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: 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: 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 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: 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 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 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.