US and Dutch nurse experiences with fall prevention technology within nursing home environment and workflow: A qualitative study

Dissonance Between Human Nurses And Technology: Understanding Nurses’ Experience Using Technology Beds With Monitoring Functions Within Clinical Nursing Practice

Aims:

Are nurses adapting to the mechanized nursing practice environment? Is it possible for nurses to collaborate with technology to provide care to patients? The aim of the study is to investigate what nurses feel about using technology in nursing practice.

Background:

Preventing patients from falling is one of the nursing tasks that can be helped by using technology, such as sensors. However, little is known about how nurses experience and feel the use of technological beds for monitoring functionality within clinical nursing practice. Especially it is indicated that alarm fatigue makes nurses and patients fatigued and induces a dissonance between nurses and technology.

Objective:

To clarify the experiences of nurses in clinical practice following the introduction of a bed with monitoring and fall prevention technology (technology bed).

Methods:

We interviewed 12 nurses working at a hospital about their nursing practice experiences with the technology bed.

Results:

The content of the interview was classified into three categories: ‘providing a safe environment’, ‘limitation of entry into machine care scenes’, and ‘nurses’ dilemmas’; with eight themes describing nursing practice: (1) strategies of fall prevention, (2) decrease in nurses’ burden, (3) not good at using technology (all tools must be easy to use), (4) inefficiency such as over-engineering, (5) patients feel annoyed by frequent visits from nurses, (6) limitations of utilization from a nursing perspective, (7) nurse resistance to equipment introduction and (8) ethical issues.

Conclusion:

Although technology beds could effectively prevent falls, many nurses face an ethical dilemma in using these beds. It would be important for nurses to recognize the role of technology, embrace it, and raise awareness of collaborating with technology to eliminate a dissonance between technology and nurses.

Evaluation on service quality in institutional pensions based on a novel hierarchical DEMATEL method for PLTSs

In recent years, to address the continued aging of China’s population, the Chinese government has focused on the issue of pensions through a series of pension policies. The traditional system of institutional pensions is facing serious challenges, with a variety of novel pension modes placing them under enormous pressure. Furthermore, the development of institutional pensions has been restricted by many factors, such as long construction cycles and high fees, meaning that this traditional system no longer meets the pension needs of the elderly. Improving the service quality of institutional pensions is inevitable for future progress. Thus, identifying the key factors that influence the service quality of institutional pensions, and understanding the relationships between these factors, is hugely significant. Furthermore, traditional decision-making trial and evaluation laboratory (DEMATEL) method can not solve this problem because the number of factors is too large. To address these issues, we establish an evaluation system for Chinese pension institutions, and propose a hierarchical DEMATEL model based on probabilistic linguistic term sets (PLTSs), which can help decision makers to find the key factors influencing service quality in institutional pensions and deal with the evaluation problem with a large number of criteria. The proposed hierarchical DEMATEL model based on PLTSs fully reflects experts’ preferences and evaluation information, and is able to identify the directions in which China’s pension institutions should improve their quality of service. In addition, we use the best-worst method (BWM) to calculate the importance values of each subsystem, which makes the cause-effect relationship between subsystems more reasonable than the traditional DEMATEL method. Finally, we apply our method to evaluate nursing homes in Zhenjiang, Jiangsu province and propose some managerial implications.

Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes

OBJECTIVE

The study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes.

MATERIALS AND METHODS

We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed.

RESULTS

Of 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed.

CONCLUSIONS

Current nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses' decision making, care delivery, and patient outcomes.

Application of Machine Learning Methods in Nursing Home Research

Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods.

A Protocol-Driven, Bedside Digital Conversational Agent to Support Nurse Teams and Mitigate Risks of Hospitalization in Older Adults: Case Control Pre-Post Study

BACKGROUND

Hospitalized older adults often experience isolation and disorientation while receiving care, placing them at risk for many inpatient complications, including loneliness, depression, delirium, and falls. Embodied conversational agents (ECAs) are technological entities that can interact with people through spoken conversation. Some ECAs are also relational agents, which build and maintain socioemotional relationships with people across multiple interactions. This study utilized a novel form of relational ECA, provided by Care Coach (care.coach, inc): an animated animal avatar on a tablet device, monitored and controlled by live health advocates. The ECA implemented algorithm-based clinical protocols for hospitalized older adults, such as reorienting patients to mitigate delirium risk, eliciting toileting needs to prevent falls, and engaging patients in social interaction to facilitate social engagement. Previous pilot studies of the Care Coach avatar have demonstrated the ECA's usability and efficacy in home-dwelling older adults. Further study among hospitalized older adults in a larger experimental trial is needed to demonstrate its effectiveness.

OBJECTIVE

The aim of the study was to examine the effect of a human-in-the-loop, protocol-driven relational ECA on loneliness, depression, delirium, and falls among diverse hospitalized older adults.

METHODS

This was a clinical trial of 95 adults over the age of 65 years, hospitalized at an inner-city community hospital. Intervention participants received an avatar for the duration of their hospital stay; participants on a control unit received a daily 15-min visit from a nursing student. Measures of loneliness (3-item University of California, Los Angeles Loneliness Scale), depression (15-item Geriatric Depression Scale), and delirium (confusion assessment method) were administered upon study enrollment and before discharge.

RESULTS

Participants who received the avatar during hospitalization had lower frequency of delirium at discharge (P<.001), reported fewer symptoms of loneliness (P=.01), and experienced fewer falls than control participants. There were no significant differences in self-reported depressive symptoms.

CONCLUSIONS

The study findings validate the use of human-in-the-loop, relational ECAs among diverse hospitalized older adults.

Health technologies for falls prevention and detection in adult hospital in-patients: a scoping review protocol

REVIEW OBJECTIVE/QUESTIONS

The objective of this scoping review is to map the evidence relating to the reporting and evaluation of health technologies for the prevention and detection of falls in adult hospital in-patients. The following questions will guide this scoping review.

Organizational factors of fall injuries among residents within German nursing homes: secondary analyses of cross-sectional data

The present study explored risk factors for fall injuries among nursing home residents, with a specific focus on the influence of organizational structure within facilities and their environment, which have been insufficiently investigated in the European context. For the analyses, secondary data collected in 2016 from 220 nursing homes across Germany were used. As a risk adjustment, two separate models were calculated for fall injuries among residents without (N = 7320) and with cognitive impairment (N = 8633). Results showed that residents without cognitive impairment had a decreased risk of fall injuries by 40.1% (P < 0.01), while those with cognitive impairment were at an increased risk of 23.8% (P < 0.05) when living in facilities that had dementia care units. However, disparities were found between federal states for both groups of residents (P < 0.05 vs. P < 0.01, respectively). Similarly, a higher proportion of registered nurses were associated with decreased risk of fall injuries among cognitively impaired residents (45.6%), which differed between federal states (P < 0.01). Facilities with homelike environments had a 16.7% (P < 0.05) lower risk of fall injuries among cognitively impaired residents than did traditionally organized facilities. Further research is needed to explain the disparities between German federal states using representative samples.

Alarming and/or Alerting Device Effectiveness in Reducing Falls in Long-Term Care (LTC) Facilities? A Systematic Review

Perceptions against the use of alarming devices persist in long-term care environments as they are seen as annoying, costly, and a waste of time to the staff involved. Ascertaining whether these perceptions are true or false via the literature was a focus of this study. Proper information to educate staff and to work past these perceptions can be a positive effector for resident safety. Many facilitators for the use of alarming devices were found, as well as many barriers to their use as well. New technology is changing the perceptions regarding these types of devices as time passes. Education is a key component for staff, residents, and families. There are “traditional” issues with the use of alarms such as alarm fatigue by caregivers, high costs of implementation, and issues with proper implementation of alarms. Alarms are perceived as intrusive and the noise from them can be a potential cause of falls. However, alarming devices can be a key intervention in the safety of those residents who are prone to falls. This requires proper implementation and education for all parties involved, and proper oversight surrounding use of the devices.