Background: Intravenous (IV) therapy using short peripheral IV catheters (PIVC) is commonplace with neonatal patients. However, this therapy is associated with high complication rates including the leakage of infused fluids from the vasculature into the surrounding tissues; a condition referred to as, peripheral IV infiltration/extravasation (PIVIE). Objective: The quality improvement project aimed to identify the prevalence of known risk factors for PIVIE in the neonatal intensive care unit (NICU) and explore the feasibility of using novel optical sensor technology to aid in earlier detection of PIVIE events. Methods: The plan, do, study, act (PDSA) model of quality improvement (QI) was used to provide a systematic framework to identify PIVIE risks and evaluate the potential utility of continuous PIVC monitoring using the ivWatch model 400® system. The site was provided with eight monitoring systems and consumables. Hospital staff were supported with theoretical education and bedside training about the system operations and best use practices. Results: In total 113 PIVIE's (graded II-IV) were recorded from 3476 PIVCs, representing an incidence of 3.25%. Lower birth weight and gestational age were statistically significant factors for increased risk of PIVIE (p = 0.004); all other known risk factors did not reach statistical significance. Piloting the ivWatch with 21 PIVCs using high-risk vesicant solutions over a total of 523.9 h (21.83 days) detected 11 PIVIEs (graded I-II). System sensitivity reached 100%; 11 out of 11 PIVIEs were detected by the ivWatch before clinician confirmation. Conclusions: Prevailing risk factors for PIVIE in the unit were comparable to those published. Continuous infusion site monitoring using the ivWatch suggests this technology offers the potential to detect PIVIE events earlier than relying on intermittent observation alone (i.e. the current standard of care). However, large-scale study with neonatal populations is required to ensure the technology is optimally configured to meet their needs.
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Purpose People with dementia (PwD) often present Behavioral and Psychological Symptoms of Dementia, which include agitation, apathy, and wandering amongst others, also known as challenging behaviors (CBs). These CBs worsen the quality of life (QoL) of the PwD and are a major source/reason of (increased) caregiver burden. The intricate nature of the symptoms implies that there is no “one size fits all solution”, and necessitates tailored approaches for both PwDs and caregivers. To timely prevent these behaviors assistive technology can be utilized to guide caregivers by enabling remote monitoring of contextual, environmental, and behavioral parameters, and subsequently alarming nurses on early-stage behavioral changes prior to the presentation of CBs. Eventually, the system should propose an intervention/action to prevent escalation. In turn, improvement in QoL for both caregivers and PwD living in nursing homes (NHs) is expected. In the current project “MOnitoring Onbegrepen Gedrag bij Dementie met sensortechnologie” (MOOD-Sense), we aim to develop such a monitoring system. The strengths of this new monitoring system lie in its ability to align with the individual needs of the PwD, utilization of a combination of wearables and ambient sensors to obtain contextual data, such as location or sound, and predict or monitor CBs individually rather than in groups, thus facilitating person-centered care, based on ontological reasoning. The project is divided into three parts, Toolbox A, B and C. Toolbox A focuses on obtaining insight in which behaviors are challenging according to nurses and how they are described. Previous studies utilize clinical terminology to describe or classify behavior, we aim to employ concrete descriptions of behavior that are observable and independent of clinical terminology, aligning with nurses who are often the first to notice behavior and can be operationalized such that it can also be aligned with sensor data. As a result, an ontology will be developed based on the data such that sensor data can be integrated into the same conceptual information that standardizes the communication in our monitoring system. Toolbox B focuses on translating data coming from various sensors into the concepts expressed in the ontology, and timely communicate situations of interest to the caregivers. In Toolbox C the focus is exploring interventions/actions employed in practice to prevent CBs. Method In Toolbox A we used a qualitative approach to collect descriptions of CBs. For this purpose, we employed focus groups (FGs) with nursing staff who provide daily care to PwD. In Toolbox B pilot studies were conducted. A set of experiments using sensors in NHs were performed. During each pilot, multiple PwD with CBs in NHs were monitored with both ambient and wearables sensors. The pilots were iteratively approached, which means that insights from previous pilot studies were used to improve consecutive pilot studies. Lastly, the elaboration of Toolbox C is ongoing. Results and Discussion Regarding Toolbox A four FGs were conducted during the period from January 2023 to May 2024. Each FG was comprised of four nurses (n = 16). From the FGs we gained insights into behavioral descriptions and the context of CBs. Although data analysis has to be performed yet, there are indications that changes preceding CBs can be observed, such as frowning or clenching fists for agitation or aggression. Further results will be available soon. Regarding Toolbox B a monitoring system, based on sensors, is developed iteratively (see Figure 1) and piloted in three consecutive NHs from January 2021 to December 2023. Each pilot was comprised of two PwD (n = 6). Analysis of sensor data is ongoing.
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The creation of artifacts is one of the factors that make us human. Artifacts contribute to our continual adaptation to the world by permitting better knowledge and control of it. The focus of this chapter is on the role of one specific kind of artifact: sensors. In contrast to our immediate perception of the world from our senses, sensors provide large amount of reliable measurements of the physical world that enhance human cognitive capacities in overcoming our perceptual limitations. However, “raw” sensor data require interpretation that relies on different types of expertise and knowledge to provide relevant meaning for human (adaptive) purposes. We suggest that a cognitive approach to understanding the differences between the different types of knowledge provided by current sensors as artifacts and the human senses is of interest. This approach questions the conception of human cognition as an analytic system of processing information from the world rather than as one which interprets and gives meanings to the world. We posit that understanding the differences between human and artificial sensors can shape a new era of technological advancement that is uniquely collaborative insofar as it would rely on the partnership of scientists working in the Humanities and in the Natural Sciences. In this article we provide some data from cognitive research that outline the beginnings of a pluridisciplinary endeavor to conceive sensors which integrate performances of artifacts and the diversity and richness of human cognition, with the goal of transforming so-called “intelligent” devices into cognitive sensors.
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