Development of an Alarm Algorithm, With Nanotechnology Multimodal Sensor, to Predict Impending Infusion Failure and Improve Safety of Peripheral Intravenous Catheters in Neonates

Bosque, Elena

doi:10.1097/anc.0000000000000690

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…Figure 2 also presents that the most adopted role of AI in care management in the AINA studies was profiling and prediction (36 out of 47 studies), followed by assessment and evaluation (four studies), adaptive systems and personalization (four studies), and intelligent tutoring systems (three studies). For example, Bosque 23 developed an intravenous infusion nanotechnology monitoring system to warn nurses of impending peripheral intravenous infusion failure. In the AINA studies, the most frequent role of AI in the nursing assessment component was profiling and prediction (14 out of 37 studies), followed by assessment and evaluation (13 studies), intelligent tutoring systems (six studies), and adaptive systems and personalization (four studies).…”

Section: Research Resultsmentioning

confidence: 99%

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

Hwang

Chang

Tseng

et al. 2022

CIN: Computers, Informatics, Nursing

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The present study referred to the technology-based learning model to conduct a systematic review of the dimensions of nursing activities, research samples, research methods, roles of artificial intelligence, applied artificial intelligence algorithms, evaluation measure of algorithms, and research foci. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure, this study obtained and analyzed a total of 102 high-quality artificial intelligence–associated nursing activities studies published from 2001 to 2020 in the Web of Science database. The results showed: (1) In terms of nursing activities, nursing management was explored the most, followed by nursing assessment; (2) quantitative methods were most frequently adopted in artificial intelligence–associated nursing activities studies to investigate issues related to patients, followed by nursing staff; (3) the most adopted roles of artificial intelligence in artificial intelligence–associated nursing activities studies were profiling and prediction, followed by assessment and evaluation; (4) artificial intelligence–associated nursing activities studies frequently mixed applied artificial intelligence algorithms and evaluation measure of algorithms; (5) in the dimension of research foci, most studies mainly paid attention to the design or evaluation of the artificial intelligence systems/instruments, followed by investigating the correlation and affect issues. Based on the findings, several recommendations are raised as a reference for future researchers, educators, and policy makers.

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Section: Research Resultsmentioning

confidence: 99%

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

Hwang

Chang

Tseng

et al. 2022

CIN: Computers, Informatics, Nursing

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“…The remaining articles were organized according the TNPS conceptual framework which grounded the articles in the TNPS clinical use scenarios to provide a basis for parity of the synthesis. The main limitations noted in these articles included generalizability, 19 20 21 22 23 24 25 26 27 28 small samples sizes, 23 26 29 30 31 32 potential deviations from methodological convention, 22 33 and feasibility. 24 34…”

Section: Resultsmentioning

confidence: 99%

“…All of the articles that underwent full-text review were able to be sorted into various clinical use cases of the TPNS conceptual model. Ten articles described patient assessment, monitoring, or surveillance use, 19 21 23 24 26 30 31 32 33 35 with vital signs monitoring and CDSS related to early warning detection comprising most of this subset. 19 23 24 33 35 Three articles described patient protection from harm, all of which focused on CDSS for medication safety.…”

Section: Summary Of Study Characteristicsmentioning

confidence: 99%

“…24 28 31 32 One article described “smart” intravenous catheter technology coupled with a fuzzy logic algorithm to detect impending intravenous catheter failure in neonates and its affiliated warning system. 21 Another article described a wireless device that monitored the patient's fluid status and movement through sensors, although its primary objective was to facilitate communication between clinicians and patients. The device and the associated dashboard were designed to engage patients directly in several ways.…”

Section: Summary Of Study Characteristicsmentioning

confidence: 99%

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A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting

et al. 2022

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Background Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS). Objective The objective of this scoping review is to (1) examine the existing evidence related to integrated medical devices, primarily medication pump devices, and associated clinical decision support (CDS) in acute care settings and (2) to identify how acute care clinicians may use device CDS in clinical decision-making. The rationale for this review is that integrated devices are ubiquitous in the acute care setting, and they generate data that may help to contribute to the situational awareness of the clinical team necessary to provide individualized patient care. Methods This scoping review was conducted using the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extensions for Scoping Review guidelines. PubMed, CINAHL, IEEE Xplore, and Scopus databases were searched for scholarly, peer-reviewed journals indexed between January 1, 2010 and December 31, 2020. A priori inclusion criteria were established. Results Of the 1,924 articles screened, 18 were ultimately included for synthesis, and primarily included articles on devices such as intravenous medication pumps and vital signs machines. Clinical alarm burden was mentioned in most of the articles, and despite not including the term “medication” there were many articles about smart pumps being integrated with the EHR. The Revised Technology, Nursing & Patient Safety Conceptual Model provided the organizational framework. Ten articles described patient assessment, monitoring, or surveillance use. Three articles described patient protection from harm. Four articles described direct care use scenarios, all of which described insulin administration. One article described a hybrid situation of patient communication and monitoring. Most of the articles described devices and decision support primarily used by registered nurses (RNs). Conclusion The articles in this review discussed devices and the associated CDSS that are used by clinicians, primarily RNs, in the daily provision of care for patients. Integrated device data provide insight into user–device interactions and help to illustrate health care processes, especially the activities when providing direct care to patients in an acute care setting. While there are CDSS designed to support the clinician while working with devices, RNs and providers may disregard this guidance, and defer to their own expertise. Additionally, if clinicians perceive CDSS as intrusive, they are at risk for alarm and alert fatigue if CDSS are not tailored to sync with the workflow of the end-user. Areas for future research include refining inclusion criteria to examine the evidence for devices and their CDS that are most likely used by other groups' health care professionals (i.e., doctors and therapists), using integrated device metadata and deep learning analytics to identify patterns in care delivery, and decision support tools for patients using their own personal data.

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“…The technological innovation of sensors and the application of deep learning technology have brought new ideas for the continuous improvement of the safety management of smart IPs. Bosque 39 used multimodal sensors based on nanotechnology to provide intravascular pressure, pH, and oxygen saturation data. They combine them with fuzzy logic algorithms to predict impending venous infusion failure, which is likely to prevent complications and tissue damage related to intravenous infusion.…”

Section: Design and Development Of Smart Ipsmentioning

confidence: 99%

Development of smart infusion pumps: State of the art and future perspectives

Wang

Hou

Zhu

et al. 2023

Interdisciplinary Nursing Research

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Development of an Alarm Algorithm, With Nanotechnology Multimodal Sensor, to Predict Impending Infusion Failure and Improve Safety of Peripheral Intravenous Catheters in Neonates

Cited by 5 publications

References 39 publications

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting

Development of smart infusion pumps: State of the art and future perspectives

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