Healthcare and life science organizations face the challenge of filtering ever‐increasing volumes of text information. Linguamatics NLP platform allows organizations to gain actionable insights from this text for key decision‐making. Find hidden insights and connections to boost innovation, improve patient outcomes, increase efficiency and reduce costs.
Discover our main solutions below
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 hold huge volumes of valuable data, for novel chemicals, discoveries, competitor intelligence and more. Text analytics can unlock these valuable data.
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 (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 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.
Linguamatics Natural Language Processing (NLP) extracts the key facts from structured and unstructured data, transforming real world data into actionable intelligence for decision making.
Clinical trials are used to gather safety and efficacy data on new drugs in development, or existing drugs being tested for new indications.
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.
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.
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 is always a priority. Detecting early signs of clinical risk and disease gives us a greater chance of a successful outcome.
The regulatory burdens of initiative quality measures continue to put a strain on overworked hospital departments, and payers alike.
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.
Better disease management may be obtained by adding vital information in the form of unstructured data to your disease registries.
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.
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.
Our products have an exceptional combination of flexibility, scalability and data transformation power to effectively address the challenges of analyzing unstructured data, underpinned by Linguamatics NLP platform. We have applications for end-users, informaticians and pro-users, plus tools for automation, integration and customization.