Healthcare Form Processing NLP/OCR
AI/NLP Form processing is the use of NLP to parse and validate the contents of a form for processing, digitization, or sorting.
Most problems can be solved with a healthy mix of policy and technology. Unfortunately, in the absence of policy to require all forms be digital, paper forms are still the frequent norm in healthcare, and this problem must be solved with technology. Patients must sign consent forms, intake forms, authorization forms, and privacy policy forms on a regular basis. These forms must be processed in a HIPAA compliant method, and the volume of them makes manual review almost a non-option.
For this, we have automated healthcare form processing. This permits PDF and image forms to be automatically digested to create structured output and to validate if forms have been completed properly.
Analysis of a social security consent form processed by Tenasol
Categories of Healthcare Forms
There are 3 formats in which healthcare forms come in:
Wet-signed healthcare forms. These are forms that are completed on paper with pen or pencil. This form is scanned to create an image (jpg/png/pdf). This file then must be run through an OCR/NLP process or parsed by human.
Digital PDF healthcare forms: These are forms that are pdfs but have text entries within them. Sometimes they will feature signature boxes.
Fully digital healthcare forms: These are forms that fully digital and are rendered to a user as a UI. Usually they can either be exported as a JPG/PNG/PDF, but are most commonly exported in a structured JSON format. Some digital form suites, such as Lobbie, permit HL7 FHIR exporting and even integrate directly into electronic medical record systems. Fully digital medical forms do not require processing, unless only their image output is available.
Step 1: Healthcare Form OCR Processing
As an initial step, Tenasol makes use of optical character recognition (OCR) to detect what text appears where on a chart. In a more advanced step, key-value pairs are detected with use of convolutional neural networks. In this scenario, the key is the name of the field, best detected by the CNN, and the value is what was entered by the patient. This of course, may be nothing.
Step 2: Healthcare Form Detection and Validation
Next, a form must be validated that is of interest. In more detail:
The healthcare form and its version must be detected to validate the required pages are present.
Pages must be indexed. Pages may be out of order, or extra pages may be the document as well.
While minor this step is simply required for further processing.
Step 3: Field Detection and Validation
Individual fields must be analyzed to:
match each one to the expected form field
validate that each field is filled that is required
perform field validation for example standardization of date format
chain-validation where some fields are required in the event other fields are filled.
Step 4: Output
With all fields matched to their expected components, a JSON or table-based representation of the values may be generated for export to a database for storage and further processing.
Post-Processing of Healthcare Forms
After processing a form may be categorized as:
Complete, such that all required fields are detected, completed, and validated.
Incomplete, such as having fields that are not completed properly or pass validation. These forms may potentially be passed for human review or passed back to the patient for completion again.
Invalid, such as an image that is not the expected form. These forms may also potentially be passed for human review.
Forms that reach a complete stage may pass this data on to further automated processes if the proper checks are reached.
Conclusion
Automated healthcare form processing significantly improves efficiency, accuracy, and compliance in handling patient documentation. By leveraging OCR, NLP, and deep learning techniques, systems like Tenasol’s can digitize, validate, and categorize healthcare forms with minimal human intervention. This ensures that patient data is properly structured, reducing manual review efforts and potential errors. The classification of forms into complete, incomplete, or invalid allows for streamlined workflows, ensuring that only necessary documents undergo human review.
As healthcare increasingly embraces digital transformation, fully digital forms with HL7 FHIR integration offer a future where form processing is near-instantaneous. However, the persistence of wet-signed and digital PDF forms makes automated processing a critical bridge to full digitization. Ultimately, efficient form processing enhances patient experience, reduces administrative burden, and ensures compliance with regulatory standards like HIPAA. Organizations implementing these solutions will be better positioned to manage healthcare data securely and effectively in an evolving landscape.