The impact of continuous wireless monitoring on adverse device effects in medical and surgical wards: a review of current evidence

Aagaard, Nikolaj; Larsen, Arendse Tange; Aasvang, Eske Kvanner; Meyhoff, Christian S.

doi:10.1007/s10877-022-00899-x

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…In our previous experiences, some patients developed skin irritation from the plaster material [ 22 ]. In line with this, a recent review stated that most adverse device events reported for wearable sensors on the ward are skin related [ 39 ]. In general, frequent evaluation of acceptance and required workload is needed, so that the willingness to use remains high.…”

Section: Resultsmentioning

confidence: 92%

Implementing Wearable Sensors for Clinical Application at a Surgical Ward: Points to Consider before Starting

Melzen

Haveman

Schuurmann

et al. 2023

Sensors

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Incorporating technology into healthcare processes is necessary to ensure the availability of high-quality care in the future. Wearable sensors are an example of such technology that could decrease workload, enable early detection of patient deterioration, and support clinical decision making by healthcare professionals. These sensors unlock continuous monitoring of vital signs, such as heart rate, respiration rate, blood oxygen saturation, temperature, and physical activity. However, broad and successful application of wearable sensors on the surgical ward is currently lacking. This may be related to the complexity, especially when it comes to replacing manual measurements by healthcare professionals. This report provides practical guidance to support peers before starting with the clinical application of wearable sensors in the surgical ward. For this purpose, the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework of technology adoption and innovations in healthcare organizations is used, combining existing literature and our own experience in this field over the past years. Specifically, the relevant topics are discussed per domain, and key lessons are subsequently summarized.

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Section: Resultsmentioning

confidence: 92%

Implementing Wearable Sensors for Clinical Application at a Surgical Ward: Points to Consider before Starting

Melzen

Haveman

Schuurmann

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Deteriorating patients in Australian hospitals – Current issues and future opportunities

Jones¹

2023

Australian Critical Care

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Discrepancies between Promised and Actual AI Capabilities in the Continuous Vital Sign Monitoring of In-Hospital Patients: A Review of the Current Evidence

Aagaard,

Aasvang,

Meyhoff

2024

Sensors

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Continuous vital sign monitoring (CVSM) with wireless sensors in general hospital wards can enhance patient care. An artificial intelligence (AI) layer is crucial to allow sensor data to be managed by clinical staff without over alerting from the sensors. With the aim of summarizing peer-reviewed evidence for AI support in CVSM sensors, we searched PubMed and Embase for studies on adult patients monitored with CVSM sensors in general wards. Peer-reviewed evidence and white papers on the official websites of CVSM solutions were also included. AI classification was based on standard definitions of simple AI, as systems with no memory or learning capabilities, and advanced AI, as systems with the ability to learn from past data to make decisions. Only studies evaluating CVSM algorithms for improving or predicting clinical outcomes (e.g., adverse events, intensive care unit admission, mortality) or optimizing alarm thresholds were included. We assessed the promised level of AI for each CVSM solution based on statements from the official product websites. In total, 467 studies were assessed; 113 were retrieved for full-text review, and 26 studies on four different CVSM solutions were included. Advanced AI levels were indicated on the websites of all four CVSM solutions. Five studies assessed algorithms with potential for applications as advanced AI algorithms in two of the CVSM solutions (50%), while 21 studies assessed algorithms with potential as simple AI in all four CVSM solutions (100%). Evidence on algorithms for advanced AI in CVSM is limited, revealing a discrepancy between promised AI levels and current algorithm capabilities.

show abstract

The impact of continuous wireless monitoring on adverse device effects in medical and surgical wards: a review of current evidence

Cited by 3 publications

References 41 publications

Implementing Wearable Sensors for Clinical Application at a Surgical Ward: Points to Consider before Starting

Implementing Wearable Sensors for Clinical Application at a Surgical Ward: Points to Consider before Starting

Deteriorating patients in Australian hospitals – Current issues and future opportunities

Discrepancies between Promised and Actual AI Capabilities in the Continuous Vital Sign Monitoring of In-Hospital Patients: A Review of the Current Evidence

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