| CPC G16H 40/20 (2018.01) [G06F 16/9024 (2019.01); G06F 17/18 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06Q 10/067 (2013.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01)] | 25 Claims |
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1. A system, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a grouping component that defines a group of beds at a medical facility based on at least one grouping factor;
a training component that learns patterns in historical patient flow data related to historical flow of historical patients in and out of respective beds in the group using one or more machine learning processes and generates one or more census forecasting models trained to predict an expected occupancy level for the group at one or more future time periods based on current patient flow data for the group and the patterns, wherein the historical patient flow data comprises patient journey data tracked for the historical patients regarding respective journeys of the historical patients at the medical facility, and wherein the one or more machine learning processes comprise modeling the respective journeys using heterogeneous graphs and learning the patterns from the heterogenous graphs;
a patient census component that applies the one or more census forecasting model to the current patient flow data to forecast the expected occupancy level for the group during at the one or more future periods of time; and
an alert component that generates and provides an alert to a device associated with an administrator of the medical facility in response to the expected occupancy level satisfying an alert criterion.
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