Randomization methods for healthcare scheduling optimization using perioperative stages

A computer-implemented method, the computer-implemented method comprising:

• receiving a historical dataset;
• receiving performance data relating to a service, wherein the service includes at least one healthcare procedure taking place with a healthcare resource, wherein the at least one healthcare procedure is at least one surgical procedure, wherein the healthcare resource includes an operating room, and wherein the performance data includes live surgical data relating to at least one of the healthcare procedures and the healthcare resource, including usage time of the healthcare resource, turn-around-time of the healthcare resource, and uptime of the healthcare resource;
• processing the historical dataset and the performance data into a processed historical dataset and processed performance data, wherein the processing comprises:
• filtering the historical dataset and the performance data into discrete groups identified by type of healthcare procedure,
• calculating an expected completion time for each of the at least one healthcare procedure based on one or more probability models, wherein the one or more probability models include actual surgical duration for each at least one healthcare procedure, procedure volume changes, cancellation rates for each at least one healthcare procedure, and/or combined emergency surgical rates,
• wherein the calculating the expected completion time for each of the at least one healthcare procedure includes calculating a duration of sequential perioperative sub-stages of the at least one healthcare procedure, wherein the duration of sequential perioperative sub-stages include pre-anesthesia duration, patient positioning duration, surgery duration, and post anesthesia duration,
• wherein the calculating the expected completion time for each of the at least one healthcare procedure includes aggregating the calculated duration of the sequential perioperative sub-stages for each of the at least one healthcare procedure,
• calculating a standard deviation for each expected completion time for each at least one healthcare procedure;
• creating a first training set comprising the processed historical dataset and the processed performance data;
• and
• training a machine learning model using the first training set for predicting the duration of the sequential perioperative sub-stages of the at least one healthcare procedure, predicting the expected completion time for each of the at least one healthcare procedure, and for generating a schedule of the at least one healthcare procedure to take place with the healthcare resource.