Nirveda Cognition Reviews

PrecisionOCR is an easy-to-use, secure and HIPAA-compliant cloud-based optical character recognition (OCR) platform that organizations and providers can user to extract medical meaning from unstructured health care documents. Our OCR tooling leverages machine learning (ML) and natural language processing (NLP) to power semi-automatic and automated transformations of source material, such as pdfs and images, into structured data records. These records integrate seamlessly with EMR data using the HL7s FHIR standards to make the data searchable and centralized alongside other patient health information. Our health OCR technology can be accessed directly in a simple web-UI or the tooling can be used via integrations with API and CLI support on our open healthcare platform. We partner directly with PrecisionOCR customers to build and maintain custom OCR report extractors, which intelligently look for the most critical health data points in your health documents to cut through the noise that comes with pages of health information. PrecisionOCR is also the only self-service capable health OCR tool, allowing teams to easily test the technology for their task workflows.

Machine learning can provide insightful text analysis that extracts, analyses, and stores text. AutoML allows you to create high-quality custom machine learning models without writing a single line. Natural Language API allows you to apply natural language understanding (NLU). To identify and label fields in a document, such as emails and chats, use entity analysis. Next, perform sentiment analysis to understand customer opinions and find UX and product insights. Natural Language with speech to text API extracts insights form audio. Vision API provides optical character recognition (OCR), which can be used to scan scanned documents. Translation API can understand sentiments in multiple languages. You can use custom entity extraction to identify domain-specific entities in documents. Many of these entities don't appear within standard language models. This allows you to save time and money by not having to do manual analysis. You can create your own machine learning custom models that can classify, extract and detect sentiment.