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

Wen, Ming-Huan; Bai, Dorothy Li; Lin, Shirling; Chu, Chi‐Jen; Hsu, Yeh‐Liang

doi:10.1186/s12913-022-07511-7

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…They rely on their experience with patients to determine whether the alarm is of high priority. This can lead to many alarms being ignored by caregivers [ 25 ] and can also add to caregivers’ burden when multiple patients are cared for by the same caregiver. Incorporating bed-exit alarm systems with extended communication features into mattresses and other parts of nursing beds can create a substantial market in the healthcare industry.…”

Section: Discussionmentioning

confidence: 99%

“…Multilayer perceptron machine learning was used to create a model with an accuracy of 89.0% (10-fold cross-validation). In particular, the sensitivity of awakeness is 69.2% [ 25 ]. As shown in Figure 4 , the machine learning algorithm on the chip of the control box of the mattress classifies the in-bed time into sleep (high bar in Figure 4 ) and awake (medium bar in Figure 4 ).…”

Section: Methodsmentioning

confidence: 99%

“…In the SMCS ward, no false bed-exit alarms were observed among 110 alarms. Comparatively, 42 (35%) of 120 alarms in traditional care practice were false bed-exit alarms ( p < 0.001) and approximately 56% of nurses viewed the bed-exit alert as ignorable [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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

Bai

Mathunjwa

et al. 2023

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Bai

Mathunjwa

et al. 2023

Sensors

Self Cite

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“…Nurses lamented the fatigue they felt when their phones would constantly ring and difficulty with deactivating bed alarms. Noise fatigue is not a new problem and has been reported in the contact of a myriad of interventions (call bell, telemetry, vital sign monitoring) [33,34]. Various approaches can be used to overcome alarm fatigue including modification of alarm parameters or clinical workflow re-design with some studies showing a 68% improvement in alarm notifications after using improvement cycles to redesign workflows with multidisciplinary input [35].…”

Section: Barriersmentioning

confidence: 99%

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

Delaforce,

Li,

Grujovski

et al. 2023

IJERPH

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(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.

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Remote Monitoring of Patient Health Indicators Using Cloud Technologies

Motienko

2024

Lecture Notes in Networks and Systems

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

Cited by 7 publications

References 51 publications

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

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

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

Remote Monitoring of Patient Health Indicators Using Cloud Technologies

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