Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing

Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing

Introduction to Fall Detection and Prevention Systems

Falls are a major health concern for elderly individuals, with potentially severe consequences including injury, loss of independence, and decreased quality of life. As the global population ages, developing effective fall detection and prevention technologies has become increasingly important. I selected fall detection and prevention systems as the focus for this annotated bibliography because of the significant impact these technologies can have on enhancing the safety and quality of care for elderly patients.

To research this topic, I utilized the Capella University Library databases, including PubMed and IEEE Xplore. Key search terms included “fall detection,” “fall prevention,” “elderly,” “wearable sensors,” and “machine learning.” I targeted peer-reviewed articles published within the last 5 years to ensure that the information contained was updated. Of those results retrieved, I narrowed down only to four works that gave a good overview of recent advances in fall detection systems and also evaluations of real-world effectiveness and challenges of implementation.

Annotation Elements

Gharghan, S. K., & Hashim, H. A. (2024). A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques. Measurement, 226, 114186–114186. https://doi.org/10.1016/j.measurement.2024.114186

This review article thoroughly examines current fall detection systems (FDSs) designed specifically for elderly individuals. The authors review several FDSs from different aspects, such as detection method, system architecture, wireless communication, type of sensor, performance metrics, and related limitations.
The article identifies that the advancement of wireless and Internet of Things technologies has laid the ground for the development of more efficient systems for fall detection and rescue. The authors of the paper declare that this kind of system will increase patient safety by allowing quick intervention after a fall to reduce the possibility of severe injury or death. Of those systems reviewed, the deep learning-based systems proved to be very accurate in the detection of falls.
The authors underline that FDSs play a significant role in enhancing quality care for elderly patients, whether they are staying in independent houses or living in a care facility. For example, they allow early responses to incidents of falls, thereby avoiding complications that result from prolonged bed rest following a fall. The article also describes how FDSs can be used in concert with more general healthcare monitoring systems to enable coordination among interdisciplinary care providers.

This publication was selected because it offers a comprehensive and up-to-date overview of fall detection technologies, with a focus on wireless and AI-based approaches. The authors’ analysis of system performance and limitations provides valuable insights for healthcare practitioners considering implementation of these technologies.

Kulurkar, P., Dixit, C. kumar, Bharathi, V. C., Monikavishnuvarthini, A., Dhakne, A., & Preethi, P. (2023). AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT. Measurement: Sensors, 25, 100614. https://doi.org/10.1016/j.measen.2022.100614

This research article presents a novel IoT-based fall detection system that utilizes low-power wireless sensing networks, big data analytics, cloud computing, and smart devices. The system incorporates a three-dimensional accelerometer integrated into a wearable device to gather real-time movement data from elderly individuals.

The authors report that their edge computing system achieved a 95.87% accuracy rate in detecting falls using real-time data stream analytics. This high level of accuracy suggests significant potential for improving patient safety by enabling rapid response to fall incidents. The system’s ability to differentiate between falls and normal activities of daily living also helps reduce false alarms, enhancing the quality and efficiency of care.

The article discusses how the proposed system can be integrated into smart home environments, facilitating remote monitoring by healthcare professionals and family members. This aspect of the technology supports interdisciplinary care by allowing various stakeholders to access and respond to fall detection alerts.

I selected this publication because it demonstrates a practical application of AI and IoT technologies in fall detection, with promising results for accuracy and real-time monitoring. The authors’ discussion of system optimization, including sensor placement and data sampling rates, provides valuable insights for healthcare practitioners considering implementation of similar technologies.

Pech, M., Sauzeon, H., Yebda, T., Benois-Pineau, J., & Amieva, H. (2021). Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality? JMIR Aging, 4(4), e29744. https://doi.org/10.2196/29744

This article critically examines the current state of fall detection and prevention systems for home care of older adults. The authors analyze the technological challenges and user-specific barriers that have limited the widespread deployment of these systems.

Regarding patient safety and quality of care, the article highlights the potential of fall detection systems to enable rapid response to fall incidents, potentially reducing the severity of injuries and complications. However, the authors also note that the effectiveness of these systems in real-world settings remains largely unproven due to limited large-scale studies in population-based settings.

This article’s key contribution is its analysis of acceptability factors specific to the older adult population. The authors discuss how factors such as the generational digital divide, perceived usefulness, ease of use, and cost can significantly impact the adoption and effective use of fall detection technologies. This perspective is crucial for understanding how these technologies may affect the work of interdisciplinary healthcare teams, particularly in terms of patient education and technology implementation.

I selected this publication because it offers a balanced and nuanced view of the challenges and opportunities associated with fall detection systems. The authors’ emphasis on the need for more research in real-life settings and consideration of user-specific factors provides an important context for healthcare practitioners evaluating these technologies.

Ramachandran, A., & Karuppiah, A. (2020). A Survey on Recent Advances in Wearable Fall Detection Systems. BioMed Research International, 2020, 1–17. https://doi.org/10.1155/2020/2167160

This survey article covers recent developments in wearable fall detection systems, emphasizing especially machine learning techniques. Various fall detection systems are reviewed with respect to the type of sensors used, how data processing is performed, and the machine learning techniques used.

The article looks forward to a prospect when wearable fall detection systems can enable continuous monitoring in a minimally intrusive way and can thus offer more favorable prospects for the safety of the patients and their quality of care in the case of elderly individuals living independently. According to the authors, techniques based on machine learning have shown the ability to detect falls with good, though variable, accuracy depending on sensor placement and fall patterns.

It supports this view by discussing the possibility of integrating fall detection systems with other health monitoring technologies. This would provide an interdisciplinary healthcare team with an opportunity for better collaboration since they would have a more complete picture of a patient’s health status and risk factors.

I chose this publication because it reviewed in a wide-reaching manner the technical considerations related to wearable fall detection systems, pointing out both their strengths and limitations. Future research directions shared by the authors, such as using biological factors in concert with prior health history in fall detection algorithms, provide a great deal of food for thought on a topic that has been in continuous evolution through new research and development. More importantly, these are particularly useful suggestions for health practitioners who may be interested in the ongoing development of these technologies.

Summary of Recommendation

The potential of fall detection and prevention systems to improve both the safety and quality of care related to the elderly patient population is well testified through the four publications reviewed. Major learnings will involve the fact that there is a high degree of accuracy achievable with AI-based systems, that it is of paramount importance to consider user acceptability factors, and there is a potential involvement of fall detection with broader health monitoring technologies. Factors that organizationally affect the choice of technology for the detection of falls in healthcare facilities include available resources, that is, hardware, software costs, and maintenance costs; infrastructure, i.e., IT systems and infrastructure already in place: Human resource training and support, that is the willingness to impart necessary education to use the technology effectively. Further, organizational culture, that is, willingness to adopt new technologies and alter processes of care. Lastly, patient population characteristics, i.e., overall level of need and preference of the elderly population served.

Justification for Fall Detection Technology Adoption in Health Care Institutions

Improved Patient Safety

Early detection and timely response in cases of falls can prevent severe injury and complications.

The Quality of Care

Continuous monitoring will ensure timely intervention and personalized care plans.

Independent Living

These technologies could extend the self-sufficient lives of aged adults with ensured safety.

Resources well Utilized

Automated fall detection provides many staff with effective deployment and response times.

Data-Driven Insights

The rich data generated by these systems can inform fall prevention strategies and overall care improvements.

Therefore, although there are still challenges in the real-world effectiveness and user acceptance of fall detection and prevention systems, the evidence tends to indicate a significant potential for their benefits in improving the outcomes of elderly patients. Such an implementation of pilot studies in health care organizations is essentially needed to evaluate the patient’s care and staff workflows and overall efficiency in health care delivery. Ongoing research and development in this field promise to further enhance the capabilities and integration of these technologies into comprehensive geriatric care systems.

References

Gharghan, S. K., & Hashim, H. A. (2024). A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques. Measurement, 226, 114186–114186. https://doi.org/10.1016/j.measurement.2024.114186

Kulurkar, P., Dixit, C. kumar, Bharathi, V. C., Monikavishnuvarthini, A., Dhakne, A., & Preethi, P. (2023). AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT. Measurement: Sensors, 25, 100614. https://doi.org/10.1016/j.measen.2022.100614

Pech, M., Sauzeon, H., Yebda, T., Benois-Pineau, J., & Amieva, H. (2021). Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality? JMIR Aging, 4(4), e29744. https://doi.org/10.2196/29744

Ramachandran, A., & Karuppiah, A. (2020). A Survey on Recent Advances in Wearable Fall Detection Systems. BioMed Research International, 2020, 1–17. https://doi.org/10.1155/2020/2167160

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Write a 4–6 page annotated bibliography where you identify peer-reviewed publications that promote the use of a selected technology to enhance quality and safety standards in nursing.

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Introduction
This assessment will give you the opportunity to deepen your knowledge of how technology can enhance quality and safety standards in nursing. You will prepare an annotated bibliography on technology in nursing. A well-prepared annotated bibliography is a comprehensive commentary on the content of scholarly publications and other sources of evidence about a selected nursing-related technology. A bibliography of this type provides a vehicle for workplace discussion to address gaps in nursing practice and to improve patient care outcomes. As nurses become more accountable in their practice, they are being called upon to expand their role of caregiver and advocate to include fostering research and scholarship to advance nursing practice. An annotated bibliography stimulates innovative thinking to find solutions and approaches to effectively and efficiently address these issues.

Professional Context
Rapid changes in information technology go hand-in-hand with progress in quality health care delivery, nursing practice, and interdisciplinary team collaboration. Technology is essential to the advancement of the nursing profession, maintaining quality care outcomes, patient safety, and research.

Preparation
Before you begin to develop the assessment, you are encouraged to complete the Annotated Bibliography Formative Assessment. Completing this activity will help you succeed with the assessment and counts towards course engagement.

To successfully complete this assessment, perform the following preparatory activities:

Review the technologies presented in the Assessment 03 Supplement: Evidence-Based Proposal and Annotated Bib on Tech in Nursing [PDF] Download Assessment 03 Supplement: Evidence-Based Proposal and Annotated Bib on Tech in Nursing [PDF]resource and select one of the options to use as the focus for this assessment.
Direct patient care technologies require an interaction, or direct contact, between the nurse and patient. Nurses use direct patient care technologies every day when delivering care to patients. Electronic thermometers or pulse oximeters are examples of direct patient care technologies.
Indirect patient care technologies, on the other hand, are those employed on behalf of the patient. They do not require interaction, or direct contact, between the nurse and patient. A handheld device for patient documentation is an example of an indirect patient care technology.
Conduct a library search using the various electronic databases available through the Capella University Library.
Consult the BSN Program Library Research Guide for help in identifying scholarly and/or authoritative sources.
Access the NHS Learner Success Lab, linked in the courseroom navigation menu, for additional resources.
Scan the search results related to your chosen technology.
Select four peer-reviewed publications focused on your selected topic that are the most interesting to you.
Evaluate the impact of patient care technologies on desired outcomes.
Analyze current evidence on the impact of a selected patient care technology on patient safety, quality of care, and the interdisciplinary team.
Integrate current evidence about the impact of a selected patient care technology on patient safety, quality of care, and the interdisciplinary team into a recommendation.
Notes
Publications may be research studies or review articles from a professional source. Newspapers, magazines, and blogs are not considered professional sources.
Your selections need to be current—within the last five years.
Instructions
First, review the technologies presented in the Assessment 03 Supplement: Evidence-Based Proposal and Annotated Bib on Tech in Nursing [PDF] Download Assessment 03 Supplement: Evidence-Based Proposal and Annotated Bib on Tech in Nursing [PDF]resource and select one of the options to use as the focus for this assessment.

Next prepare a 4–6 page paper in which you introduce your selected technology and describe at least four peer-reviewed publications that promote the use of your selected technology to enhance quality and safety standards in nursing. You will conclude your paper by summarizing why you recommend a particular technology by underscoring the evidence-based resources you presented. Be sure that your paper includes all of the following elements:

Introduction to the Selected Technology Topic
What is your rationale for selecting this particular technology topic? What is interesting about it?
What research process did you employ?
Which databases did you use?
Which search terms did you use?
Note: In this section of your bibliography, you may use first-person since you are asked to describe your rationale for selecting the topic and the research strategies you employed. Use third person in the rest of the bibliography, however.
Annotation Elements
For each resource, include the full reference followed by the annotation.
Explain the focus of the research or review article you chose.
Provide a summary overview of the publication.
According to this source, what is the impact of this technology on patient safety and quality of care?
According to this source, what is the relevance of this technology to nursing practice and the work of the interdisciplinary health care team?
Why did you select this publication to write about out of the many possible options? In other words, make the case as to why this resource is important for health care practitioners to read.
Summary of Recommendation
How would you tie together the key learnings from each of the four publications you examined?
What organizational factors influence the selection of a technology in a health care setting? Consider such factors as organizational policies, resources, culture/social norms, commitment, training programs, and/or employee empowerment.
How would you justify the implementation and use of the technology in a health care setting? This is the section where you will justify (prove) that the implementation of the
patient care technology is appropriate or not. The evidence should be cited from the literature that was noted in the annotated bibliography.
Consider the impact of the technology on the health care organization, patientcare/satisfaction, and interdisciplinary team productivity, satisfaction, and retention.
Example Assessment: You may use the following to give you an idea of what a Proficient or higher rating on the scoring guide would look like:

Assessment 3 Example [PDF] Download Assessment 3 Example [PDF].
Additional Requirements
Written communication: Ensure written communication is free of errors that detract from the overall message.
Length: 4–6-typed, double-spaced pages.
Number of resources: Cite a minimum of four peer-reviewed publications, not websites.
Font and font size: Use Times New Roman, 12 point.
APA: Follow APA style and formatting guidelines for all bibliographic entries. Refer to Evidence and APA as needed.
Competencies Measured
By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and scoring guide criteria:

Competency 3: Evaluate the impact of patient care technologies on desired outcomes.
Analyze current evidence on the impact of a selected patient care technology on patient safety, quality of care, and the interdisciplinary team.
Integrate current evidence about the impact of a selected patient care technology on patient safety, quality of care, and the interdisciplinary team into a recommendation.
Competency 4: Recommend the use of a technology to enhance quality and safety standards for patients.
Describe organizational factors influencing the selection of a technology in the health care setting.
Justify the implementation and use of a selected technology in a health care setting.
Competency 5: Apply professional, scholarly communication to facilitate use of health information and patient care technologies.
Create a clear, well-organized, and professional annotated bibliography that is generally free from errors in grammar, punctuation, and spelling.
Follow APA style and formatting guidelines for all bibliographic entries.