Patent ID: 9202066
Filing Date: 2015-12-01
Classification: G06F,G06Q,G16H,H04L

Abstract:
1. A method comprising: grouping, with a processor in communication with a database, a plurality of de-identified data sets into a plurality of cohorts, each of the plurality of de-identified data sets being derived from data associated with one of a plurality of patients and each of the plurality of de-identified data sets comprising at least one attribute, each cohort comprising at least one of the plurality of de-identified data sets grouped together based on at least one similarity of the at least one attribute; receiving, with the processor, a patient data set comprising at least one of the attributes, the patient data set associated with a patient having a treatable medical condition or disease; determining, with the processor, a relationship between the patient data set and one of the plurality of cohorts based on a similarity between the at least one attribute of the patient data set and the at least one attribute of the cohort; determining, with the processor, a predicted treatment cost associated with the one of the plurality of cohorts; receiving, with the processor, an evidence-based treatment cost for the patient, the evidence-based treatment cost being the cost of an evidence-based treatment for the treatable medical condition or disease; comparing, with the processor, the evidence-based treatment cost to the predicted treatment cost; when the evidence-based treatment cost is greater than the predicted treatment cost, generating, with the processor, an alternative treatment for the treatable medical condition or disease based on the one of the plurality of cohorts, the alternative treatment being different from the evidence-based treatment; determining, with the processor, an alternative predicted treatment cost associated with the alternative treatment; comparing, with the processor, the evidence-based treatment cost to the alternative predicted treatment cost; generating, with the processor, a treatment recommendation based on the comparing of the evidence-based treatment cost to the alternative predicted treatment cost; de-identifying, with the processor, the patient data set to generate a new de-identified data set that is not associated with the patient; encrypting, with the processor, the new de-identified data set to generate an encrypted data set; and storing, with the processor, the encrypted data set in the database as one of the plurality of de-identified data sets.