Abstract: Certain aspects of the present disclosure provide techniques for wound management and treatment. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first ML model. The techniques further include identifying patient medical data including characteristics relating to a medical history for the patient, and predicting a first care plan for the patient based on providing the characteristics of the wound and the patient medical data to a second ML model. The first care plan is configured to be used to treat the wound for the patient.

Abstract: Certain aspects of the present disclosure provide techniques for wound management and treatment. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first ML model trained to detect wound characteristics from a captured image. The techniques further include identifying patient medical data including characteristics relating to a medical history for the patient, and predicting a first care plan for the patient based on providing the characteristics of the wound and the patient medical data to a second ML model. The second ML model is trained to predict the first care plan using prior wound care outcome data including a prior wound care outcomes relating to prior patients. The first care plan is configured to be used to treat the wound for the patient.

Abstract: Embodiments herein use a blockchain to store encrypted patient data. The blockchain may be distributed amongst multiple different healthcare providers and use a shared data format standard. By placing the patient data on a blockchain which is accessible by multiple healthcare providers, the data is more readily transferrable. The patient can maintain a EHR wallet that stores cryptography elements for decrypting that patient's data on the blockchain. For example, when the patient wants her data to be readable by a healthcare provider that did not put the data on the blockchain, the patient can transfer the corresponding cryptography element (e.g., a private key) from her wallet to the new healthcare provider who can then use the cryptography element to decrypt the patient's data that was put on the blockchain by a different healthcare provider.

Abstract: A sentiment analysis system is described that uses a machine learning model to predict a mood of an individual based on the motions used by the individual when performing sign language. The present disclosure uses image processing to monitor how a non-verbal individual communicates to, for example, predict overall health and mental well-being.

Abstract: The present disclosure provides for the division of environmental sounds from speech sounds to extract and analyze the behaviors and activities occurring in the environment; thus expanding and improving the functionality of AI assistant devices. These environmental sounds can be securely uploaded to an electronic chart, and may be used to aid in the treatment of existing conditions or the prophylaxis/mitigation of conditions not yet experienced by a patient under observation. Accordingly, the present disclosure provides for improved functionality in assistant devices and devices linked to the AI assistant devices.

Abstract: Techniques for improved data services are provided. A check-in scan indicating a user device in a medical facility is received, and a medical professional associated with the user device is identified. A patient of the medical facility is identified based at least in part on the check-in scan. Access permission of the medical professional is validated, with respect to the patient, based at least in part on the check-in scan. In response to validating the access permission of the medical professional, credentials of the medical professional are not requested, a patient profile of the patient is automatically retrieved via one or more networks, and input describing the patient, from the medical professional, is automatically stored in the patient profile via the one or more networks.

Abstract: Certain aspects of the present disclosure provide techniques for predicting electronic health record data using ML. This includes determining a plurality of attributes of a textual description of a patient medical examination, including: detecting the plurality of attributes based on analyzing the textual description using a first ML model trained to parse patient textual descriptions. This further includes predicting a change to an electronic health record for the patient, including: providing to a second ML model the plurality of attributes of the textual description and patient medical data for the patient, where the second ML model is trained to predict changes to patient electronic health records based on attributes of textual data relating to the patient and patient medical data. The predicted change is provided to an electronic system to change the electronic health record and affect medical treatment for the patient.

Abstract: Techniques for predicting caregiver retention using machine learning (ML) are discussed. These techniques include predicting an impact of one or more caregiver tasks on continued employment of the caregiver with a care provider, including determining a plurality of intermediate prediction scores relating to characteristics for the caregiver using one or more first ML models trained to determine intermediate prediction scores. The techniques further include determining a retention prediction for the caregiver using the plurality of intermediate prediction scores, including: generating the retention prediction by providing the plurality of intermediate prediction scores to a second ML model trained to determine the retention prediction based on intermediate prediction scores.

Abstract: Certain aspects of the present disclosure provide techniques for security management of health information using artificial intelligence assistant by receiving, at an artificial intelligence (AI) assistant device from a requestor in an environment, an utterance including a request to provide health information related to a patient and confirming, via a machine learning model hosted by the AI assistant device, whether an unauthorized person is present in the environment with the AI assistant device, where the unauthorized person is not permitted by the patient to receive the health information but is permitted to interact with the AI assistant device. Further, in response to determining that the unauthorized person is present, generating, by the AI assistant device, an audio deferral that does not include the health information that was requested.

Abstract: Embodiments herein determine when to place a passive assistive call using personal artificial intelligence (AI) assistants. The present embodiments improve upon the base functionalities of the assistant devices by monitoring the usually discarded or filtered-out environmental sounds to identify when a person is in distress to automatically issue an assistive call in addition to or alternatively to monitoring user speech for active commands to place assistive calls. The assistant device may be in communication with various other sensors to enhance or supplement the audio assessment of the persons in the environment, and may be used in a variety of scenarios where prior call systems struggled to quickly and accurately identify distress in various monitored persons (e.g., patients) including falls, stroke onset, and choking.

Abstract: Certain aspects of the present disclosure provide a wound management system and method for predicting and avoiding wounds in a healthcare facility. The method includes collecting data relating to a patient’s health and applying a machine learning model to the data relating to the patient’s health to predict a first probability that the patient will sustain a first wound while the patient is at a first healthcare facility. The method also includes determining an action that reduces the first probability that the patient will sustain the first wound and communicating, to the first healthcare facility, a message indicating that the action should be taken to reduce the first probability that the patient will sustain the first wound while the patient is at the first healthcare facility.

Abstract: Certain aspects of the present disclosure provide techniques for wound management and treatment. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first ML model. The techniques further include identifying patient medical data including characteristics relating to a medical history for the patient, and predicting a first care plan for the patient based on providing the characteristics of the wound and the patient medical data to a second ML model. The first care plan is configured to be used to treat the wound for the patient.

Abstract: Certain aspects of the present disclosure provide techniques for wound management and treatment. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first ML model trained to detect wound characteristics from a captured image. The techniques further include identifying patient medical data including characteristics relating to a medical history for the patient, and predicting a first care plan for the patient based on providing the characteristics of the wound and the patient medical data to a second ML model. The second ML model is trained to predict the first care plan using prior wound care outcome data including a prior wound care outcomes relating to prior patients. The first care plan is configured to be used to treat the wound for the patient.

Abstract: Certain aspects of the present disclosure provide a wound management system and method for predicting and treating wounds. The method includes collecting data relating to a patient's health and applying a machine learning model to the data relating to the patient's health to predict a first probability that the patient will sustain a first wound type outside of a care setting. The method also includes, in response to determining that the first probability exceeds a threshold, determining an action that reduces the first probability and communicating, to the patient, a message indicating the action should be taken to reduce the first probability that the patient will sustain the first wound type.

Abstract: Certain aspects of the present disclosure provide techniques for predicting wound management treatment resources. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first machine learning (ML) model trained to detect wound characteristics from a captured image. The techniques further include predicting at least one of: (i) treatment resources or (ii) a treatment facility for treating the wound, including providing to a second trained ML model characteristics of the wound, patient medical data for the patient, and treatment facility data describing a plurality of available treatment facilities.

Abstract: Embodiments herein describe an automated notification system that can detect a change to a person's medical record, notify an authorized person (e.g., a family member), and direct the authorized person to a health portal to view the change. The health portal then authenticates the person to ensure they are authorized to access the health record. Once authenticated, the health portal displays a graphical user interface (GUI) that includes both the older information in the medical record as well as the change in the record (e.g., a new diagnosis or new medication). The GUI can use a visual indicator to emphasize the change to the medical record to easily distinguish it from the older information (e.g., previous diagnosis and medications).

Abstract: Apparatuses and associated techniques for fall detection and prevention via wearable devices are provided. In one aspect, a wearable device is provided including one or more pressure sensors arranged on a bottom portion of the wearable device to collect pressure data from a bottom of a foot of a user, a wireless transmitter communicatively coupled with the one or more pressure sensors, and a power source coupled with the wireless transmitter.

Abstract: Techniques for improved sensor monitoring using machine learning are provided. An elopement likelihood indicating a probability of elopement for a patient in a care setting is generated by processing first patient data using a trained machine learning model. One or more sensor devices are enabled in response to determining that the elopement likelihood exceeds a threshold. Second patient data is collected for the patient using the enabled one or more sensor devices. An intervention for the patient is selected based on the second patient data, and the intervention is enacted.

Abstract: A system includes a sensor, a memory, and a control system. The sensor is configured to generate data associated with a living space of a resident. The memory is configured to store machine-readable instructions and a resident profile. The control system includes one or more processors configured to execute the machine-readable instructions to analyze the data to identify one or more hazards in the living space. The control system is further configured to execute the machine-readable instructions to determine a risk score for the living space based at least in part on the identified one or more hazards.

Abstract: Certain aspects of the present disclosure provide techniques for wound management and treatment. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first ML model trained to detect wound characteristics from a captured image. The techniques further include identifying patient medical data including characteristics relating to a medical history for the patient, and predicting a first care plan for the patient based on providing the characteristics of the wound and the patient medical data to a second ML model. The second ML model is trained to predict the first care plan using prior wound care outcome data including a prior wound care outcomes relating to prior patients. The first care plan is configured to be used to treat the wound for the patient.