How I built insurance card scanning into a doctor search app using Firebase OCR
Building useful features into mobile apps is always a challenge, but it's also rewarding! Recently, I worked on a doctor search and booking app where one of the key requirements was to simplify the process of inputting insurance information. Users needed a fast, easy way to submit their insurance details, so I decided to implement an insurance card scanning feature using Firebase’s Optical Character Recognition (OCR) technology!
Why Insurance Card Scanning?
In many apps, including those in the healthcare space, asking users to manually input data like their insurance carrier and plan information can be tedious and error-prone. By allowing users to simply take a photo of their insurance card, I could make the process more streamlined and user-friendly.
Why I Chose Firebase OCR
Google’s Firebase provides a powerful machine learning service for text recognition, perfect for this type of feature. I was excited to learn that Firebase offers two options for recognizing text in photos:
- Free version: Runs directly on the device, but increases the app's size by including necessary libraries.
- Paid version (after the first 1,000 requests): Uses Firebase’s remote servers for text processing, reducing app size but adding ongoing costs.
This flexibility allowed me to choose a solution that fit the needs of the app, balancing performance and cost-efficiency. While implementing this was new to me—especially since I had never set up a Firebase backend before—I found Firebase to be quite intuitive and user-friendly.
The Steps to Implementation
Here’s how I integrated the insurance card scanning feature into the app:
- Install the Firebase SDK for React Native: Firebase has great documentation on setting this up for mobile apps. You can check it out here.
- Add a camera/photo button to the search screen: Users can click this to capture or upload a photo of their insurance card.
- Open the image picker: On button click, the app opens the camera or lets the user choose an image from their gallery.
- Send the image to Firebase OCR: Once the user selects an image, it’s sent through Firebase’s text recognition service.
- Process the recognized text: I built new API endpoints to handle the recognized text, which matches it with known insurance carriers and plans stored in our database.
- Set the insurance carrier and plan automatically: The recognized carrier and plan are populated in the appropriate text fields, saving users time and reducing errors.
Overcoming Challenges
One of the challenges I faced was deciding whether to use the free on-device text recognition or the paid remote version. For apps with a lot of users, the free version increases the app size, which can be a downside, especially for those with limited device storage. However, the paid version does have ongoing costs, which is something developers need to factor in, especially if your app is processing a high volume of images.
Another consideration is privacy and security. When handling sensitive information like insurance details, it’s crucial to ensure that user data is processed securely. Firebase does offer robust security features, but it’s important to be aware of these as you build out similar features in your own apps. Learn more about Firebase security here.
The Results
The outcome was exactly what I hoped for! The insurance card scanning feature worked smoothly, providing a seamless experience for users. Now, instead of manually entering their insurance details, users can simply snap a picture, and the app does the rest.
Incorporating Firebase’s OCR technology not only improved the app's functionality but also gave me a valuable learning experience with machine learning tools in mobile development. I highly recommend checking out Firebase ML Kit for text recognition and other machine learning tasks—it’s a powerful tool that can be adapted to various use cases.
What’s Next?
I’m always looking for ways to enhance the app and make it more user-friendly. In the future, I’d like to explore adding more automation features or even integrating additional AI-powered services to further improve the user experience.
Have you used Firebase OCR in any of your projects? What other machine learning tools have you found useful? I’d love to hear your thoughts and experiences in the comments below!
⁉️ Let me know what y’all think!
