A Low-cost Reader for Automatically Collecting Vital Signs in Hospitals

Chen, Kuo-Yi; Chen, Fuh-Gwo; Hou, Ting‐Wei

doi:10.1007/s10916-011-9732-z

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…However, vital sign measurement can now be automated and entered directly into an electronic patient medical record. [23][24][25][26][27] Moreover, these vital signs can be monitored remotely 27 and early warnings can be generated upon the development of abnormalities. 25 Bellomo et al 26 reported that automated vital signs monitors improved the proportion of rapid response team calls triggered by respiratory criteria, increased survival of patients and decreased the time required for vital signs measurement.…”

Section: Discussionmentioning

confidence: 99%

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The association between abnormal vital sign groups and undesirable patient outcomes

Lee

Taylor

Ugoni

2018

Hong Kong j. emerg. med.

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Introduction: To determine the association between both abnormal individual vital signs and abnormal vital sign groups in the emergency department, and undesirable patient outcomes: hospital admission, medical emergency team calls and death. Method: We undertook a prospective cohort study in a tertiary referral emergency department (February-May 2015). Vital signs were collected prospectively in the emergency department and undesirable outcomes from the medical records. The primary outcomes were undesirable outcomes for individual vital signs (multivariate logistic regression) and vital sign groups (univariate analyses). Results: Data from 1438 patients were analysed. Admission was associated with tachycardia, tachypnoea, fever, ≥1 abnormal vital sign on admission to the emergency department, ≥1 abnormal vital sign at any time in the emergency department, a persistently abnormal vital sign, and vital signs consistent with both sepsis (tachycardia/hypotension/abnormal temperature) and pneumonia (tachypnoea/fever) (p < 0.05). Medical emergency team calls were associated with tachycardia, tachypnoea, ≥1 abnormal vital sign on admission (odds ratio: 2.3, 95% confidence interval: 1.4-3.8), ≥2 abnormal vital signs at any time (odds ratio: 2.4, 95% confidence interval: 1.2-4.7), and a persistently abnormal vital sign (odds ratio: 2.7, 95% confidence interval: 1.6-4.6). Death was associated with Glasgow Coma Score ≤13 (odds ratio: 6.3, 95% confidence interval: 2.5-16.0), ≥1 abnormal vital sign on admission (odds ratio: 2.6, 95% confidence interval: 1.2-5.6), ≥2 abnormal vital signs at any time (odds ratio: 6.4, 95% confidence interval: 1.4-29.5), a persistently abnormal vital sign (odds ratio: 4.3, 95% confidence interval: 2.0-9.0), and vital signs consistent with pneumonia (odds ratio: 5.3, 95% confidence interval: 1.9-14.8). Conclusion: Abnormal vital sign groups are generally superior to individual vital signs in predicting undesirable outcomes. They could inform best practice management, emergency department disposition, and communication with the patient and family.

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Section: Discussionmentioning

confidence: 99%

“…Manual vital sign measurement and recording is labour and time intensive. However, vital sign measurement can now be automated and entered directly into an electronic patient medical record 23–27 . Moreover, these vital signs can be monitored remotely 27 and early warnings can be generated upon the development of abnormalities 25 .…”

Section: Discussionmentioning

confidence: 99%

The association between abnormal vital sign groups and undesirable patient outcomes

Lee

Taylor

Ugoni

2018

Hong Kong j. emerg. med.

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“…(b) Design sensible systems and scenarios, then collect data. (c) Construct data schema and perform patient activity [ 54 , 55 ].…”

Section: “Slow Well-being Gardening” Frameworkmentioning

confidence: 99%

Slow Well-Being Gardening: Creating a Sensor Network for Radiation Therapy Patients via Horticultural Therapeutic Activity

Chang,

Tsai,

Huang

et al. 2024

Sensors

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Well-being can reflect people’s psychological conditions and be used alongside physiological parameters to evaluate patients’ physical and mental health. The modern medical environment increasingly incorporates digital carriers, human–computer interaction devices, sensible spaces, and the execution of suitable algorithms. Slow design in healthy human–computer interaction is often used to reflect people’s dependence on or support from behaviors or objects, promoting the stability of behaviors as well as meaningful and positive changes. Therefore, in this study, we propose a slow sensing model, develop a Slow Well-Being Gardening system, and use it to evaluate behavioral data from radiation therapy patients during treatment sessions and horticultural therapy. This study is based on SENS and slow design, setting the hospital lounge as a sensible space and establishing a sensor system. After a 10-day inspection, the process was evaluated and verified. Ultimately, data from facial detection (smile) and HRV showed that the patients in the experimental group experienced a significant improvement in their well-being, feeling better than those in the control group who maintained the most common state in normal treatment. Therefore, it can be inferred that the Slow Well-Being Gardening model is indeed valid and can be further developed.

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“…Medical sensors, such as electrocardiograms, pulse oximeters, sphygmomanometers and arrhythmia detectors, are widely used to monitor vital signs of patients [21]. Pulse oximeter is a non-invasive device which does not emit heat or radiation.…”

Section: Pulse Oximeter and Its Operation Principlementioning

confidence: 99%

Design and Construction of a Microcontroller-Based Ventilator Synchronized with Pulse Oximeter

2016

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This study aims to introduce a novel device with which mechanical ventilator and pulse oximeter work in synchronization. Serial communication technique was used to enable communication between the pulse oximeter and the ventilator. The SpO2 value and the pulse rate read on the pulse oximeter were transmitted to the mechanical ventilator through transmitter (Tx) and receiver (Rx) lines. The fuzzy-logic-based software developed for the mechanical ventilator interprets these values and calculates the percentage of oxygen (FiO2) and Positive End-Expiratory Pressure (PEEP) to be delivered to the patient. The fuzzy-logic-based software was developed to check the changing medical states of patients and to produce new results (FiO2 ve PEEP) according to each new state. FiO2 and PEEP values delivered from the ventilator to the patient can be calculated in this way without requiring any arterial blood gas analysis. Our experiments and the feedbacks from physicians show that this device makes it possible to obtain more successful results when compared to the current practices.

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A Low-cost Reader for Automatically Collecting Vital Signs in Hospitals

Cited by 5 publications

References 18 publications

The association between abnormal vital sign groups and undesirable patient outcomes

The association between abnormal vital sign groups and undesirable patient outcomes

Slow Well-Being Gardening: Creating a Sensor Network for Radiation Therapy Patients via Horticultural Therapeutic Activity

Design and Construction of a Microcontroller-Based Ventilator Synchronized with Pulse Oximeter

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