Engaging nurses in health policy in the era of COVID‐19

Anders, Robert L.

doi:10.1111/nuf.12514

Cited by 45 publications

(50 citation statements)

References 14 publications

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“…It is essential to be able to gain the benefit of the recent advances in AI and computerized algorithms, especially during these hard times of COVID-19 spread worldwide. Deep learning not only provides high levels of performance, it also reduces the dependency on experts, i.e., clinicians and nurses, who are now suffering in handling the pandemic due to the huge and rapidly increasing number of infected patients [ 77 – 79 ]. Recently, the detection of COVID-19 using deep learning has reached high levels of accuracy through two-dimensional (2D) lung CT images [ 80 – 82 ].…”

Section: Discussionmentioning

confidence: 99%

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

Alkhodari

Khandoker

2022

PLoS ONE

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This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein that relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). The statistical analysis of patient profiles has shown a significant difference (p-value: 0.041) for ischemic heart disease between COVID-19 and healthy subjects. The Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35, p-value: <0.001). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

Alkhodari

Khandoker

2022

PLoS ONE

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“…The development and implementation extend to local, state, and federal agencies outside care sites. The players adjust for various environments, and the cycle can vary, but the process remains the same 33 …”

Section: Yoder‐wise Framework For Policy Change Regarding Staffing Ratiosmentioning

confidence: 99%

Patient safety time for federally mandated registered nurse to patient ratios

Anders

2021

Nursing Forum

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COVID‐19 highlighted the role registered nurses (RNs) have in our healthcare system. The shortage of equipment plus an overwhelming demand for nursing care placed significant stress on nurses. The physical and emotional toll of caring for COVID patients and not having an adequate number of nurses in many cases placed many nurses in harm's way. Despite some healthcare systems reporting significant profits during COVID, few nurses benefited financially. ICU nurses found themselves assigned to three or more patients. Having a defined RN to patient ratio is an urgent need. Pass efforts to mandate via Federal legislation ratios have failed largely due to hospitals' opposition. California has had mandated RN to patient ratios since 2011. Their experience has proven that patient outcomes improve as well as nurse retention. Hospitals' decrease has largely offset the impacts of hospitals' net revenue by decreasing acquired infections, reducing the 30‐readmission rate, and improving patient satisfaction scores. As documented in recent Harris polls, the consumer appears ready to support nurses, with nurses enjoying significant public support. The political climate for such change is favorable compared to the past. The Yoder‐Wise Planned Policy Change illustrates how Federal legislation mandates RN to patient ratios is achievable.

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“…It is essential to be able to gain the benefit of the recent advances in AI and computerized algorithms, especially during these hard times of COVID-19 spread worldwide. Deep learning not only provides high levels of performance, it also reduces the dependency on experts, i.e., clinicians and nurses, who are now suffering in handling the pandemic due to the huge and rapidly increasing number of infected patients [50][51][52]. Recently, the detection of COVID-19 using deep learning has reached high levels of accuracy through two-dimensional (2D) lung CT images [53][54][55].…”

Section: Diagnosis Of Covid-19 Using Deep Learningmentioning

confidence: 99%

Detection of COVID-19 in smartphone-based breathing recordings using CNN-BiLSTM: a pre-screening deep learning tool

Alkhodari

Khandoker

2021

Preprint

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This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein the relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

show abstract

Engaging nurses in health policy in the era of COVID‐19

Cited by 45 publications

References 14 publications

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

Patient safety time for federally mandated registered nurse to patient ratios

Detection of COVID-19 in smartphone-based breathing recordings using CNN-BiLSTM: a pre-screening deep learning tool

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