tracked. Whenever appropriate, high and moderate-risk patients will be referred to physical therapists for addressing the risk factors. After 4 weeks of project implementation, fall risk and referral rates will be extracted from the EHR. Demographics will also be collected from the EHR and include age, gender, race, ethnicity, history of falls, and primary diagnosis. The data will be stored on a password protected computer at the project site. Data will be destroyed 12 months after project completion.

Data Analysis Approach Data Analysis Approach:

Descriptive statistics will describe the sample characteristics and variable results.
Chi-square test of independence can be applied for evaluating distributions in data (Shi, 2018). This test is robust and informative, and it offers detailed information to be extracted from this statistical method


Centers for Disease Control and Prevention. (2019). Algorithm for fall risk screening, assessment, and intervention

Dhalwani, N. N., Fahami, R., Sathanapally, H., Seidu, S., Davies, M. J., & Khunti, K. (2017). Association between polypharmacy and falls in older adults: a longitudinal study from England. BMJ open, 7(10), e016358.

Durgun, H., Turan, N., & Kaya, H. (2021). Relationship between fall behavior and quality of life of elderly individuals. Psychology, Health & Medicine, 1-8.

Eckstrom, E., Parker, E. M., Lambert, G. H., Winkler, G., Dowler, D., & Casey, C. M. (2017). Implementing STEADI in academic primary care to address older adult fall risk. Innovation in aging, 1(2), igx028.

Gomez, F., Wu, Y., Auais, M., Vafaei, A., & Zunzunegui, M. (2017). A simple algorithm to predict falls in primary care patients aged 65 to 74 years: The international mobility in aging study. Journal of the American Medical Directors Association, 18(9), 774-779. doi: 10.1016/j.jamda.2017.03.021

Haines, T. P., Lee, D. C. A., O’Connell, B., McDermott, F., & Hoffmann, T. (2015). Why do hospitalized older adults take risks that may lead to falls? Health Expectations, 18(2), 233-249. doi:10.1111/hex.12026

Johnston, Y., & Reome-Nedlik, C. (2020). STEADI in Primary Care: A Process Evaluation of the New York State Implementation. Innovation in Aging, 4(Suppl 1), 774.

Kiyoshi‐Teo, H., Northrup‐Snyder, K., Robert Davis, M., Garcia, E., Leatherwood, A., & Izumi, S. (2020). Qualitative descriptions of patient perceptions about fall risks, prevention strategies and self‐identity: Analysis of fall prevention Motivational Interviewing conversations. Journal of Clinical Nursing, 29(21-22), 4281-4288.

Kuhirunyaratn, P., Prasomrak, P., & Jindawong, B. (2019). Effects of a health education program on fall risk prevention among the urban elderly: A quasi-experimental study. Iranian Journal of Public Health, 48(1), 103–111. doi: 10.18502/ijph.v48i1.788

Lohman, M., Crow, R., DiMilia, P., Nicklett, E., Bruce, M., & Batsis, J. (2017). Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample. Journal of Epidemiology and Community Health, 71(12), 1191–1197. doi: 10.1136/jech-2017-209769

Mota de Sousa, L. M., Marques-Vieira, C. M., Caldevilla, M. N., Henriques, C. M., Severino, S., & Caldeira, S. M. (2017). Risk for falls among community-dwelling older people: systematic literature review. Revista gaucha de enfermagem, 37(4), e55030.

Patterson, B. W., Engstrom, C. J., Sah, V., Smith, M. A., Mendonça, E. A., Pulia, M. S.,… & Shah, M. N. (2019). Training and interpreting machine learning algorithms to evaluate fall risk after emergency department visits. Medical care, 57(7), 560-566.

Phelan, E. A., Pence, M., Williams, B., & MacCornack, F. A. (2017). Telephone Care Management of Fall Risk: A Feasibility Study. American Journal of Preventive Medicine, 52(3), S290-S294.

Shahrbanian, S., Hashemi, A., & Hemayattalab, R. (2021). The comparison of the effects of physical activity and neurofeedback training on postural stability and risk of fall in elderly women: A single-blind randomized controlled trial. Physiotherapy theory and practice, 37(2), 271-278.

Yoo, J., Kim, C., Yim, J., & Jeon, M. (2016). Factors influencing falls in the frail elderly individuals in urban and rural areas. Aging Clinical and Experimental Research, 28(4), 687-697. doi: 10.1007/s40520-015-0469-2

Younas, A. (2017). A foundational analysis of Dorothea Orem’s Self-Care Theory and evaluation of its significance for nursing practice and research. Creative Nursing, 23(1), 13-23. doi: 10.1891/1078-4535.23.1.13

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