We have NLP solutions to transform your unstructured data from bench to bedside. To learn about specific NLP solutions for pharma, providers, payers, government and more – choose your industry above.

Drug Discovery

Drug Discovery

Understanding gene-disease associations, pathways and systems, is critical for drug discovery and basic research. Text mining can access the landscape of relevant information.

Patents Analytics

Patents hold huge volumes of valuable data, for novel chemicals, discoveries, competitor intelligence and more. Text analytics can unlock these valuable data.

Drug Development Safety and Pharmacovigilance

The growing cost of drug development is driving pharmaceutical companies to identify potential safety issues earlier in the drug development process.
Voice of the customer data

Voice of the Customer

Voice of the customer (VoC) call feeds can provide a rich source of real world data (RWD). Pharma and healthcare organizations need to understand how their customers (e.g. patients, clinicians) are responding to marketed drugs and gain a broader picture of patient reported outcomes

Precision Medicine

Precision medicine refers to the ability to tailor treatment to the most appropriate group of patients, either at the clinical level, or within drug discovery and development.
Scientists reviewing information

Real World Data Analytics

Linguamatics Natural Language Processing (NLP) extracts the key facts from structured and unstructured data, transforming real world data into insights for decision making.

Clinical Trial Analytics

Clinical trials are used to gather safety and efficacy data on new drugs in development, or existing drugs being tested for new indications.

Regulatory Compliance

The pharmaceutical industry is among the most heavily regulated in the world. Linguamatics NLP can bring time-saving benefits for regulatory compliance compared to manual efforts, which can be slow and expensive.

Clinical Research

Mining unstructured data to support medical research has been prevalent for many years. This requires analysis of Big Data sets and often includes manual chart review to identify patients and extract specific attributes.

Social Determinants of Health (SDoH)

To enhance population health analysis and identify the care needs of individuals, providers and payers must extract insights from data stored in unstructured text in EHRs.

Patient Safety

Patient safety is always a priority. Detecting early signs of clinical risk and disease gives us a greater chance of a successful outcome.

Quality Measures

The regulatory burdens of initiative quality measures continue to put a strain on overworked hospital departments, and payers alike.
Risk adjustment for payers

Risk Adjustment

Medicare advantage and commercial risk adjustment involves the review of clinical notes to identify diagnoses that are not captured in the discrete EHR field. Manual processes are time consuming and repetitive. NLP can do the heavy lifting for you.

Disease Registries

Better disease management may be obtained by adding vital information in the form of unstructured data to your disease registries.

Population Health Management and Analytics

To enhance population health analysis and identify the care needs of individuals, providers and payers must extract insights from data stored in unstructured text in EHRs.

Big Data Analytics in Healthcare

The term "big data" was coined in the early 2000s by industry analyst Doug Laney. In Laney's definition, the term referred to information existing in large data sets, with high update rates and in a variety of formats.
Case Studies

Success Stories

Case Study: Improving Care and Population Insights in Health Systems with NLP
Case study

Case Study: Improving Care and Population Insights in Health Systems with NLP

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