MODELING AND PREDICTING INSURANCE REIMBURSEMENT FOR MEDICAL SERVICES
Systems and methods are provided for monitoring insurance billing events using patient data, healthcare services data, insurance reimbursement criteria and insurance reimbursement records to model and calculate an insurance reimbursement score for a future planned health service event using an analytic engine to calculate an insurance reimbursement score for a future planned health service event based at least in part on a comparison to a plurality of calculated insurance reimbursement scores.
1 . A method for predicting an insurance-related event, the method comprising:
ingesting healthcare data of a patient received from one of a plurality of patient data providers and physician data relating to the patient from insurance records;
enriching at least one new data element of the ingested healthcare and physician data based on the determined one or more relationships among the ingested healthcare and physician data;
transmitting the at least one new data element to a machine learning module; and
using the machine learning module to predict a future insurance-related event relating to the patient.
2 . The method of claim 1 , wherein the healthcare data derives from an electronic medical record.
3 . The method of claim 1 , wherein the healthcare data derives from a pharmacy database.
4 . The method of claim 1 , wherein the healthcare data derives from a laboratory database.
5 . The method of claim 1 , wherein the healthcare data derives from an insurer database.
6 . The method of claim 1 , wherein the healthcare data derives from a physician's database.
7 . The method of claim 1 , wherein the machine learning module is configured to train a machine learned model that is leveraged by a test management system.
8 . The method of claim 1 , wherein the machine learning module is configured to train a machine learned model that is leveraged by a prescription monitoring system.
9 . The method of claim 1 , wherein the machine learning module is configured to train a machine learned neural network model.
10 . The method of claim 9 , wherein the machine learned neural network model is a recurrent neural network model.
11 . The method of claim 1 , wherein the machine learning module is configured to train a Bayesian model.
12 . The method of claim 1 , wherein the machine learning module is configured to train an artificial intelligence system.
13 . The method of claim 1 , wherein the machine learning module is configured to train a rules-based recommendation system.
14 . The method of claim 13 , wherein the rules-based recommendation system includes rules for determining the appropriateness of a treatment.
15 . The method of claim 14 , wherein the treatment is a prescription medication.
16 . The method of claim 13 , wherein the configuration of the machine learning module to train a rules-based recommendation system includes using training data from a prescription medication data set.
17 . The method of claim 13 , wherein the configuration of the machine learning module to train a rules-based recommendation system includes using training data from a prescription medication data set.
18 . The method of claim 13 , wherein the future insurance-related event is a reimbursement event.
19 . The method of claim 13 , wherein the reimbursement event is a reimbursement denial.
20 . A method for monitoring insurance billing events, the method comprising:
ingesting patient data received from one of a plurality of patient data providers and healthcare services data relating to the patient data;
ingesting data relating to insurance reimbursement criteria and insurance reimbursement records relating to the healthcare services data;
determining one or more relationships between the ingested patient data, healthcare services data, insurance reimbursement criteria and insurance reimbursement records, and data and previously ingested patient data, healthcare services data, insurance reimbursement criteria and insurance reimbursement records wherein at least one new enriched data set is created based on the determined one or more relationships;
transmitting the enriched data set to an analytic engine;
using the analytic engine to calculate an insurance reimbursement score, wherein the insurance reimbursement score is based at least in part on an association between the healthcare services data and insurance reimbursement records; and
using the analytic engine to calculate an insurance reimbursement score for a future planned health service event based at least in part on a comparison to the plurality of calculated insurance reimbursement scores.