A Brief Guide to AI Code Assistants for Health Insurers

Futurists predicted AI assistants would revolutionize software development, and that future is now. You can unlock significant advantages by leveraging AI code assistants, from boosting developer productivity to accelerating time-to-market for your projects. But navigating this new landscape requires a strategic approach. 

Read on to learn the best practices health insurers should use to ensure you reap the full benefits of AI code assistants while addressing potential concerns like data security and accuracy.

What are AI Code Assistants

Developers use AI code assistants to generate, debug, and review code. Additionally, they provide real-time interactions, including code suggestions, corrections, and auto-completions.

Code assistants can help developers of all levels. Assistants can help novice developers or those new to your organization better understand your company’s code, improving onboarding and ensuring novice developers reach full productivity faster. For veteran developers, it can serve to speed up their work. For example, auto-complete functionality not only limits keystrokes but can also limit formatting errors by correcting code inline, improving code quality.

At their core, AI code assistants streamline the development process and can help developers focus more on innovation while the code assistant handles fundamentals.

Most AI code assistants integrate with popular integrated development environments (IDE), making them easy to set up and run.

Common Features

Here are some features you’ll experience in AI code assistants:

Code Context

Most health insurers have specific coding practices, conventions, styles, and processes. AI assistants must understand an organization’s unique code and environment to provide the most value.

That’s where code context comes in. Understanding code context improves the output an AI assistant may provide. As we have experienced with our premium billing solution, bad data sent into the billing platform often leads to inaccurate invoices. The same applies to code assistants. If the assistant doesn’t understand the context of the question or supplied code, its response will be of lesser quality.

Code Optimization

Code optimization can take many forms, but ultimately the assistant examines your code and suggests ways to optimize it. That may be by identifying and simplifying redundant code or analyzing and improving queries and algorithms. 


Surveys show developers spend as much as 50% of their time debugging the code they’ve created. AI assistants can help detect issues and offer suggestions, speeding the process as developers don’t have to rely on others on the team or the Internet to solve their problems. Reducing debugging time can help organizations deliver solutions faster.


Refactoring helps restructure code without modifying its behavior. Developers often refactor code to make it easier to read, reduce complexity, and improve extensibility. Ultimately, refactoring makes code easier to maintain. 

Often, refactoring requires vast knowledge of the entire codebase, architecture, and data models. AI assistants can improve refactoring by quickly reading and understanding your codebase. They offer both minute and large-scale refactoring opportunities.


Many insurers have large teams working on large codebases. Improving developer productivity starts with good code documentation. Documentation also lowers the likelihood of mistakes and can make debugging easier.

However, creating documentation is typically not a task developers enjoy. You’ll get better documentation by leaning on an AI assistant.


Google began testing search auto-complete in 2004, widely rolling it out four years later. Leveraging their search data to improve the user experience by suggesting common searches meant users spent less time typing and more time viewing results, leading to a better user experience.

Code auto-complete from AI assistants has the same impact. AI assistants can auto-complete code by reading common coding patterns in your codebase, improving productivity.


Software developers don’t code in a vacuum. They discuss with their peers, their network, and those on the Internet to get ideas and concepts to help them write great code. The ability to discuss ideas has made pair programming a popular approach.

With the massive improvements ChatGPT introduced to AI chat, having an AI chat assistant is in many ways better than pair programming in terms of developer productivity. Developers can chat in real-time with a knowledgeable coding partner.

Test Generation

Businesses know that generating unit tests helps improve the reliability of their development process while limiting the number of production bug fixes. However, developing those unit tests can be tedious.

AI code assistants can help health insurers generate unit tests quickly, saving time while generating more tests that improve reliability. AI-generated tests still need to be reviewed for accuracy, but AI assistants can reduce the time it takes to develop unit tests.

What are the benefits of AI Code Assistants?

Now that we’ve got a basic understanding of the core elements, let’s take a look at the benefits of AI code assistants for health insurers:

Time Savings

As you’ve read, almost all of the features highlighted previously can improve developer productivity. From faster debugging to quick documentation to code auto-completion, AI code assistants help developers get more done.

Better Developer Experience

Many organizations – including health insurers – have dragged their feet by introducing an enterprise-approve AI code assistant. That’s left developers to surreptitiously find and use a solution on their own. Why? Because AI code assistants make a developer’s job easier. It’s a better developer experience. 

Businesses that fail to adopt AI code assistants risk failing to capitalize on development productivity gains. But they also run the risk of high developer churn as code assistants improve the developer experience, improving many of the manual, menial tasks that many developers dislike.

Improved Code Quality

Introducing bugs to a production environment can have many impacts. Errors to an enrollment or member services portal may impact the client or member experience, while billing platform bugs may reduce revenue. Improved debugging procedures, real-time code reviews, better documentation, and more unit tests improve code quality. At the same time, they enhance the member/customer experience while maximizing revenue.

Faster Time-to-Market

Futurists have postulated that AI assistants could lead to increased competition as AI simplifies and speeds up software creation. By leveraging AI code assistants, you’ll improve developer productivity and time-to-market for products and services that rely on development resources.

AI Code Assistant Best Practices

Here are some tips to help you get started and get the most out of your AI code assistant:

Developer Training

If you’ve ever used ChatGPT or other AI assistants, you know accuracy is a concern. Health insurance must train its developers not only on how to use the tools but also to proceed with caution. Developers should review all AI-generated content. Testing and code review processes are still vital to code accuracy.

Consider Solutions that Won’t Leak Your Data

Most enterprise solutions won’t train the product they release to all their customers using your data. That’s important because you’re likely sharing proprietary code and data as part of your usage. You don’t want that data leaked. 

Also, AI models trained on vast amounts of data can become “black boxes,” making it difficult to understand how they arrive at their suggestions. This lack of transparency can be problematic when dealing with code, as errors can be introduced unintentionally. AI assistants trained on generic code avoid this issue and can offer more transparent suggestions.

Design a Proof of Concept and Measure Results

If you haven’t selected an AI code assistant, consider running a proof of concept with a subset of your development team on one or many vendor solutions. Start with a metric in mind – like increased productivity, improved quality, or faster time-to-market – and create a plan to measure that metric. 

AI Code Assistant Solutions

Here are some of the leading AI code assistant solutions:

  1. Amazon CodeWhisperer
  2. Codeium
  3. GitHub Copilot
  4. GitLab Duo
  5. Google Gemini Code Assist
  6. Tabnine

Certifi’s health insurance premium billing and payment solutions help healthcare payers improve billing accuracy while reducing administrative costs.


AI for Health Insurance: A Practical Handbook

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