Implementation and experience of an innovative smart patient care system: a cross-sectional study

The digital transformation of nursing practice: an analysis of advanced IoT technologies and smart nursing systems

Facing unprecedented challenges due to global population aging and the prevalence of chronic diseases, the healthcare sector is increasingly relying on innovative solutions. Internet of Things (IoT) technology, by integrating sensing, network communication, data processing, and security technologies, offers promising approaches to address issues such as nursing personnel shortages and rising healthcare costs. This paper reviews the current state of IoT applications in healthcare, including key technologies, frameworks for smart nursing platforms, and case studies. Findings indicate that IoT significantly enhances the efficiency and quality of care, particularly in real-time health monitoring, disease management, and remote patient supervision. However, challenges related to data quality, user acceptance, and economic viability also arise. Future trends in IoT development will likely focus on increased intelligence, precision, and personalization, while international cooperation and policy support are critical for the global adoption of IoT in healthcare. This review provides valuable insights for policymakers, researchers, and practitioners in healthcare and suggests directions for future research and technological advancements.

Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention

BACKGROUND

The challenge of preventing in-patient falls remains one of the most critical concerns in health care.

OBJECTIVE

This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention.

METHODS

A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system.

RESULTS

In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047).

CONCLUSIONS

The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety.

The psychometric properties of the Persian version of the innovation support inventory (ISI-12) in clinical nurses: a methodological cross-sectional study

BACKGROUND

Innovation in nursing involves applying new knowledge to create novel ideas, methods, or technologies, resulting in higher-quality care and improved patient outcomes. Adequate support for innovation is crucial for progress in nursing. This study aimed to translate the Innovation Support Inventory (ISI-12) into Persian and assess its psychometric properties specifically among clinical nurses.

METHODS

A methodological cross-sectional study was conducted from September 2022 to July 2023 to evaluate the face, content, and construct validity of the ISI-12. Construct validity was assessed through confirmatory factor analysis (CFA) and convergent and discriminant validity evaluation using data obtained from 321 clinical nurses. The test-retest stability and internal consistency of the ISI-12 were also evaluated to assess its reliability.

RESULTS

The Persian version of the ISI-12 validation through confirmatory factor analysis has confirmed its fit with the proposed three-factor model. The ISI-12 demonstrated high reliability, as evidenced by a Cronbach's alpha coefficient (α = 0.969), McDonald's omega coefficient (ω = 0.922), coefficient H (H = 0.979), and mean inter-item correlation (ρ = 0.418). Additionally, the stability of the ISI-12 during two weeks among 40 clinical nurses was found to be excellent, with an ICC of 0.951.

CONCLUSION

The Persian version of the ISI-12 is a valid inventory for evaluating the innovation Support of clinical nurses.

Creating an Implementation Enhancement Plan for a Digital Patient Fall Prevention Platform Using the CFIR-ERIC Approach: A Qualitative Study

(1) Background: Inpatient falls are a major cause of hospital-acquired complications (HAC) and inpatient harm. Interventions to prevent falls exist, but it is unclear which are most effective and what implementation strategies best support their use. This study uses existing implementation theory to develop an implementation enhancement plan to improve the uptake of a digital fall prevention workflow. (2) Methods: A qualitative approach using focus groups/interview included 12 participants across four inpatient wards, from a newly built, 300-bed rural referral hospital. Interviews were coded to the Consolidated Framework for Implementation Research (CFIR) and then converted to barrier and enabler statements using consensus agreement. Barriers and enablers were mapped to the Expert Recommendations for Implementing Change (ERIC) tool to develop an implementation enhancement plan. (3) Results: The most prevalent CFIR enablers included: relative advantage (n = 12), access to knowledge and information (n = 11), leadership engagement (n = 9), patient needs and resources (n = 8), cosmopolitanism (n = 5), knowledge and beliefs about the intervention (n = 5), self-efficacy (n = 5) and formally appointed internal implementation leaders (n = 5). Commonly mentioned CFIR barriers included: access to knowledge and information (n = 11), available resources (n = 8), compatibility (n = 8), patient needs and resources (n = 8), design quality and packaging (n = 10), adaptability (n = 7) and executing (n = 7). After mapping the CFIR enablers and barriers to the ERIC tool, six clusters of interventions were revealed: train and educate stakeholders, utilize financial strategies, adapt and tailor to context, engage consumers, use evaluative and iterative strategies and develop stakeholder interrelations. (4) Conclusions: The enablers and barriers identified are similar to those described in the literature. Given there is close agreement between the ERIC consensus framework recommendations and the evidence, this approach will likely assist in enhancing the implementation of Rauland's Concentric Care fall prevention platform and other similar workflow technologies that have the potential to disrupt team and organisational routines. The results of this study will provide a blueprint to enhance implementation that will be tested for effectiveness at a later stage.

Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care

Bed is often the personal care unit in hospitals, nursing homes, and individuals' homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users' bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents' on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients' sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers' main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users' personalized sleep-related data.

Creating the future of nursing in the post-pandemic world

The COVID-19 pandemic highlighted that nursing cannot go back to its old way of providing care. Care is central to what nurses do and the profession itself, and now is the time for nursing to innovate and reimagine what nursing will look like in the future. From new models of care to technology, nursing has an endless opportunity to innovate the profession. The new model of nursing care must be sustainable and work to maximize nurses while leveraging technology as a tool to help improve quality outcomes. The opportunities are endless, and the time is now to innovate and reimagine nursing and its caring core.