Systems and methods for patient record matching

An artificial intelligence (AI) record matching system comprising:

• one or more processors at a healthcare management system that are configured to obtain patient records having demographic information including first names, the one or more processors configured to determine a difference between birth dates in the patient records,
• the one or more processors configured to use artificial neurons in different layers and connected by mathematical relationships to classify the difference between the birth dates between at least two different confidence levels, the one or more processors configured to determine whether and what additional matching information is required in the patient records to determine that the patient records match to a same patient based on the confidence level to which the difference between the birth dates is classified, the one or more processors configured to determine that the patient records match to the same person based on the difference between the birth dates, the one or more processors configured to repeatedly receive feedback data indicative of whether the patient records that are identified as matching to the same patient do match, the one or more processors configured to repeatedly train the artificial neurons based on the feedback data by repeatedly modifying the mathematical relationships between the artificial neurons to change one or more of the confidence levels,
• the one or more processors configured to use the mathematical relationships between the artificial neurons that are modified to change which of the confidence levels that the difference in the birth dates is classified during successive iterations of the one or more processors examining the patient records.

An artificial intelligence (AI) record matching system comprising:

• one or more processors at a healthcare management system that are configured to obtain patient records having demographic information including first names or last names, the one or more processors configured to determine that the first names are entirely different first names, the last names are entirely different last names, or both the first names are entirely different first names and the last names are entirely different last names,
• the one or more processors configured to use artificial neurons in different layers and connected by mathematical relationships to examine the patient records for additional distinguishing demographic features that match, the one or more processors configured to determine whether the patient records match to a same person based on the additional distinguishing demographic features, the one or more processors configured to define and save a linkage between the entirely different first names or between the entirely different last names responsive to determining that the patient records match to the same person,
• the one or more processors configured to use the linkage that is defined in comparing additional patient records to determine whether the additional patient records match to the same person,
• the one or more processors configured to repeatedly receive feedback data indicative of whether the patient records that are identified as matching to the same patient do match, the one or more processors configured to repeatedly train artificial neurons based on the feedback data by repeatedly modifying mathematical relationships between the artificial neurons to change the additional distinguishing demographic features that are examined,
• the one or more processors configured to use the mathematical relationships between the artificial neurons that are modified to change which of the additional distinguishing demographic features are examined during successive iterations of the one or more processors examining the patient records.

A method comprising:

• obtaining patient records at an artificial intelligence (AI) record matching system, the patient records including demographic information containing first names;
• calculating commonality measurements of first names in the patient records in a population;
• and
• identifying the patient records as matching to a same patient based on the patient records including additional matching information, the patient records identified as matching by requiring different amounts of the additional matching information for different values of the commonality measurements.