Posts from June 2018

Collaboration addresses major bottlenecks in prior authorization and medical review

Cambridge, England and ROCKVILLE, Md. — June 26th, 2018 — Linguamatics, the leading natural language processing (NLP) text analytics provider, and Secure Exchange Solutions (SES), a market leader in enabling the secure exchange of health information, today announced the selection of Linguamatics Health as the NLP platform for SES SPOT, a solution that, when combined with SES Fetch, streamlines clinical information exchange and automates the review process.

Inefficient medical review processes (either before or after submitting claims) are major contributors to rising costs in healthcare systems. SES SPOT was developed to evaluate clinical information to help control costs and improve outcomes so that patients receive the appropriate care rapidly, reducing manual effort for providers and public or private health plans, and providing the opportunity to dramatically save time and money.

SES SPOT includes Linguamatics I2E to provide Artificial Intelligence (AI) to extract information from both free text and codified data in an electronic medical record, to compare extracted data with guidelines, and to return evidence, recommendations and an audit trail to automate or semi-automate approval of claims.

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Innovative Artificial Intelligence and Machine Learning Technologies can improve Pharma R&D, Reduce Costs and Benefit Patients

The Pharma industry is constantly searching for more effective, more efficient tools and technologies to improve the drug discovery process. The statistics are well-known, and make gloomy reading: it takes 10-15 years to develop a new drug, at a cost of up to $1 billion. There is currently vigorous discussion over whether new tools and technologies can significantly impact these metrics. Big data, blockchain, artificial intelligence (AI) and machine learning (ML) are much talked-about as holding the key to digital transformation of drug discovery.

At the Bio-IT World Conference & Expo last month, many of these themes were explored. Across the dozen or so session tracks, there were talks and workshops to share information and best practises on how scientists in biotech, pharma, academic institutes and vendor companies are applying AI and ML for a variety of use case, such as models for adaptive clinical trials, imaging analytics (e.g. for pathology or clinical sample data), lead design, QSAR, analysing data streams from mobile monitoring devices, and more. Some snippets from the talks include:

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