Abstract: Techniques for implementing a decision-making assistant tool are disclosed herein. In one particular aspect, identity information about a first entity and contextual information about a second entity are receiving. Subsequently, the decision-making assistant tool can be initialized. A first set of data can be requested from a first computing system by the decision-making assistant tool. A request for a second set of data can be transmitted to a second computing system disjunctive with the first computing system and by the decision-making assistant tool. The first set of data and the second set of data can be de-duplicated to generate de-duplicated data. The de-duplicated data can be provided to the first entity.

Abstract: A system includes a learning filter for deidentified healthcare data. The system provides self-improving data filtering of data for the detection of disorders for engaging patients in observational research to gather additional data. A processor performs operations including obtaining patient information about a patient and current order data. The current order data indicates an order for a laboratory test for the patient and obtaining, from a database, historical data indicating a previous laboratory test for the patient and a result of the previous laboratory test. The operations further include determining that the patient is a subject of interest for a disorder by applying the filter to historical data to produce filtered data and comparing the filtered data to a profile associated with the disorder. Additional laboratory tests can be performed, after which a hash-to-patient identifier (PID) mapping database and the filter can be updated.

Abstract: A system includes a learning filter for deidentified healthcare data. The system provides self-improving data filtering of data for the detection of disorders for engaging patients in observational research to gather additional data. A processor performs operations including obtaining patient information about a patient and current order data. The current order data indicates an order for a laboratory test for the patient and obtaining, from a database, historical data indicating a previous laboratory test for the patient and a result of the previous laboratory test. The operations further include determining that the patient is a subject of interest for a disorder by applying the filter to historical data to produce filtered data and comparing the filtered data to a profile associated with the disorder. Additional laboratory tests can be performed, after which a hash-to-patient identifier (PID) mapping database and the filter can be updated.

Abstract: A system includes a learning filter for deidentified healthcare data. The system provides self-improving data filtering of data for the detection of disorders for engaging patients in observational research to gather additional data. A processor performs operations including obtaining patient information about a patient and current order data. The current order data indicates an order for a laboratory test for the patient and obtaining, from a database, historical data indicating a previous laboratory test for the patient and a result of the previous laboratory test. The operations further include determining that the patient is a subject of interest for a disorder by applying the filter to historical data to produce filtered data and comparing the filtered data to a profile associated with the disorder. Additional laboratory tests can be performed, after which a hash-to-patient identifier (PID) mapping database and the filter can be updated.

Abstract: A system includes a learning filter for deidentified healthcare data. The system provides self-improving data filtering of data for the detection of disorders for engaging patients in observational research to gather additional data. A processor performs operations including obtaining patient information about a patient and current order data. The current order data indicates an order for a laboratory test for the patient and obtaining, from a database, historical data indicating a previous laboratory test for the patient and a result of the previous laboratory test. The operations further include determining that the patient is a subject of interest for a disorder by applying the filter to historical data to produce filtered data and comparing the filtered data to a profile associated with the disorder. Additional laboratory tests can be performed, after which a hash-to-patient identifier (PID) mapping database and the filter can be updated.