Monitoring, predicting and alerting for census periods in medical inpatient units

A method, comprising:

• executing, by a system, one or more machine learning processes to learn patterns in historical patient flow data related to a flow of patients in and out of respective beds in a group at a medical facility, wherein the executing comprises generating one or more census forecasting models based on the patterns, wherein the historical patient flow data comprises patient journey data tracked for a plurality of patients regarding their journeys at the medical facility, the patient journey data comprising information regarding workflow events, condition of the patients, operations of the medical facility and temporal relationships between the workflow events, the conditions of the patients and the operations, or a combination thereof, wherein the patient journey data is modeled as a heterogeneous graph comprising nodes representing patient attributes and workflow events and arcs representing relationships between the patient attributes and the workflow events, wherein the one or more machine learning processes learn the patterns from the heterogeneous graph;
• selecting, by the system, the one or more census forecasting models for the group based on a measure of occupancy variability;
• applying, by the system, the one or more census forecasting models to current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time;
• tuning one or more parameters of the one or more census forecasting models for the group prior to application to the current patient flow data to forecast the expected occupancy level for the group;
• and
• providing an alert to a device associated with the medical facility in response to the expected occupancy level meeting a threshold value.

A non-transitory machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

• executing one or more machine learning processes to learn patterns in historical patient flow data related to a flow of patients in and out of respective beds in a group at a medical facility, wherein the executing comprises generating one or more census forecasting models based on the patterns, wherein the historical patient flow data comprises patient journey data tracked for a plurality of patients regarding their journeys at the medical facility, the patient journey data comprising information regarding workflow events, condition of the patients, operations of the medical facility and temporal relationships between the workflow events, the conditions of the patients and the operations, or a combination thereof, wherein the patient journey data is modeled as a heterogeneous graph comprising nodes representing patient attributes and workflow events and arcs representing relationships between the patient attributes and the workflow events, wherein the one or more machine learning processes learn the patterns from the heterogeneous graph;
• selecting the one or more census forecasting models for the group based on a measure of occupancy variability;
• applying one or more census forecasting models to current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time;
• tuning one or more parameters of the one or more census forecasting models for the group prior to application to the current patient flow data to forecast the expected occupancy level for the group;
• and
• providing an alert to a device associated with the medical facility in response to the expected occupancy level meeting a threshold value.