Text Extraction

Our software WHITE can identify important medical sentences based on diseases, medications, symptoms, procedures etc from unstructured clinical texts. This helps in extracting key chronologies and using them for building summaries or creating structured data from unstructured notes. Example: Extracting key terms like "hypertension," "insulin," "chronic pain," and "surgery" from medical reports and writing text descriptions into an automated summary.

Extract key information from unstructured clinical texts like doctor's notes, patient records, discharge summaries, etc. Examples: Patient history, diagnosis, treatment plans, medication lists and lab results.

WHITE converts clinical terms into standardized codes (such as ICD-10, CPT) to ensure consistency and accuracy for billing and insurance workflow platforms by automatically assigning diagnostic and procedural codes from patient records.

Document Processing

WHITE is capable of advanced document processing offering healthcare providers to quickly retrieve and process patient records, clinical notes, and medical histories from electronic health records (EHRs), without needing to manually search through paper files.

Essential data from medical documents can be automatically extracted, reducing the time healthcare providers spend entering information manually and reducing human error.

Document processing tools can seamlessly integrate with EHR systems, ensuring that all relevant medical information is updated in real-time across all platforms. This leads to more accurate and comprehensive patient records.

CTRank for Medical Records

WHITE incorporates CTRank - A graph-based algorithm used for text summarization, especially for extracting key phrases and sentences from documents. It's particularly useful in contexts where summarization needs to be done from large text datasets, like medical records. It identifies the most important sentences or phrases from a document based on their relationships with other sentences or phrases.

CTRank plays a crucial role by identifying important details from long, unstructured medical texts (such as physician’s notes, discharge summaries, clinical reports, and research articles) and creating concise, meaningful summaries that are easier for healthcare providers to review.