Abstract: Methods and systems for generating a report that ranks patients at risk for readmission to an acute care facility from a post-acute care facility. A system receives, from the post-acute care facility, patient data for a plurality of patients. The system inputs the patient data into a risk machine-learning system that was previously trained using historical patient data and data reflecting any readmissions from one or more post-acute care facilities to one or more acute care facilities. The system determines, by the risk machine-learning system, a risk score for each patient. Each risk score represents risk of a respective patient being readmitted to an acute care facility from the post-acute care facility. The system further generates the report. The report including a list of at least a subset of patients from the plurality of patients ranked from the patient with the highest risk to the lowest risk of readmission.

Abstract: A computer-implemented-method for adjusting keyword bids includes aggregating a plurality of keywords into a pool and performing a statistical analysis of the pool to determine a return-on-investment factor. Each keyword is then evaluated in view of the return-on-investment factor and use of an individual keyword is paused or a bid price of the individual keyword is maintained or adjusted or the individual keyword is deleted—all of which is based on the return-on-investment factor.