SIMULATING AND MONITORING CONSISTENCY IN A DRUG PRESCRIPTION SYSTEM
Systems and methods are provided for monitoring consistency in a drug prescription system using prescription drug data and prescription drug treatment plan data relating to a plurality of patients received from a plurality of patient data providers, and using a machine learning module to compare treatment plans among the prescription drug treatment plan data, using simulations of one of one or more drugs and one or more treatment plans, among the prescription drug data and prescription drug treatment plan data.
1 . A method for monitoring consistency in a drug prescription system, comprising:
ingesting prescription drug data and prescription drug treatment plan data relating to a plurality of patients received from at least one of a plurality of patient data providers;
determining, by the computing device, one or more relationships between the ingested prescription drug data and prescription drug treatment plan data relating to a plurality of patients and previously ingested prescription drug data and prescription drug treatment plan data, wherein at least one new enriched data set is created based on the determined one or more relationships;
transmitting the enriched data set to a machine learning module; and
using the machine learning module to compare treatment plans among the prescription drug treatment plan data, using simulations of one of one or more drugs and one or more treatment plans, among the prescription drug data and prescription drug treatment plan data.
2 . The method of claim 1 , wherein the prescription drug data derives from an electronic medical record.
3 . The method of claim 1 , wherein the prescription drug data derives from a pharmacy database.
4 . The method of claim 1 , wherein the prescription drug data derives from a laboratory database.
5 . The method of claim 1 , wherein the drug treatment plan derives from an insurer database.
6 . The method of claim 1 , wherein the drug treatment plan 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 . A system for monitoring consistency in a drug prescription system, comprising:
a processor configured at least to: identify a requested laboratory report; associate the laboratory report with a prescription drug management program; identify one or more laboratory result data; trigger a drug consistency awareness service corresponding to the prescription drug management program; send the one or more laboratory result data to a destination corresponding to the laboratory report; and send one or more parameters associated with the drug consistency awareness service to the destination corresponding to the laboratory report;
a reporting module for intelligent drug consistency reporting comprising a lab data collection module that integrates patient drug toxicology data, user reported symptoms, and patient prescriptions;
a consistency module that applies a set of rules and algorithms to determine the if the metabolites of the toxicology test are consistent with the known patient prescription; and
an interaction module that analyzes the detected metabolites to see if they indicate a potential adverse reaction; and
a recommendation module that provides the physician with an indicated likelihood that the patient is abusing and a risk report for the physician to work with the patient.
19 . The method of claim 18 , wherein the risk report includes a recommended treatment plan.
20 . The method of claim 18 , wherein the recommended treatment plan includes a recommended prescription medication.