Predictions of surgery<unk>  (SD) are used to schedule planned/elective surgeries so that utilization rate of operating theatres be optimized (maximized subject to policy constraints). An example for a constraint is that a pre-specified tolerance for the percentage of postponed surgeries (due to non-available operating room (OR) 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<unk>  addresses also SD<unk><unk>  and vice versa.<unk> s of surgeries are known to have large variability. Therefore, SD<unk><unk>  attempt, on the one hand, to reduce variability (via stratification and covariates, as detailed later), and on the other employ best available<unk>  to produce SD predictions. The more accurate the predictions, the better the scheduling of surgeries (in terms of the required OR utilization optimization). An SD<unk> <unk> would ideally deliver a predicted SD statistical distribution (specifying the distribution and estimating its parameters). Once SD distribution is completely specified, various desired types of information could be extracted thereof, for example, the most probable<unk>  (mode), or the probability that SD does not exceed a certain threshold value. In less ambitious circumstance, the<unk> <unk> would at least predict some of the basic properties of the distribution, like location and scale parameters (mean, median, mode, standard deviation or coefficient of variation, CV). Certain desired percentiles of the distribution may also be the objective of estimation and prediction. Experts estimates, empirical histograms of the distribution (based on historical computer records), data mining and knowledge discovery techniques often replace the ideal objective of fully specifying SD theoretical distribution. Reducing SD variability prior to prediction (as alluded to earlier) is commonly regarded as part and parcel of SD<unk> <unk>  Most probably, SD has, in addition to random variation, also a systematic component, namely, SD distribution may be affected by various related factors (like medical specialty, patient condition or age, professional experience and size of medical team, number of surgeries a surgeon has to perform in a shift, type of anesthetic administered). Accounting for these factors (via stratification or covariates) would diminish SD variability and enhance the accuracy of the<unk> <unk>  Incorporating expert estimates (like those of surgeons) in the<unk>  model may also contribute to diminish the uncertainty of data-based SD prediction. Often, statistically significant covariates (also related to as factors, predictors or explanatory variables) — are first identified (for example, via simple techniques like linear regression and knowledge discovery), 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<unk><unk> . Here are some examples (latest first). The rest of this entry review various perspectives associated with the process of producing SD predictions — SD statistical distributions,<unk>  to reduce SD variability (stratification and covariates),<unk>  models and<unk> , and Surgery as a work-process. The latter addresses surgery characterization as a work-process (repetitive, semi-repetitive or memoryless) and its effect on SD distributional shape.
Predictive methods for surgery duration
Instruments used in general surgery
Perspectives in Vascular Surgery and Endovascular Therapy
Perioperative mortality
Theories of general anaesthetic action
Hysterectomy Educational Resources and Services 
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Instruments used in obstetrics and gynecology