| CPC G16H 50/30 (2018.01) [G16H 40/20 (2018.01)] | 20 Claims |
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1. A computer-implemented method, comprising:
storing information about a plurality of patients in a plurality of network-based non-transitory storage devices, wherein the information includes:
data from a plurality of external sources; and
patient engagement data based on healthcare provider interactions with at least a subset of the plurality of patients, stored as analyzed text;
receiving, from an ensemble machine learning model, intervention information for a first patient and intervention information for a second patient, the intervention information for the first patient and the intervention information for the second patients each comprising a respective first prediction of risk for one or more interventions or a second prediction of a change in risk for the one or more interventions, wherein the first prediction and the second prediction are different for the first patient and the second patient and wherein the ensemble machine learning model:
is trained based on the information about the plurality of patients; and
comprises one or more machine learning models that determine the intervention information for the first and second patients using a weighted composite output that combines outputs of the one or more machine learning models;
generating a prioritized intervention list according to the intervention information for the first patient and the intervention information for the second patient;
providing remote access to the prioritized intervention list over a network to one or more client devices;
receiving, from a first client device using a graphical user interface, updated patient engagement data over the network, wherein the updated patient engagement data includes updated information about the first patient and updated information about the second patient, wherein the first client device provides the updated patient engagement data as freeform text;
converting the freeform text of the updated patient engagement data into analyzed text;
adding the analyzed text of the updated patient engagement data to the patient engagement data;
receiving, from the ensemble machine learning model, updated intervention information for the first patient and updated intervention information for the second patient, the updated intervention information for the first patient and the updated intervention information for the second patient each comprising an updated prediction of risk for a first intervention or an updated prediction of a change in risk for the first intervention for the respective first and second patients, wherein the ensemble machine learning model is re-trained based on the updated information about the first patient and the updated information about the second patient;
generating an updated prioritized intervention list according to the updated intervention information for the first patient and the updated intervention information for the second patient;
automatically generating a message containing the updated prioritized intervention list whenever the patient engagement data is updated; and
transmitting the message to the one or more client devices over the network.
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