Identifying factors that nurses consider in the decision-making process related to patient care during the COVID-19 pandemic.

Background: Nurse identification of patient deterioration is critical, particularly during the COVID-19 pandemic, as patients can deteriorate quickly. While the literature has shown that nurses rely on intuition to make decisions, there is limited information on what sources of data experienced nurses utilize to inform their intuition. The objectives of this study were to identify sources of data that inform nurse decision-making related to recognition of deteriorating patients, and explore how COVID-19 has impacted nurse decision-making.

mHealth spectroscopy of blood hemoglobin with spectral super-resolution.

Although blood hemoglobin (Hgb) testing is a routine procedure in a variety of clinical situations, noninvasive, continuous, and real-time blood Hgb measurements are still challenging. Optical spectroscopy can offer noninvasive blood Hgb quantification, but requires bulky optical components that intrinsically limit the development of mobile health (mHealth) technologies. Here, we report spectral super-resolution (SSR) spectroscopy that virtually transforms the built-in camera (RGB sensor) of a smartphone into a hyperspectral imager for accurate and precise blood Hgb analyses.

SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network.

Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of smartphone video imaging and an artificial neural network (ANN) system to estimate Hgb levels non-invasively. We recorded 10 second-300 frame fingertip videos using a smartphone in 75 adults.