The client is a leading US-based healthcare plan with a workforce of over 285,000 professionals and a presence in more than 130 countries. As a diversified healthcare organization, the client aims to create a health care system that is connected, aligned and affordable for all stakeholders.
The volume of complex documents flooding the client’s mailroom was growing rapidly. Optimizing mailroom and data capture operations was a top priority for the client and its provider customers. The client turned to Firstsource for an automated solution that would enable faster, prioritized turnaround of urgent documents while ensuring greater accuracy and reducing costs.
Inefficient mail center operations that require human operators to review thousands of keywords can make it hard for insurers to identify urgent documents. Each month, the client’s mailroom received a large volume of documents in both digital and physical formats. As a result, it took the client 12 hours to process faxed medical records and 24 hours to process appeals and grievances. This mattered enormously to patients waiting for treatment.
Firstsource leveraged automation and natural language processing (NLP) techniques to deliver on the client’s objectives:
- All documents are scanned leveraging OCR and ICR (Intelligent Character recognition) technologies – an activity that now takes place in minutes instead of hours, enabling faster downstream processes
- A digital workflow splits, sorts, categorizes and indexes all documents for further processing
- An intelligent machine learning solution identifies relevant keywords, keyword combinations, and keyword exceptions in order to segregate urgent documents quickly
- Once the system identifies an urgent appeal or grievance, it is pushed into a separate processing queue for priority processing. If the system is unsure whether a document is urgent or not, the document goes to human operators for exception handling.