Patient Treatment Resource Utilization Predictor

A computer implemented method comprising:

• accessing a trained machine learning system that has been trained by:
• obtaining a schema defining variables representing patient treatment experiences for one or more health conditions;
• obtaining data from historical patient treatment experiences, the data being arranged in accordance with the schema;
• training a machine learning system based on a training set of the data for a specified time-period;
• generating a model from the trained machine learning system, the model being based on variables selected by the trained machine learning system to predict patient treatment experiences for the one or more health conditions;
• testing the trained machine learning system on a set of test data from the obtained data from historical patient treatment experiences;
• classifying test data patient treatment experiences via the trained machine learning system;
• identifying the number of patient treatment experiences misclassified by the machine learning system as “in story”;
• identifying the number of patient treatment experiences correctly classified by the machine learning system as “out of story”;
• generating a new model using one or more qualifiers based on incorrectly classified in story patient treatment decisions to increase the accuracy of the machine learning system;
• and
• analyzing, using the trained machine learning system, the patient discharge records to measure the performance of a health care facility.

A system comprising:

• a processor and a memory device coupled to the processor and having instructions stored thereon for execution by the processor to perform operations comprising:
• accessing a trained machine learning system that has been trained by:
• obtaining a schema defining variables representing patient treatment experiences for one or more health conditions;
• obtaining data from historical patient treatment experiences, the data being arranged in accordance with the schema;
• training a machine learning system based on a training set of the data for a specified time-period;
• generating a model from the trained machine learning system, the model being based on variables selected by the trained machine learning system to predict patient treatment experiences for the one or more health conditions;
• testing the trained machine learning system on a set of test data from the obtained data from historical patient treatment experiences;
• classifying test data patient treatment experiences via the trained machine learning system;
• identifying the number of patient treatment experiences misclassified by the machine learning system as “in story”;
• identifying the number of patient treatment experiences correctly classified by the machine learning system as “out of story”;
• generating a new model using one or more qualifiers based on incorrectly classified in story patient treatment decisions to increase the accuracy of the machine learning system;
• and
• analyzing, using the trained machine learning system, the patient discharge records to measure the performance of a health care facility.