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Based on the study of the literature on the quality assessment of operativetreatment in reconstructive surgery of the spine and pelvic complex, it can beconcluded that, as a rule, multiple linear or logistic regression, adecision tree, is used to predict the quality of operative treatment. Neuralnetworks are less commonly used. Forecasting is performed on the basis of a comparison of the pre- andpostoperative condition of the patient, assessed according to variousordinal and quantitative scales as a result of interviewing the patient. With a relatively small number of analyzed cases of the disease (severaltens or hundreds) and a small number of indicators (no more than two orthree dozen), the use of neural networks seems premature for two reasons: asmall amount of data allows analyzing them with classical methods ofmathematical statistics, and identifying dependencies on a given stagerequires constant “manual” intervention, taking into account informationfrom the subject area. The application of statistical analysis methods to data on the treatment ofchronic injuries showed the presence of standard problems for medical data.This is the presentation of the initial information in nominal or ordinalscales, the subjective nature of some indicators, as well as theinterdependence of the presented characteristics, which reduces the qualityof research. The search for the objective function that characterizes the quality ofsurgical treatment has shown the ambiguity of solving this problem even fora highly specialized situation. The identification of objectively present relationships also revealed alarge number of problems, especially related to the choice of the type ofsurgical treatment, which is largely determined by the experience of thesurgeon. Based on the study, it was proposed to build a model for predicting thequality of surgical treatment, based on expert assessments in the form of aforecast tree with recommended surgical treatment options and a statisticalforecast based on the available experience. It is assumed that the modelwill be dynamic with feedback and be able to self-update. To predict the quality of surgical treatment in reconstructive surgery ofthe spine and pelvic complex, it is advisable to use a forecast tree, which allows usto recommend the type of surgery for a specific case of injury or diseaseand calculate the predicted values of quality of life indicators.