etherFAX Unlocks Structured Data and Eliminates Information Silos with its Artificial Intelligence Solution for Automated Data Extraction

etherFAX announced an artificial intelligence (AI) solution that facilitates advanced capabilities of searchable PDF, OCR, and other Key Value Pairs. These new capabilities are ideal for healthcare and organizations that need to extract data from a broad range of applications and systems. Using Microsoft’s Cognitive Services, etherFAX AI extracts and digitizes data from a range of unstructured documents and forms to eliminate information silos and dramatically improve processes and workflows.

Today, clinical care teams and healthcare administrative teams still spend a considerable amount of time typing in, clicking through, and editing electronic health records. Manually keying in patient data into fields is not only time-consuming and inefficient, but also can be inaccurate and unreliable.

etherFAX AI reduces error rates associated with manual data entry by extracting information that is stored in unstructured document types, such as PDFs and paper-based forms. The solution digitizes data that can be searchable and ready to be integrated into workflows and applications, such as EMRs. To improve interoperability and reduce information silos, form recognition allows users to easily incorporate data into third-party workflows and share information across platforms.

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“Healthcare providers must be able to share, access, and analyze data fast and accurately,” said Paul Banco, CEO and co-founder of etherFAX. “Automated data extraction transforms content locked in unstructured formats into usable, structured information. For healthcare organizations specifically, etherFAX AI ensures less time is spent on manually entering and searching for information, helping to deliver a quality patient care experience, process claims faster, and receive timely payments.”

etherFAX’s AI solution for document data extraction can be used with multiple formats including JPG, PNG, PDF, and TIFF, while results can be extracted into JSON or XML formats. Extracted data can be mapped to third-party systems, allowing tasks such as indexing patient records, scheduling and referrals to be automated. As staff no longer has to spend valuable time unlocking unstructured data trapped in form images, they can focus on more value-added items and care initiatives to improve patient health outcomes.

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