DETECTION AND MODELING OF DRUG DISPENSING BEHAVIORS BY HEALTHCARE PROVIDERS
Systems and methods are provided for determining drug dispensing consistency based on using patient data from at least one healthcare provider that includes at least one of drug toxicology data, metabolite data, patient-reported symptoms, and patient prescriptions, computing relationships among the data, transmitting an enriched data set to a machine learning module, and using the machine learning module to analyze the enriched data set to determine if a patient metabolite reported in a toxicology test is consistent with a known patient prescription.
1 . A method for determining drug dispensing consistency, the method comprising:
ingesting patient data from at least one patient data provider, wherein the patient data includes at least one of drug toxicology data, metabolite data, patient-reported symptoms, and patient prescriptions;
enriching a data set by computing one or more relationships between the ingested patient data and previously ingested patient population data that includes at least one of drug toxicology data, metabolite data, patient-reported symptoms, and patient prescriptions, wherein at least one new enriched data element is created based on the determined one or more relationships;
transmitting the enriched data set to a machine learning module; and
using a machine learning module to analyze the enriched data set to determine if a patient metabolite reported in a toxicology test is consistent with a known patient prescription.
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 configuration of the machine learning module to train a rules-based recommendation system includes using training data from a prescription medication metabolization data set.
19 . The method of claim 13 , wherein the prescription medication metabolization data set includes time-series data on prescription drug metabolism over a specified time period.
20 . A method for determining drug dispensing consistency, the method comprising:
ingesting, by a computing device, patient data from at least one patient data provider, wherein the patient data includes at least one of drug toxicology data, metabolite data, patient-reported symptoms, and patient prescriptions;
determining, by the computing device, one or more relationships between the ingested patient data and previously ingested patient population data that includes at least one of drug toxicology data, metabolite data, patient-reported symptoms, and patient prescriptions, wherein at least one new enriched data element is created based on the determined one or more relationships;
transmitting the at least one new data element to a raw data cluster;
storing the determined one or more relationships in a data store;
using a machine learning module to analyze data within the data store to determine the if detected metabolites are indicative of a potential adverse patient reaction; and
transmitting an alert to the at least one healthcare provider indicating the predicted potential adverse patient reaction.