Healthcare

Transform unstructured text into actionable insights using state-of-the-art machine learning and AI models.

Healthcare document processing with IDP, RPA, ML, and NLP

In healthcare, every day brings more forms, reports, and clinical notes. If your teams still key data by hand, you feel it in claim delays, denials, and stressed staff. Our healthcare document processing solutions use Intelligent Document Processing (IDP), Robotic Process Automation (RPA), Machine Learning (ML), and Natural Language Processing (NLP) to turn unstructured documents into clean, usable data that flows into your clinical and revenue systems.

Learn how this connects with our medical records scanning and digitization services on the Medical Records Scanning and Digitization page.

Key healthcare documents we help you automate

Patient intake and registration

Clinical documentation and lab results

  • Patient intake forms

  • Demographic and insurance forms

  • ID images and supporting documents

How we process them:

  • IDP captures patient demographics, policy numbers, and contact details.

  • NLP reads handwritten notes and symptoms when they appear.

  • RPA updates your EHR or practice management system so staff do not re key data.

  • Clinical notes and doctor notes

  • Lab reports and test results

  • Radiology reports and imaging notes

  • Discharge summaries and referral letters

How we process them:

  • NLP extracts diagnoses, medications, and key clinical terms.

  • ML improves recognition of clinical language over time.

  • IDP converts scanned notes into searchable digital documents.

  • RPA routes reports to the right provider or team and updates patient records.

You can see more about how AI supports these use cases in our article
From Manual to Intelligent: How AI is Transforming Healthcare, Insurance, and Mortgage Processing.

Claims, billing, and revenue cycle

Patient and quality feedback

  • Medical claims and prior authorization packets

  • Explanation of Benefits (EOBs)

  • Billing statements and invoices

  • ICD and CPT coding sheets

How we process them:

  • IDP extracts policy numbers, diagnosis codes, and procedure codes.

  • RPA submits claims, tracks status, and posts updates into billing systems.

  • ML flags coding errors, missing values, or potential fraud.

  • NLP reviews denial notes and reason codes to support appeal workflows.

For a deeper look at financial impact, review healthcare examples in our
Case Studies section, including Sutter Health.

  • Patient feedback forms

  • Satisfaction surveys

  • Complaint letters and comments

How we process them:

  • NLP analyzes sentiment and themes across responses.

  • ML groups feedback by location, service line, or issue type.

  • RPA routes urgent or negative feedback directly to quality teams.

Example workflow: Healthcare claims processing

Here is how a typical medical claim flows through our healthcare document processing solution:

  1. A claim packet arrives as paper, fax, or digital upload.

  2. IDP classifies the documents and extracts patient, provider, and code data.

  3. NLP reads notes related to treatment or denial reasons.

  4. ML checks for coding issues, missing information, or fraud risk.

  5. RPA enters the claim into the payer or clearinghouse portal and updates your EHR or billing system.

The result is fewer manual touches, fewer denials based on simple errors, and faster reimbursement.

Why healthcare providers choose OMG

Healthcare clients partner with Onsite Management Group to:

  • Reduce manual data entry in registration, coding, and billing

  • Improve claim accuracy and speed reimbursement

  • Support clinicians with cleaner, easier to find documentation

  • Protect PHI with secure, compliant digital workflows

To see how this works in a real healthcare network, explore our
Sutter Health Digital Mail case study.

If you prefer a quick conversation, you can also schedule time with our team on the
Contact Us page.