NLP Data Collection Services: Structured Text for Smarter AI Solutions

NLP Data Collection Services

News & Trends

NLP Data Collection Services: Structured Text for Smarter AI Solutions

May 2025 | Source: News-Medical

How to Ensure Annotation Quality in Your AI Training Data

Natural Language Processing (NLP) is changing how healthcare organizations analyse clinical, diagnostic and patient data. For a healthcare organization to build quality AI models that are accurate and compliant, it must collect structured clean text data. Healthcare organizations work with a range of healthcare-related datasets (EHR records, medical transcriptions, clinical trial data, patient-reported outcomes etc.) that can enable intelligent systems to help decision making, automate activities and ultimately improve patient care.

With usable and domain specific curated text data, healthcare organizations can build great and reliable NLP applications capable of addressing their specific requirements.

The Case for NLP Data Collection in Healthcare

Healthcare is a unique and highly sensitive, complex, and unstructured environment. There is an abundance of valuable data locked in:

Doctor’s notes
Radiology reports
Discharge summaries
Clinical trial protocol
Transcripts of patient input and support

These datasets must be structured, domain-specific text datasets, and given annotations and preprocessing for machine learning (ML) models to understand and learn from them.

Our Healthcare NLP Data Collection Services Include

Clinical Text Data Collection
Clinical documentation, electronic health record (EHR) notes, lab results and case summaries are collected and de-identified to use as training data for diagnosis prediction, clinical decision support systems (CDSS) and automated medical coding.

Medical Terminology & Lexicon Development
Medical lexicons are created in consideration of the appropriate codes (e.g. ICD codes, SNOMED terms, medication information) that are required to facilitate processing entities, synonyms and context for NLP models.
Patient Voice and Sentiment Data Collection
We can collect patient feedback, symptom stories or post-clinic survey data that can be used to create models for sentiment analysis, mental health tracking, or training of chatbots.
Multilingual Collection for Medical Text
Cross-border use cases that require multilingual sources for health care text data (e.g. English, Spanish, Arabic, etc.) The medical data will be collected in compliance with regulations ii.e. HIPAA, GDPR, etc.

Annotated Medical Text for NLP purposes

We can supply annotated data sets to provide support for:

Named Entity Recognition (NER) – diseases, symptoms, medications, etc.
Intent Classification – that can be used with virtual health assistants.
Relationship Extraction – drugs, conditions, treatments.
Coreference Resolution – entity references across medical texts.

Quality, Compliance & Confidentiality

Human in the Loop Validation: Quality assurance for all datasets is provided through multi-staging, validation by extractions experts.

Compliant with HIPAA and GDPR: Our workflows are created and exist with privacy and data protections.

Customizable Pipelines: We alter our workflows, and data types or formats to comply with your unique healthcare NLP application.

Use Cases for Healthcare Applications We Support

Clinical decision support systems
Medical chatbots and virtual assistants
Automated coding and billing
Predictive analytics within clinical research
Public health monitoring and epidemiology

Collaborate with Statswork for Healthcare NLP

Build smarter healthcare AI with structurally rich text data. Statswork provides scalable, compliant solutions to collecting NLP data that create real clinical change.