Abstract: A method and system for generating real time workload balancing recommendations comprising receiving transition data, medical data, and staffing data; determining a transition probability for each of a plurality of patients; determining a predicted workload to be generated by each of the plurality of patients; simulating the predicted workload to be generated by each of the plurality of patients, the future workload for each of a plurality of units in the hospital; generating staffing recommendations; and displaying the generated staffing recommendations on a user display of the workload balancing system.

Abstract: A method for performing a demand analysis for a hospital, including: (i) receiving hospital capacity information; (ii) receiving hospital data, the hospital data comprising information on patient admissions, patient discharges, and patient transfers for a previous period of time for each of a plurality of patient types; (iii) adapting parameters of a machine learning algorithm based on the hospital data; (iv) receiving clinical information about patients currently admitted in the hospital; and (v) determining, based on output from the adapted machine learning algorithm and using the current clinical information and the hospital capacity information, a predicted patient flow for the hospital in real-time. The method further includes displaying, to at least one user in real-time, the predicted patient flow for the ward and at least one suggested rearrangement of resources within the hospital.

Abstract: A method for performing, using a patient disposition system, a disposition analysis of a plurality of patients to optimize a discharge planning process for each of the plurality of patients, including: (i) receiving electronic medical record information about each of the plurality of patients; (ii) identifying one of a plurality of different patient types for each of the plurality of patients based on the received electronic medical record information; (iii) selecting a trained multi-state model for each identified patient type; and (iv) determining, based on the selected trained multi-state model, a disposition state for each of the plurality of patients in real-time, wherein each disposition state includes a location to which the patient is to be discharged. The method further includes determining at least one service or assessment that can be deferred to the location to which the patient is to be discharged.

Abstract: A method for performing a demand analysis for a hospital, including: (i) receiving hospital capacity information; (ii) receiving hospital data, the hospital data comprising information on patient admissions, patient discharges, and patient transfers for a previous period of time; (iii) adapting parameters of a machine learning algorithm based on the hospital data; (iv) receiving clinical information about patients currently admitted in the hospital; (v) determining, based on output from the adapted machine learning algorithm and clinical information about the patients currently admitted in the hospital and the hospital capacity information a predicted patient flow for the hospital in real-time; (vi) detecting a deviation between the predicted patient flow and at least one actual data point; and (vii) displaying to at least one user in real-time, the detected deviation for the hospital.