| CPC G16H 40/20 (2018.01) | 10 Claims |
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1. A method of scheduling, comprising:
receiving historical data and live operating data relating to at least one healthcare procedure, wherein the at least one healthcare procedure is at least one surgical procedure, the at least one surgical procedure corresponding to an entire perioperative process of the at least one surgical procedure, and wherein the live operating data includes live surgical data relating to a surgical procedure and a healthcare resource, including usage time of the healthcare resource, turn-around-time of the healthcare resource, and uptime of the healthcare resource;
processing the data using a machine learning model, wherein the processing comprises:
applying one or more probability models to the data, wherein the one or more probability models include actual surgical duration for each surgical procedure, procedure volume changes, cancellation rates for each surgical procedure, and combined emergency surgical rates,
performing one or more iterations of a Monte Carlo simulation on the data to calculate the collective time for provision of the at least one healthcare procedure,
applying a stochastic optimization to the one or more iterations of the Monte Carlo simulation, wherein the stochastic optimization process is based on a site configuration of a location containing the healthcare resource,
determining an expected completion time for each of the at least one healthcare procedure taking place with a healthcare resource, based on the results of the stochastic optimization, wherein the healthcare resource is an operating room, wherein the determining the expected completion time includes using sub-models of the machine learning model to predict duration of sequential perioperative sub-stages of each of the at least one surgical procedure, wherein the duration of the sequential perioperative sub-stages include, in sequence: pre-anesthesia duration, patient positioning duration, surgery duration, and post anesthesia duration, and wherein the determining the expected completion time includes aggregating the predicted duration of the sequential perioperative sub-stages for each of the at least one surgical procedure, and
determining, based on the expected completion time for each of the at least one healthcare procedure taking place with the healthcare resource, availability for one or more healthcare procedures with the healthcare resource, wherein determining the availability for one or more healthcare procedures is based on duration of the one or more healthcare procedures, importance of the one or more healthcare procedures, and availability of the one or more healthcare resources; and
generating a schedule of the one or more healthcare procedures to take place with the healthcare resource.
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