Abstract: This disclosure relates to systems and methods for managing patients of knee surgeries. A pre-operative patient questionnaire user interface is associated with a future knee operation of the patient. Patient input data is indicative of answers of a patient in relation to the pre-operative patient questionnaire. A processor of a computer system evaluates a statistical model to determine a predicted satisfaction value indicative of satisfaction of the patient with the future knee operation. The statistical model comprises nodes stored on data memory representing the patient input data and the predicted satisfaction value, and edges stored on data memory between the nodes representing conditional dependencies between the patient input data and the predicted satisfaction value. The processor then generates an electronic document comprising a surgeon report associated with the future knee operation to indicate to the surgeon the predicted satisfaction value.

Abstract: Techniques for qualifying a candidate supplier are presented. Such techniques may include obtaining a part produced by a candidate supplier and measuring a quantifiable property in each of a plurality of samples to obtain an empirical data set. Such techniques may also include selecting, based on the empirical data set, and fitting to the empirical data set, an appropriate linear mixed model for the quantifiable property. Such techniques may further include computing an acceptance parameter from a mean and standard deviation obtained from the appropriate linear mixed model. The acceptance parameter may include a process capability index or a tolerance interval bound. Such techniques may further include determining that the candidate supplier qualifies based on comparing the acceptance parameter to a threshold.

Abstract: Techniques for qualifying a candidate supplier are presented. Such techniques may include obtaining a part produced by a candidate supplier and measuring a quantifiable property in each of a plurality of samples to obtain an empirical data set. Such techniques may also include selecting, based on the empirical data set, and fitting to the empirical data set, an appropriate linear mixed model for the quantifiable property. Such techniques may further include computing an acceptance parameter from a mean and standard deviation obtained from the appropriate linear mixed model. The acceptance parameter may include a process capability index or a tolerance interval bound. Such techniques may further include determining that the candidate supplier qualifies based on comparing the acceptance parameter to a threshold.