Machine learning models in location based episode prediction

At least one computer-readable storage medium, excluding transitory signals and carrying instructions, which, when executed by at least one data processor of a system, cause the system to:

• train a predictive model based on training data including records of non-public healthcare insurance claims and public healthcare fee schedule data;
• determine a multiplier for each of multiple healthcare facilities based on the predictive model,
• wherein a first multiplier for a first healthcare facility is configured to scale the public healthcare fee schedule data such that cost estimates for healthcare services provided by the first healthcare facility are predicted based on the first multiplier;
• and
• wherein a second multiplier for a second healthcare facility, different from the first multiplier, is configured to scale the public healthcare fee schedule data such that cost estimates for healthcare services provided by the second healthcare facility are predicted based on the second multiplier;
• receive, from a consumer device, a query for a particular cost estimate of a particular healthcare service provided by the multiple healthcare facilities,
• wherein the query includes constraints for a preferred geographic region or a preferred insurer, and
• wherein the first and second healthcare facilities satisfy the constraints of the query;
• predict a first cost estimate for the particular healthcare service at the first healthcare facility based on the first multiplier and a second cost estimate for the particular healthcare service at the second healthcare facility based on the second multiplier;
• and
• cause the consumer device to display query results including indications of the first and second cost estimates.

A method comprising:

• determining a multiplier for each of multiple healthcare facilities based on records of non-public healthcare insurance claims and public healthcare fee schedule data,
• wherein a first multiplier for a first healthcare facility is configured to scale the public healthcare fee schedule data such that cost estimates for healthcare services provided by the first healthcare facility are based on the first multiplier;
• and
• wherein a second multiplier for a second healthcare facility, different from the first multiplier, is configured to scale the public healthcare fee schedule data such that cost estimates for healthcare services provided by the second healthcare facility are based on the second multiplier;
• receiving, from a consumer device, a query for a particular cost estimate of a particular healthcare service provided by the multiple healthcare facilities,
• wherein the query includes constraints for a preferred geographic region or a preferred insurer, and
• wherein the first and second healthcare facilities satisfy the constraints of the query;
• predicting a first cost estimate for the particular healthcare service at the first healthcare facility based on the first multiplier and a second cost estimate for the particular healthcare service at the second healthcare facility based on the second multiplier;
• and
• causing the consumer device to display query results including indications of the first and second cost estimates.

A server computer system comprising:

• a processor;
• and
• a memory including instructions that, when executed by the processor, cause the server computer system to:
• train a predictive model based on training data including records of non-public healthcare insurance claims and public healthcare fee schedule data;
• and
• determine a multiplier for each of multiple healthcare facilities based on the predictive model,
• wherein a first multiplier for a first healthcare facility is configured to scale the public healthcare fee schedule data such that cost estimates for healthcare services provided by the first healthcare facility are predicted based on the first multiplier;
• and
• wherein a second multiplier for a second healthcare facility, different from the first multiplier, is configured to scale the public healthcare fee schedule data such that cost estimates for healthcare services provided by the second healthcare facility are predicted based on the second multiplier.