3m Innovative Properties Company

Amazon Technologies, Inc.

Richard Hansen Wolniewicz

Eric C Lobner

Brian J Stankiewicz

William L Schofield, Iii

Ion T Ieong

Christopher R Barkley

Matthew O Anderson

This disclosure includes techniques for analyzing patient data. In one example, a method includes accessing, by a computer system, one or more databases comprising health information, with protected health information, for a plurality of patients, accessing, by the computer system, an analytical model, and receiving, by the computer system via a user interface, instructions to apply the analytical model to health information for each of the plurality of patients. The protected health information is isolated from the user interface to restrict access to the protected health information. The method further includes applying, by the computer system, the analytical model to health information for each of the plurality of patients, and storing a result of the analytical model to the one or more databases.

Performing analytics on protected health information

At least some aspects of the present disclosure feature systems and methods for obfuscating data. The method includes the steps of receiving an input data stream including a sequence of n-grams, mapping at least some of the sequence of n-grams to corresponding dictionary terms using a dictionary, and disposing the corresponding tokens to an output data stream.

Systems and method for obfuscating data using dictionary

This disclosure includes techniques for analyzing patient data. In one example, a method includes accessing, by a computer system, one or more databases comprising health information for a plurality of patients, wherein the health information includes protected health information, randomly selecting, by the computer system, a subset of the health information from the one or more databases, wherein the subset of the health information corresponds to a subset of the plurality of patients, removing, by the computer system, the protected health information from the subset of health information to produce a de-identified analytics subset of patient data suitable for analytical model construction and evaluation, and storing, by the computer system, the de-identified analytics subset of the patient data in the one or more databases.

Analytic modeling of protected health information

At least some aspects of the present disclosure feature systems and methods for obfuscating data. The method includes the steps of receiving or retrieve an input data stream including a sequence of n-grams, mapping at least some of the sequence of n-grams to corresponding tokens using an obfuscation table, and disposing the corresponding tokens to an output data stream.

Obfuscating data using obfuscation table

Perfoming analytics on protected health information

A facility selects a unit of content to include in an instance of a content container. For each of a plurality of units of content, across a plurality of other instances of the content container distributed during a foregoing period of time that included the unit of content, the facility determines both an average level of success achieved by including the unit of content, and a measure of the level of variability of the level of success achieved by including the unit of content. The facility scores each unit of content by selecting a value from a probability distribution that is based on the determined average level of success and measure of the level of variability of the level of success. The facility selects one of the units of content to include in the instance of the content container based on the determined scores.

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Longmont, CO, United States of America

Richard Hansen Wolniewicz