Questions to the Patient about Informed Consent

Background: Informed consent is a right of the patient that consists of the patient before the medical intervention is carried out in his body, must express his consent that must be preceded by the due information that allows him to decide according to his interests. As a correlation of this right, the doctor's obligation to inform the patient and to obtain his consent before carrying out the medical act arises.

Purpose: The purpose is to study Patient Information within the informed consent "Patient Information" variable.

Methods: This study collects data from hospitals in the University Hospital of Burgos, Spain, for two years, configuring a file with data with 647 cases and 23 variables, 21 of them referring to the attitude towards informed consent, Sex and Age. We will previously carry out a descriptive-exploratory and comparative analysis to have information on the variables that make up the classification/prediction model (Artificial Neural Network), how the data are distributed by category ("Yes" and "Not") of the variable "Patient Information". The study used a three-layer perceptron (input, hidden and output) to analyze three cases: i) One covariate variable (Age); ii) One covariate variable (Consultation time) y iii) Two covariate variables (Age and Consultation time).

Results: Tables 3 and 3(continued) show the results of cross-referencing the variable "Patient Information" with the rest of the qualitative variables. The study on the variable "Age", the study of the difference in mean age, generated by the variables that have two categories (Table 4) and three categories (Table 5) leads us to know which difference in means is significant for a level of significance of 5%. The most efficient artificial neural network structure found in the classification of the categories of the variable "Patient Information" ("Yes" and "Not" categories) is the binomial hidden layer-output layer: hyperbolic tangent- softmax (Dependent variable: "Patient Information"; Partition: Training 60%, Testing 20% and Holdout 20%). Qualifying results are very low for the "Not" category.

Conclusion: The information process, in order to obtain informed consent, has an essentially particular character for each patient, it must be away from any situation of overcrowding, bureaucratization and dehumanization and must be based on their self-determination and freedom.

Most patients are aware of the existence of a legal rule that obliges the physician to inform in order to obtain consent. Patients show total awareness of the right they have to be informed, evidence the desire to know, affirm their right to their family and most deny that the information provided by the doctor has caused them a state of fear or anxiety.

The study of the variable "Patient Information" using the artificial neural network, perceptron, offers us a low classification/prediction of the "Not" category.

In all cases of the study, applying the artificial neural network, a low percentage of classification is observed for the “Not” category due, in part, to the low number of cases for this category. We can observe that if we use two covariate variables, the classification percentage increases for this category.