Systems and methods for inaccuracy detection and prevention within prescription information

A system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed, facilitate:
• receiving, from a prescription generation system, prescriber prescription information indicating an electronically transmitted medical prescription associated with a patient in a first data format;
• parsing the electronically transmitted prescription information from the first data format of the electronically transmitted medical prescription into a second data format of a plurality of hierarchical prescription entries, wherein the electronically transmitted prescription information is parsed into subsets of electronically transmitted prescription information for conversion into the second data format, wherein parsing the electronically transmitted prescription information from the first data format into the second data format is based on using a natural language processing (NLP) machine learning algorithm;
• determining whether the plurality of hierarchical prescription entries includes one or more dispensing inaccuracies based on using the plurality of hierarchical prescription entries, the prescriber prescription information, and one or more dispensing parameters indicating a ranking associated with the plurality of hierarchical prescription entries;
• based on determining the one or more dispensing inaccuracies within the plurality of hierarchical prescription entries, providing, to a prescription provider system, provider prescription information indicating the converted electronically transmitted prescription information and an alert indicating the one or more dispensing inaccuracies;
• receiving, from the prescription provider system, feedback information associated with the dispensing one or more inaccuracies;
• using the feedback information to train and/or update a machine learning model by providing confidence thresholds for the alert;
• and
• inputting a plurality of new hierarchical prescription entries into the machine learning model to generate data indicating one or more new dispensing inaccuracies.

A non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed, facilitate:

• receiving, from a prescription generation system, prescriber prescription information indicating an electronically transmitted medical prescription associated with a patient in a first data format;
• parsing the electronically transmitted prescription information from the first data format of the electronically transmitted medical prescription into a second data format of a plurality of hierarchical prescription entries, wherein the electronically transmitted prescription information is parsed into subsets of electronically transmitted prescription information for conversion into the second data format, wherein parsing the electronically transmitted prescription information from the first data format into the second data format is based on using a natural language processing (NLP) machine learning algorithm;
• determining whether the plurality of hierarchical prescription entries includes one or more dispensing inaccuracies based on using the plurality of hierarchical prescription entries, the prescriber prescription information, and one or more dispensing parameters indicating a ranking associated with the plurality of hierarchical prescription entries;
• based on determining the one or more dispensing inaccuracies within the plurality of hierarchical prescription entries, providing, to a prescription provider system, provider prescription information indicating the converted electronically transmitted prescription information and an alert indicating the one or more dispensing inaccuracies;
• receiving, from the prescription provider system, feedback information associated with the one or more dispensing inaccuracies;
• using the feedback information to train and/or update a machine learning model by providing confidence thresholds for the alert;
• and
• inputting a plurality of new hierarchical prescription entries into the machine learning model to generate data indicating one or more new dispensing inaccuracies.