June 23, 2021
This guide walks through how to set up an AutoML solution for medical forms processing, including OCR data extraction and export, software, people, and processes.
Inefficiencies in processing medical forms will, at best, frustrate patients while really hurting your bottom line. A healthy dose of AutoML might be just what the doctor ordered. In this article, we discuss how to use Impira to automate the processing of your medical forms, which can be implemented in less than five minutes.
Factors to consider with an AI/ML-assisted medical form processing solution include:
For your initial training, you might be using a system with a template that matches your forms, in which case the training of the ML model was done for you in advance. While this approach can save time up front, it may prove difficult to update should your form layout get altered, or the information you need to extract changes. Assuming you need to train the ML model, a traditional approach might require providing a data science team with a large number of training documents (up to hundreds or thousands) for you to use to train your model, and a similar number each time you want to re-train or even tweak said model. Impira takes a different approach, which not only lets you start seeing results right away, but also affords the flexibility needed to adapt to changing business requirements.
You’re in the business of keeping your patients healthy, but inefficiencies in processing medical records can affect both the care you can provide and your profitability. Impira offers you flexibility in how you ingest your files, direct access to train the AutoML extraction models, real-time application of updated models across all of your documents, and several ways to extract your data to incorporate into your medical form processing workflow. The result of incorporating Impira into your medical record processing workflow will allow you to process more forms in less time, eliminating manual data entry to free your staff up to better care for your patients.
Follow the steps below to walk through Impira’s HIPAA-compliant software-as-a-service (SaaS) technology:
Once you've created a free Impira account and logged in, start by creating a new collection, which is like a folder that holds files with the same layouts and data fields. For example, if you have multiple client intake forms from which you want to extract names, phone numbers, and addresses, group them in the same collection.
Create a collection by clicking on the + next to Collections in the left-hand sidebar and give it a name like "Medical forms."
There are many ways to get your files into Impira. You can:
With your files uploaded into the collection, you're ready to start extracting data from your forms.
Impira takes a unique approach to training which doesn’t require large numbers of training documents and allows you to train your model directly with just a handful of files. Start by double-clicking one of your medical forms to begin extracting data.
Double-click records-0.pdf file and highlight the value in the box under “Account #.”
Let’s name this field "Account Number" and leave it as the default type of Text Extraction (we’ll discuss the other types later), and for Data, we’ll leave this as "Text." Even though the Account Number value contains only numerals, you're unlikely to perform mathematical functions on the account number, and as a text value, any leading zeros will be preserved.
Once you’ve added the Account Number field, you’ve just trained your first ML model. You’ll see the panel on the right will list all fields associated with this collection (at this point there’s only one).
From here, you can go on and add more fields or you can close this window and see that Impira has applied its learnings across all the files in the collection by extracting the Account Number from each of them. Since you’ll need more than just an account number, let’s extract a few more fields and then check our other documents.
Highlight "Roberts" In the Patient name box, and create a field called "Patient Last Name."
Now, you’ll name "Patient First Name" (highlighting Barry), "SSN" (highlighting 864 - 37 - 5912), "Primary Insurer" (highlighting Pacific Care) and "Secondary Insurer" (highlighting Eastern Care). In practice, you would continue this for each value you want to extract — e.g., policy numbers, dates, address values, diagnoses — but, for the purpose of this instruction, let’s stop here.
Now that Impira has extracted all the data fields you want, we have the opportunity to fine tune Impira by reviewing its work (called "predictions").
Learn how to refine and improve Impira's ability to extract data from any new files in this collection by going through the review workflow.
Once you’ve uploaded your files and documents into a collection and worked with Impira to extract all the data you need, you’ll want to access the data in a format that is useful to you.
Click Download in the top-right corner of your browser window and select As CSV.
You can now import your data to your spreadsheet application or any other application you typically use to work with CSV files.
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.