Unlocking The Power of Conversational AI: A Look at GPT-4 in Azure HealthBot

Conversational AI is on the verge of revolutionizing healthcare, and OpenAI's latest language model, GPT-4, is leading the charge. When combined with Azure HealthBot, it has the potential to transform patient engagement and healthcare outcomes. But how can you integrate GPT-4 into Azure HealthBot, and what technical considerations do you need to keep in mind?

Benefits of Using GPT-4 in Healthcare Conversations

One of the biggest benefits of using GPT-4 in healthcare conversations is its advanced semantic understanding capabilities. It can identify and respond to a wider range of patient queries and concerns, leading to more accurate and effective communication. For example, GPT-4 can detect if a user is experiencing a mental health emergency, enabling healthcare providers to respond with more empathy and appropriate care, leading to greater trust and improved patient experience.

Using GPT-4 in Azure HealthBot is easier than you might think, lets see exactly how.

How to Use GPT-4 in Azure HealthBot

1. Create your OpenAI account

You can sign up for the OpenAI at https://platform.openai.com/signup/. You'll need an account and an API key. Which you'll find by clicking on your profile button at the top-right and clicking 'View API keys'. If you don't have one already generated, go ahead and make one, and put it somewhere safe.

2. Create your Data Connection in HealthBot

Underneath Integrations, you'll need to create a new Data Connection. Give it a name and a description, the Base URL should be https://api.openai.com/v1/chat/completions and you'll need two headers.

Content-Type: application/json
Authorization: Bearer {Your API Key}

Once created, you'll be ready to call this endpoint! There is one more step we can do to make using this data connection a little bit cleaner.

3. Create a GPT-4 Scenario

Lets make a new scenario that will encapsulate all of the logic related to consuming this GPT-4 data connection. Doing this will allow us to pass in only the users utterance, and get back just the response.

You'll need 3 nodes for a minimal version of this scenario. An action block, a data block and a return block.

The entry point will be the action block, this block will be used to parse any scenario arguments passed and set defaults. Here's an example of what that could look like.

const { 
    utterance, // The users utterance
    prompt, // a function that returns the payload
    temperature = 0
} = scenario.scenarioArgs;

scenario.GPTPayload = 
    prompt 
        ? prompt(utterance) 
        : {
            "model": "gpt-4",
            "temperature": 0,
            "messages": [
                {"role": "system", "content": "You are healthGPT. 
An assistant that will only help with appointments. You will not 
help with anything that is not strictly related to appointments"},
                {"role": "user", "content": "User Utterance: " + utterance}
            ],
        };

The second node will be the data connection block will use to connect to the OpenAI data connection we setup in step 2. To use this, you'll need to POST, and set a response variable name, along with passing it a payload.

Lastly, you'll want to end the scenario with an End Scenario block. Pass back the response so you can use it in whatever scenario you called this from.

{
  response: scenario.response
}

Some potential Use Cases

The potential use cases for GPT-4 in healthcare are limitless. By using clever prompt engineering, you can get GPT-4 to respond in a way that fits your needs. For example, you can use GPT-4 to determine if a user is attempting self-harm or in a medical emergency, enabling you to prevent giving more information to someone who needs to seek immediate help.

For Example, given this prompt:

You'll respond only with True or False. Determine whether the user
 in the following conversation is attempting to self-harm or is in
 an medical emergency.

You could largely expect GPT-4 to respond either True or False. Using this information, you could prevent giving more information to someone who needs to run to an emergency room or call a mental health emergency hotline.

In conclusion, the integration of GPT-4 into healthcare conversations through Azure HealthBot has the potential to enhance patient engagement and outcomes. Its advanced semantic understanding capabilities can enable healthcare providers to identify and respond to a wider range of patient queries and concerns, leading to more effective and personalized care. With the step-by-step guide outlined above, you can now begin to explore the possibilities of GPT-4 in your own healthcare conversations and create innovative solutions that benefit patients and providers alike.