Abstract: A system for performing prediction-based automated medical examination resource management, the system being configured to collect raw data related to computerized tomography (CT) examinations from an electronic medical record (EMR) system or a hospital information system (HIS), predict a predicted demand curve representing a trend of a future average number of daily prescriptions based on the raw data by using machine learning, derive a predicted waiting day curve indicating expected waiting days for each future date by using the predicted demand curve and available examination capacity (CAPA) data of a CT scanner equipment for each date, and automatically transmit a control signal to a hospital scheduling system or CT scanner equipment when the expected waiting date for each future date on the predicted waiting day curve satisfies a predefined threshold condition.

Abstract: Provided are a method of predicting waiting days for a medical examination requiring a reservation at a hospital, the method including acquiring examination prescription and execution data, calculating, based on the examination prescription and execution data, an average ratio of executed examinations for each day required from a prescription to the execution thereof, and an average number of prescriptions based on past weekdays, estimating, based on the average number of prescriptions based on past weekdays, an average number of prescriptions based on future weekdays for a predetermined period by using at least one time-series prediction model, calculating a monthly increment in the average number of prescriptions based on future weekdays, compared to the average number of prescriptions based on the past weekdays, and allocating, based on the average number of prescriptions based on the past weekdays and the monthly increment, an expected number of prescriptions based on future weekdays.