Abstract: A system comprising a first expired drug usability device is provided. The first expired drug usability device comprises a first spectrometer and one or more first processors. The first processors are configured to: obtain at least one expired drug machine learning-artificial intelligence (ML-AI) model associated with a pharmaceutical drug; obtain drug expiration information of a sample of the pharmaceutical drug, wherein the drug expiration information comprises spectrometer data associated with using the first spectrometer on the sample; input the drug expiration information into the at least one expired drug ML-AI model to determine usability information associated with the sample of the pharmaceutical drug; and perform one or more actions based on the usability information.

Abstract: Methods and systems for analyzing care data are described. The method includes building a virtual model of a physical medical device. Training is provided to a patient associated with the physical medical device to properly use the physical medical device by manipulating the virtual model. First care data associated with a first sensor associated with the physical medical device and second care data associated with a second sensor is received by a cloud service. The cloud service analyzes the first care data to obtain a first care data score and analyzes the second care data to obtain a second care data score. The cloud service scores, using a machine learning algorithm, the first care data score and the second care data score to obtain a combined care score. The cloud service determines whether the combined care score is greater than a threshold. The cloud service triggers an emergency procedure when it is determined that the combined care score is greater than the threshold.

Abstract: Methods and systems for analyzing care data are described. The method includes building a virtual model of a physical medical device. Training is provided to a patient associated with the physical medical device to properly use the physical medical device by manipulating the virtual model. First care data associated with a first sensor associated with the physical medical device and second care data associated with a second sensor is received by a cloud service. The cloud service analyzes the first care data to obtain a first care data score and analyzes the second care data to obtain a second care data score. The cloud service scores, using a machine learning algorithm, the first care data score and the second care data score to obtain a combined care score. The cloud service determines whether the combined care score is greater than a threshold. The cloud service triggers an emergency procedure when it is determined that the combined care score is greater than the threshold.

Abstract: Methods and systems for analyzing care data using an edge computing device are provided. The edge computing device is connected to a cloud service. The edge computing device receives a machine learning algorithm from the cloud service. The edge computing device receives first care data from a first sensor and second care data from a second sensor. The edge computing device analyzes the first care data to obtain a first care data score and analyzes the second care data to obtain a second care data score. Next, the edge computing devices scores, using the machine learning algorithm, the first care data score and the second care data score to obtain a combined care score. The edge computing device determines whether the combined care score is greater than a threshold. The edge computing device triggers an emergency procedure when it is determined that the combined care score is greater than the threshold.