Abstract: Techniques for improved dynamic interfaces are provided. User interaction, from a user, is received via a graphical user interface (GUI) of a computing device. In response to receiving the user interaction, a set of user data associated with the user is collected, and a first stress score is generated by processing the set of user data using a stress model. In response to determining that the first stress score satisfies one or more defined criteria, a first prompt is generated for the user, where the first prompt requests additional user interaction, as compared to a default prompt, and the first prompt is output via the GUI.

Abstract: Techniques for improved machine learning are provided. User data describing one or more conditions of a user is received, and a healthcare service plan is generated for the user, comprising: extracting a plurality of user attributes, from the user data, for the user, selecting a first alternative healthcare service plan having a set of one or more home healthcare services to be provided to the user, and generating a predicted outcome score by inputting the first alternative healthcare service plan and the plurality of user attributes into a trained machine learning model. The healthcare service plan is implemented for the user based at least in part on the predicted outcome score.

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: 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: 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 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: Systems, methods and techniques for training and applying machine learning models to predict whether or not one or more individuals will suffer a fall event. In certain embodiments, a machine learning model can include both a static component and a dynamic component, where each component is associated with different types of medical data. In certain embodiments, an adjustment factor based on fall history of individuals is applied to the output of the machine learning model to generate a final score predictive of a fall event. In certain embodiments, the machine learning model is both trained and applied to medical data associated with predetermined forms, and where the predetermined forms include a value range associated with a medical condition.

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 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 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: 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: 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 first value for each of a plurality of parameters is received, each of the first values being associated with a user and a first day. A second value for each of the plurality of parameters is received, each of the second values being associated with the user and a second day that is subsequent to the first day. For each of the plurality of parameters, a trend indication is determined, the trend indication being based on the first values, the second values, and a first time period. A base weight value for each of the plurality of parameters is determined, the base weight value being based on the first time period and the determined trend indication associated with the one of the plurality of parameters. A mood score is determined, based on the base weight value for each of the plurality of parameters.

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.