Rapid Care | World class leader in Health care | Medical Billing | Medical records | Medical Summarization

Discourse Observation

Understanding larger text structures across sentences or paragraphs and aiding in document classification, text summarization, and plays vital role in answering queries for users.

Identifying Fraudulent Claims

Combined with NLP techniques - our software can help detect fraud by analyzing medical claims, billing codes, and clinical notes for inconsistencies or anomalies that might indicate fraudulent activity.

Correlational study

Computational linguistics can help mine medical texts to identify trends, find correlations, or summarize large volumes of medical data, enabling users to identify all treatment options provided and doctor visits till claims.

Syntactic parsing

WHITE allows high level software to understand the structure of sentences by breaking down complex medical sentences into their core components, like subjects, verbs, objects, and modifiers. For medical summarization, this means identifying crucial parts of a sentence (e.g., patient condition, diagnosis, treatment), and ensuring they are preserved in the summary.

Named Entity Recognition

One of the fundamental tasks in medical summarization is identifying relevant entities such as diseases, medications, treatments, and medical procedures. WHITE uses computational linguistics to extract these terms, ensuring that the summary highlights all critical medical details.

Resolving Coreference

Resolving Pronouns and References: Coreference resolution helps ensure that pronouns and ambiguous references (e.g., "he," "she," "the patient") are correctly associated with the right entities in the text. In medical summarization, this is important because clinical documents often refer to patients and conditions repeatedly, and the summary must maintain clarity and avoid confusion.

Extractive Summarization

WHITE extracts key sentences or passages from the text based on medical relevance and given context as per the CLIENT. Using computational linguistics, extractive summarization can prioritize important facts like diagnoses, symptoms, and prescribed treatment.

NLP Classification

NLP models can classify different sections of text (e.g., diagnosis, treatment, medical history) to help generate more structured and well-organized summaries. For example, a model might classify a part of the document as "Diagnosis" and another as "Treatment Plan," ensuring that the summary presents information in an organized way.

Sentence Simplification

Medical texts can often be complex and difficult to understand. WHITE uses computational linguistics to reduce sentence complexity by breaking down long sentences, removing unnecessary jargon, and focusing on key information. This is especially important when summarizing for non-expert audiences, such as healthcare administrators.

Extract domain data

Extract critical information accessible to healthcare professionals for quick decision-making like patient conditions, treatments, outcomes and follow-up visits, etc.

Semantic Role Labeling

WHITE assigns roles to different parts of the sentence, such as the agent (who performs the action), the patient (who ? or what ? is affected by the what action ?), and the theme (what the action is about). This helps the summarization engine program to understand the relationship between medical entities, allowing for better information extraction.

Regulatory Compliance

Assist in ensuring all documentation complies with regulations like HIPAA (Health Insurance Portability and Accountability Act), helping to automatically flag non-compliant content

Essential Advantages of WHITE's Linguistics Parsing