Real world evidence data provides valuable insights into the drug development process. Being able to rapidly transform data sources into actionable evidence has positive consequences on patient health outcomes and is already helping top pharmaceutical companies to be more efficient in their drug development processes.
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Population health management places increasing emphasis on the quality and value of medical care from a patient’s perspective. As a result, healthcare payment models require comprehensive insights into population health, and 60% of commercial plans now link payments to value. Healthcare organizations have traditionally relied upon structured data in Electronic Health Records (EHRs) and insurance claims to analyze the health of patient populations or make clinical decisions. However, an estimated 70% of clinical data stored in EHRs is unstructured text, and unlocking the value in this text requires advanced Natural Language Processing (NLP) technologies such as Linguamatics I2E.
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There’s growing interest in the use of machine learning to solve challenges across the drug-discovery pipeline within the biopharmaceutical community. The availability of high quality data for training algorithms is vital to machine learning success - but much of this information is tied up in unstructured, or semi-structured text sources. Natural language processing (NLP) is the key to extracting the wealth of data hidden in unstructured text, and Linguamatics’ customers have been finding out first-hand what this approach can do for them.
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Modern drug safety and pharmacovigilance began in the early 1960s following the thalidomide disaster.
However, the growing cost of drug development is driving pharmaceutical companies to identify potential safety issues earlier in the process. Valuable safety data are available in public databases like MEDLINE® and internal sources such as study reports, project reviews, clinical investigator brochures and case reports, but much of this is unstructured text. Linguamatics I2E platform transforms this text into actionable data that can be rapidly visualized and analyzed at every stage of the drug development process.
Read more
As healthcare continues to shift to value-based care, the use of big data analytics in healthcare and Artificial Intelligence (AI) techniques like Natural Language Processing (NLP) is essential technology for providers and payers as they work to make sense of the growing volume of unstructured data from EHRs, claims, notes, patient-reported outcomes, and social media.
Roughly 80% of clinical data is estimated to be in an unstructured form. NLP-based text analytics platforms like Linguamatics I2E enable unstructured text to be translated into discrete data fields by identifying the key concepts and their relationships in healthcare documentation.
Read more
Real world evidence data provides valuable insights into the drug development process. Being able to rapidly transform data sources into actionable evidence has positive consequences on patient health outcomes and is already helping top pharmaceutical companies to be more efficient in their drug development processes.
Read morePopulation health management places increasing emphasis on the quality and value of medical care from a patient’s perspective. As a result, healthcare payment models require comprehensive insights into population health, and 60% of commercial plans now link payments to value. Healthcare organizations have traditionally relied upon structured data in Electronic Health Records (EHRs) and insurance claims to analyze the health of patient populations or make clinical decisions. However, an estimated 70% of clinical data stored in EHRs is unstructured text, and unlocking the value in this text requires advanced Natural Language Processing (NLP) technologies such as Linguamatics I2E.
Read moreThere’s growing interest in the use of machine learning to solve challenges across the drug-discovery pipeline within the biopharmaceutical community. The availability of high quality data for training algorithms is vital to machine learning success - but much of this information is tied up in unstructured, or semi-structured text sources. Natural language processing (NLP) is the key to extracting the wealth of data hidden in unstructured text, and Linguamatics’ customers have been finding out first-hand what this approach can do for them.
Read moreModern drug safety and pharmacovigilance began in the early 1960s following the thalidomide disaster.
However, the growing cost of drug development is driving pharmaceutical companies to identify potential safety issues earlier in the process. Valuable safety data are available in public databases like MEDLINE® and internal sources such as study reports, project reviews, clinical investigator brochures and case reports, but much of this is unstructured text. Linguamatics I2E platform transforms this text into actionable data that can be rapidly visualized and analyzed at every stage of the drug development process.
Read moreAs healthcare continues to shift to value-based care, the use of big data analytics in healthcare and Artificial Intelligence (AI) techniques like Natural Language Processing (NLP) is essential technology for providers and payers as they work to make sense of the growing volume of unstructured data from EHRs, claims, notes, patient-reported outcomes, and social media.
Roughly 80% of clinical data is estimated to be in an unstructured form. NLP-based text analytics platforms like Linguamatics I2E enable unstructured text to be translated into discrete data fields by identifying the key concepts and their relationships in healthcare documentation.
Read more
