June 23, 2021
Extracting the relevant data from insurance claims is a costly, time-consuming, and painstaking process for claims operations teams in insurance companies. This guide describes how to set up Impira AutoML solution for your insurance claims, so you can automate claims processing and save time.
In an insurance company, claims operations is a critical function. It serves as the central hub for intaking, processing, and paying out claims. For the insured customer, the claims department is often a frequent point of contact with an insurance company and is, therefore, a huge driver for overall customer satisfaction. According to an Accenture study, more than 30% of customers who endured a bad claims experience switched insurers within a year of the incident.
Today, the two most common insurance claim forms are CMS-1500 and UB-04. Both are quite similar; even though the UB-04 is based on the CMS-1500, it is actually a variant of it. CMS-1500 forms are used for non-institutional healthcare facilities (e.g., private practices), while UB-04 forms are generally used in institutional healthcare facilities, such as hospitals.
For many insurance companies, processing insurance claims is still a very manual process, whereby outsourced labor is manually reviewing and re-keying data from faxed, scanned, or emailed forms. Optical Character Recognition (OCR) technology is often used to facilitate that process. However, the results are often error-prone and still require correction by a human.
Impira AutoML helps alleviate errors from OCR technology by allowing users to directly train and provide feedback to their own unique models through an easy-to-use interface. In just a few clicks, Impira AutoML can be trained to learn your CMS-1500 or UB-04 form — and securely, too, on Impira’s HIPAA-compliant platform. With Impira AutoML, you can automate your insurance claims processing in less than five minutes, and keep your insured customers happy from start to finish.
Better than OCR technology alone, Impira AutoML reduces the amount of time and effort required to process insurance claims for claims operations teams. By empowering teams to work more efficiently, complex and high priority claims can be addressed with greater care, driving higher customer satisfaction, greater retention, and increased revenue.
Impira AutoML can be applied to a wide variety of insurance use cases across the enterprise. Follow the steps below to see how easy it is to implement Impira's powerful technology in making processing claims simpler and faster.
If you haven’t already done so, head on over to impira.com/signup to create your free account.
Once you’ve created your account, go ahead and create your first collection by clicking on the + symbol in the left-hand sidebar.
In the dialogue box that appears, give your collection a meaningful name of your choice, such as “Insurance Claims.”
Let’s upload some sample statements into your account. You can download them with the link from the left sidebar of this article. Unzip these files to use them to follow along with this tutorial.
1. First, click on the name of your newly created collection.
2. Drag and drop the files onto the page.
Tip: There are several other ways to upload files into Impira. You can also:
3. The files will immediately begin processing. When complete, you’ll see that each file has its own row in the table, as shown below.
Let’s begin extracting the relevant data from your claims. First, double-click on the first file to get started.
1. Let’s start by extracting the “Patient’s Name” field. Click on the patient’s full name and highlight the entire name.
2. On the right-hand sidebar, let’s call this field “Patient’s Name” in the “Name” field. For Type, select “Text extraction” and for Data, select “Text.” (“Type” refers to the action you want to take with your data. We can extract text from a field, extract a checkbox, manually input data, create our own function (expression), or join this field to another field from another document. The latter three types are ways to manipulate or connect data, whereas the first two types are ways to extract data using Impira’s AutoML. “Data” refers to the data type you want to extract, such as Text, Number, or Date.)
3. Click Add field.
4. Close this window by clicking X in the top-right corner, and you’ll see that we now have a new column in our table for “Patient’s Name.”
Notice the spinner in the column header. This indicates that the Impira AutoML is updating the model and is applying this learning to the remainder of the documents in your collection. In a few seconds, the “Patient’s Name” column has been updated with the extracted name from every CMS-1500 form.
Some cells may initially be blank, or have a colored flag displayed on the left side of the cell. This flag color is a visual confidence indicator of Impira AutoML’s prediction. A green marker indicates high confidence, and a red flag suggests that you review this prediction.
Because Impira AutoML is a continuously learning system, you can boost these confidence levels by confirming correct information or correcting misidentified information. These affirmations increase Impira AutoML’s accuracy by teaching it what you want it to do. The more it learns, the more you’ll see red flags turning green. Read more about how reviewing your predictions custom-tailors Impira to your needs.
Now, we can extract additional information from your claims by adding more fields. We can do this by repeating the above steps to add fields such as “Insured’s ID Number,” “Patient’s Address,” etc.
As you add these additional fields, the number of columns in your table will grow.
When you are satisfied with the results, you can easily export the data from Impira. To download the data as a CSV file, click the “Download” button in the upper right-hand corner. In the drop-down, select “All files records (CSV).”
When you’re satisfied with the results of your data extraction, you you have a few options for exporting your data:
Click Download in the top-right corner and select As CSV.
You can also access your data through an API, and you have the option of utilizing Impira Query Language (IQL) to query specific data, giving you as much control and flexibility you need over your data. This document will also show you how to utilize a Poll API.
And if you're feeling a bit more adventurous, you can always connect a webhook with Impira.