The Stopping Elderly Accidents, Deaths, and Injuries Algorithm Effects

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). CDC (2019) reports that around 25% of older people experience a fall every year, but this problem can be prevented, for example, through the 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; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019). The STEADI approach’s application is a topical area in that more research is being introduced in the recent years (Casey et al., 2017; Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman 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; Phelan et al., 2017 Snooks et al., 2017; Vincenzo et al., 2020). 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; Shahrbanian et al., 2021), the need for the investigation of useful and effective interventions for them is especially significant. There is a lot of recent research on the topic (Gell & Patel, 2019; Patterson et al., 2019; Phelan et al., 2017 Snooks et al., 2017; Vincenzo et al., 2020), which shows that it is an essential and quickly developing, topical field.

The present quality improvement project focuses the introduction of a method of fall risk assessment (STEADI Algorithm) in primary care. It follows specific guidelines that are represented in its particular structure. Background information will be presented to understand the scope of the problem. This section will be followed by theoretical foundations, including problem statement, purpose, clinical question, advancing scientific knowledge, and the project’s significance. Furthermore, Chapter 1 will comment on methodology, project design, terms used, and limitations. After that, all critical 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 (Durgun et al., 2021; 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). It is called the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Algorithm for Fall Risk Screening, Assessment, and Intervention by the Centers for Disease Control and Prevention (2019), and it 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 (that is, the problem of patients experiencing risk of falls and, as a result, experiencing falls in their lives) is among the common concerns for healthcare professionals. There are no data to suggest that the issue is particularly acute at the site; the same can be said about falls outside of the clinic reported by patients or family members, which are the focus of the project. 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 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; 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). 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; Phelan et al., 2017 Snooks et al., 2017; Vincenzo et al., 2020). 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

It was not known if or to what degree the implementation of the CDC’s STEDI Algorithm impact the identification of and subsequent referrals for the identified 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 services 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 (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019). The results will not be conclusive, but 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). 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).

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 (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; 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 (Eckstrom et al., 2017; Lohman et al., 2017).

Purpose of the Project

The purpose of this quantitative, quasi-experimental quality improvement project is to determine if or to what degree the implementation of the CDC’s STEDI 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 study. 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 what degree does the implementation of the CDC’s STEDI Algorithm impacts the identification of and subsequent referrals 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.

Just 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 might 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 (that is, 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 (Eckstrom et al., 2017; Johnston & Reome-Nedlik, 2020; Lee, 2017; Lohman et al., 2017; Mark, 2019), 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 45 patients based on a GPower analysis. The inclusion criteria are the assessment for fall risks within the specified timeframe before and after intervention (four weeks). The patients will not be contacted (only their Electronic Health Record data will be used), which is why no additional concerns are necessary.

The project will use one group (pre- and post-test design); all the providers 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 one assumption that deserves 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), actually apply it 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 also reasonable to comment on the project’s limitations.

  1. On the one hand, a small sample size of 45 patients is objectively a weakness, but it should be sufficient for the specified methodology based on GPower analysis.
  2. Furthermore, 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).
  3. 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 work focuses on older patients because they are more subject, compared to younger individuals, to falls.

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 45 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.


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NursingBird. (2023, July 1). The Stopping Elderly Accidents, Deaths, and Injuries Algorithm Effects.

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"The Stopping Elderly Accidents, Deaths, and Injuries Algorithm Effects." NursingBird, 1 July 2023,


NursingBird. (2023) 'The Stopping Elderly Accidents, Deaths, and Injuries Algorithm Effects'. 1 July.


NursingBird. 2023. "The Stopping Elderly Accidents, Deaths, and Injuries Algorithm Effects." July 1, 2023.

1. NursingBird. "The Stopping Elderly Accidents, Deaths, and Injuries Algorithm Effects." July 1, 2023.


NursingBird. "The Stopping Elderly Accidents, Deaths, and Injuries Algorithm Effects." July 1, 2023.