Mitchell International, Inc.

A computer-implemented method for adjusting one or more electronic medical bills for a claimant injured in an accident comprises generating a user interface to be presented to a claims adjuster; receiving a first user input identifying a claimant; responsive to the first user input, retrieving and aggregating multiple electronic medical bills each having at least one line; generating one or more findings and multiple scenarios by providing the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios, wherein responsive to the inference input, the trained machine learning model outputs the one or more findings and the multiple scenarios, wherein the one or more findings represent rationales for approving, denying, or repricing, and wherein the multiple scenarios include cost estimates based on the one or more findings.

In general, one aspect disclosed features a system, comprising: a hardware processor; and a non-transitory machine-readable storage medium encoded with instructions executable by the hardware processor to perform operations comprising: receiving an electronic record, the electronic record representing a medical bill, the medical bill comprising a plurality of attributes; mapping each attribute in the medical bill to a single bucket of a predetermined second quantity of the buckets according to a predetermined correspondence between the attributes and the buckets, the first quantity exceeding the second quantity; and providing identifiers of the single buckets as input to a machine learning model, the machine learning model being trained according to historical correspondences between the buckets and decisions of whether human review was necessary, wherein responsive to the input, the machine learning model provides as output an indication of whether the medical bill should be reviewed by a human.

A computer-implemented method for adjusting one or more electronic medical bills for a claimant injured in an accident comprises generating a user interface to be presented to a claims adjuster; receiving a first user input identifying a claimant; responsive to the first user input, retrieving and aggregating multiple electronic medical bills each having at least one line; generating one or more findings and multiple scenarios by providing the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios, wherein responsive to the inference input, the trained machine learning model outputs the one or more findings and the multiple scenarios, wherein the one or more findings represent rationales for approving, denying, or repricing, and wherein the multiple scenarios include cost estimates based on the one or more findings.

Systems and methods are provided for a dynamic and iterative process for determining a weighted decision using a combination of weighted output from multiple, trained machine learning (ML) models. Key data can be identified and efficient decision-based processing can be achieved. In some examples, the system calculates a weighted decision of a repair or total loss determination for a motor vehicle, yet any industry or data set may be implemented with the use of the dynamic and iterative decision process.

In general, one aspect disclosed features a system, comprising: a hardware processor; and a non-transitory machine-readable storage medium encoded with instructions executable by the hardware processor to perform operations comprising: receiving an electronic record, the electronic record representing a medical bill, the medical bill comprising a plurality of attributes; mapping each attribute in the medical bill to a single bucket of a predetermined second quantity of the buckets according to a predetermined correspondence between the attributes and the buckets, the first quantity exceeding the second quantity; and providing identifiers of the single buckets as input to a machine learning model, the machine learning model being trained according to historical correspondences between the buckets and decisions of whether human review was necessary, wherein responsive to the input, the machine learning model provides as output an indication of whether the medical bill should be reviewed by a human.