Minimize Rx Error Using OPEN AI

This post has been republished via RSS; it originally appeared at: Microsoft Tech Community - Latest Blogs - .

Introduction:

Rx (Prescription drugs) error is one of the most common and significant errors in the healthcare industry every year. Even though the healthcare industry is heavily regulated in the United States, these issues still occur due to glitches in the system. This post examines the

1. Prescribing Error: Prescribing incorrect or inappropriate medications, dosages, routes, or frequencies to patients.

2. Dispensing Error: Occur when a pharmacist fills a prescription with the wrong medication or dosage, or when the pharmacist provides incorrect instructions or labels on a prescription.

3.Administrative Errors: These errors occur when a nurse, doctor, or similar healthcare worker gives a patient wrong medication or dosage, or uses an incorrect technique

Prescribing Error in Detail:

There are many reasons for writing the wrong drug name, such as confusing two drugs with the same name (e.g., Celebrex and Celexa) or using ambiguous abbreviations (e.g., SSKI for a saturated solution of potassium iodide).
Using the wrong units (e.g., mcg instead of mg) or writing the wrong dose (e.g., calculating the dose based on the patient's weight, age, or renal function) is one of the most common mistakes people make while writing prescriptions.
Providing the wrong frequency, such as omitting or delaying a prescription or giving a drug too much or too little, can lead to health issues.
Before prescribing a drug, the doctor must review the patient's medical history, allergies, current medications, and laboratory results.
Insufficient communication regarding the prescription between the prescribing provider and other health care providers

Dispensing Error Details:

Incorrectly dispensing drugs, doses, dosage forms, or quantities of drugs
Mislabeled containers with incorrect instructions on how to use them.
The dispensing of expired medications
Choosing the wrong patient for treatment.
The improper, incorrect, or inadequate preparation, packaging, or storage of medication before it is dispensing can lead to serious problems for patients.

Administrative Errors Details:

The wrong drug is being administered to the patient.
Incorrect dosage or strength being administered to a patient.
There was a mistake in giving the drug to the wrong patient.
Failure to follow prescription instructions or local protocols correctly.

How Machine Learning /Open AI can eliminate errors:

Here specifically we will concentrate on how these errors can be eliminated using Open AI /ML

Manually some of the Rx error could be eliminate by using proper protocols manually such as following a proper error check procedures. But the disadvantage of this it would take lot of time to and still it will not 100% guaranteed .

Using ML /Open AI this process could be faster and much easier.

Rx Error could be found out in three ways:

1.Before it Happens

In most cases, this occurs when an error is detected before execution. For example, a patient sees a doctor and the doctor prescribes a medication that doesn't meet the symptoms of the patient, or the doctor forgot to check the medical test reports, precautions, or any relevant restrictions that the patient must follow. Open AI will suggest the steps a doctor can take or recommend how to resolve the issues.

2.While it Happens

"While it happens" indicates the next steps after "Before Happening" for example a physician writes a prescription, and it is altered somehow at the pharmacy. Or maybe the pharmacy dispenses an incorrect medication or mixes it with another similar name type. AI will help determine this and send an alert to the pharmacy.

3. After it Happens

In the final stage, after passing through the checkpoints for "Before it Happens", "While it Happens", and "After It Happens", it goes for "After it Happens". In order to make sure the right prescription is being given to the right patient without making any mistakes, this is the final check.

How Azure Synapse can help along with Open API for RX Error

Data Preparation: Prepare and transform data for analysis using Azure Synapse. Cleaning and standardizing prescription data, integrating patient medical records with prescription data, and generating machine learning features can all be part of this process.
Data Analysis: Using Azure Synapse, exploratory data analysis can be performed on prescription data, such as identifying patterns in prescription errors and potential drug interactions.
Machine Learning: Using Azure Synapse, machine learning models can be trained on prescription data. NLP tools such as Open Ai’s GPT-3 model can be used to detect potential RX errors or drug interactions in patient medical records.
Real-Time Analytics: Healthcare providers can identify and respond to potential RX errors using Azure Synapse's real-time analytics on prescription data.
Data Governance: Azure Synapse can ensure data governance and compliance with healthcare regulations by providing security features and access controls for sensitive prescription data.

Training the Model:

#6 in the below figure

In order to train the GPT-3 model with Azure Machine Learning, several steps must be taken. Here is a general overview.

Create an Azure account, set up an Azure Machine Learning workspace, and install the necessary SDKs and libraries.
Prepare the dataset for training by loading it into an Azure datastore, preprocessing it, and splitting it into training and validation sets.
Configure the GPT-3 model architecture and hyperparameters in Azure Machine Learning.
Using Azure Machine Learning, train the GPT-3 model on the preprocessed dataset. In this step, the training script is defined, and the job is submitted to an Azure computer cluster.
Use Azure Machine Learning to evaluate the trained model on a held-out validation set.
The trained model can be deployed as a service using Azure Machine Learning. Creating an Azure Container Instance and deploying the model as a web service API are the steps involved.

Open AI Activities:

Natural Language Processing (NLP): Open AI's advanced NLP algorithms can identify potential medication errors and drug interactions in patient medical records.
The conversational AI technology in Open AI can be used to develop chatbots to assist patients in understanding their medications and how to take them. Chatbots can also answer drug interactions and side effects.
Azure services can be used to develop electronic prescribing systems that eliminate errors associated with paper prescriptions.