What is Predictive methods for surgery duration?

Predictions of surgery duration are used to schedule planned/elective surgeries so that utilization rate of operating theatres be optimized. An example for a constraint is that a pre-specified tolerance for the percentage of postponed surgeries or recovery room space] not be exceeded. The tight linkage between SD prediction and surgery scheduling is the reason that most often scientific research related to scheduling methods addresses also SD predictive methods and vice versa. Durations of surgeries are known to have large variability. Therefore, SD predictive methods attempt, on the one hand, to reduce variability , and on the other employ best available methods to produce SD predictions. The more accurate the predictions, the better the scheduling of surgeries.

An SD predictive method would ideally deliver a predicted SD statistical distribution. Once SD distribution is completely specified, various desired types of information could be extracted thereof, for example, the most probable duration , or the probability that SD does not exceed a certain threshold value. In less ambitious circumstance, the predictive method would at least predict some of the basic properties of the distribution, like location and scale parameters. Certain desired percentiles of the distribution may also be the objective of estimation and prediction. Experts estimates, empirical histograms of the distribution , data mining and knowledge discovery techniques often replace the ideal objective of fully specifying SD theoretical distribution.

Reducing SD variability prior to prediction is commonly regarded as part and parcel of SD predictive method. Most probably, SD has, in addition to random variation, also a systematic component, namely, SD distribution may be affected by various related factors. Accounting for these factors would diminish SD variability and enhance the accuracy of the predictive method. Incorporating expert estimates in the predictive model may also contribute to diminish the uncertainty of data-based SD prediction. Often, statistically significant covariates — are first identified , and only later more advanced big-data techniques are employed, like Artificial Intelligence and Machine Learning, to produce the final prediction.

Literature reviews of studies addressing surgeries scheduling most often also address related SD predictive methods. Here are some examples.