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 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.
The rest of this entry review various perspectives associated with the process of producing SD predictions — SD statistical distributions, Methods to reduce SD variability , Predictive models and methods, and Surgery as a work-process. The latter addresses surgery characterization as a work-process and its effect on SD distributional shape.
SD Statistical Distributions
Theoretical models
A most straightforward SD predictive method comprises specifying a set of existent statistical distributions, and based on available data and distribution-fitting criteria select the most fitting distribution. There is a large volume of comparative studies that attempt to select the most fitting models for SD distribution. Distributions most frequently addressed are the normal, the three-parameter lognormal, gamma and Weibull. Less frequent "trial" distributions are the loglogistic model, Burr, gamma distribution|generalized gamma] and the piecewise-constant hazard model. Attempts to presenting SD distribution as a mixture-distribution have also been reported. Occasionally, predictive methods are developed that are valid for a general SD distribution, or more advanced techniques, like Kernel Density Estimation, are used instead of the traditional methods. There is broad consensus that the three-parameter lognormal describes best most SD distributions. A new family of SD distributions, which includes the normal, lognormal and exponential as exact special cases, has recently been developed. Here are some examples.Using historical records to specify an empirical distribution
As an alternative to specifying a theoretical distribution as model for SD, one may use records to construct a histogram of available data, and use the related empirical distribution function to estimate various required percentiles. Historical records/expert estimates may also be used to specify location and scale parameters, without specifying a model for SD distribution.Data mining methods
These methods have recently gained traction as an alternative to specifying in-advance a theoretical model to describe SD distribution for all types of surgeries. Examples are detailed below.Reducing SD variability (stratification and covariates)
To enhance SD prediction accuracy, two major approaches are pursued to reduce SD data variability: Stratification and covariates. Covariates are often referred to in the literature also as factors, effects, explanatory variables or predictors.Stratification
The term means that available data are divided into subgroups, according to a criterion statistically shown to affect SD distribution. The predictive method then aims to produce SD prediction for specified subgroups, having SD with appreciably reduced variability. Examples for stratification criteria are medical specialty, Procedure Code systems, patient-severity condition or hospital/surgeon/technology. Examples for implementation are Current Procedural Terminology and ICD-9-CM Diagnosis and Procedure Codes.Covariates (factors, effects, explanatory variables, predictors)
This approach to reduce variability incorporates covariates in the prediction model. The same predictive method may then be more generally applied, with covariates assuming different values for different levels of the factors shown to affect SD distribution. A most basic method to incorporate covariates into a predictive method is to assume that SD distribution is lognormally distributed. The logged data then represent a normally distributed population, allowing use of multiple- linear-regression to detect statistically significant factors. Other regression methods, which do not require data normality or are robust to its violation and artificial intelligence methods have also been used.Predictive models and methods
Following is a representative list of models and methods employed to produce SD predictions. These, or a mixture thereof, may be found in the sample of representative references below:Linear regression ; Multivariate adaptive regression splines ; Random forests ; Machine learning; Data mining ; Knowledge discovery in databases ; Data warehouse model ; Kernel density estimation ; Jackknife; Monte Carlo simulation.