There were 1,681 press releases posted in the last 24 hours and 414,179 in the last 365 days.

Hindsait has unveiled an on-demand Clinical Natural Language Processor to unlock clinical information trapped in EMR

Hindsait Logo

After 8 years of development and rigorous testing, Hindsait is releasing Its CNLP capabilities via an API for any healthcare institution to try for free

Nearly 80% of the clinical data in healthcare is trapped in EMR systems as unstructured free text. We are excited to unveil on-demand CNLP services via an API to unlock this treasure trove of data.”
— Pinaki Dasgupta
HACKENSACK, NEW JERSEY, USA, August 17, 2021 /EINPresswire.com/ -- Hindsait, Inc. today announced that it is making available its industry-leading Clinical Natural Language Processor (CNLP) for any accredited healthcare institution to try for free for up to 50 pages of medical records.

Pinaki Dasgupta, Hindsait's CEO said, "nearly 80% of the clinical data in healthcare is trapped in EMR systems as unstructured/semi-structured free text. These include encounter notes, visit reports, consult notes, in-patient records, physical therapy notes, lab reports, medications, etc. This treasure trove of data is difficult to access and make use of but is of critical importance for a broad diversity of use-cases/applications. At Hindsait, we are excited to unveil our on-demand Clinical Natural Language Processing (CNLP) services available via an API (application programming interface).

Any organization across the USA with medical records will now have the ability to unlock their data trapped in these medical records as an intuitive structured data feed return. This utility has wide applicability for both Payers and Providers to address multiple use cases including “Quality Measures reporting to CMS, NSQIP, etc”, “Utilization Management”, “Risk Adjustment” and “Claims Cost Management”, “Revenue Cycle Management” and more.

About Hindsait: For the past 8 years, Hindsait has been on a mission to unlock unstructured data trapped in EMR systems. Hindsait has built a robust technology stack that unlocks this treasure trove of information and turns it into structured data for any organizations, be it payers, providers or health systems to make use of.

Hindsait’s technology allows users to upload medical records in any format, be it PDF, JPEG, TIFF, PNG, TXT or CCM, CCD or FHIR, which then is returned to the user as a structured dataset in JSON, XML, .xls, or .csv format, based on user preference. In addition, Hindsait’s platform enables users to view their files as hyperlinked annotated texts in Hindsait’s user-friendly audit platform to review, audit, track, and identify the relevant structured data extractions.

The structured data extracted and returned from Hindsait’s platform, accessed via an industry standard API, can be integrated with any off-the-shelf BI tools (Business Intelligence) for temporal analyses, hypotheses testing, understanding risks, appropriateness and efficacy of treatment, improving quality measures, population health, real world evidence and much more!

For payers, benefit managers, payment integrity organizations and/or IDNs, ACOs, that need to review, audit, track, and identify patient’s risks and utilizations, Hindsait’s audit platform flags unnecessary health services, detects coding errors, and flags applicable risks, by harnessing the power of AI (artificial Intelligence) technology. Hindsait’s audit platform enables much more efficient reviews while concurrently reducing human errors during the automated/augmented review process.

Hindsait’s AI platform supports variety of use cases like “Utilization Management, Prior Authorizations, Payment Integrity Audit, Risk Adjustment Audits" for payors and "Quality Improvement, Risk Adjusted Reimbursements, Real world Evidence” for providers and ACOs.
Healthcare organizations can access this free trial offer by clicking here
Contact information for Hindsait:
Email: info@hindsait.com
Phone: 862-206-6657

Pinaki Dasgupta
Hindsait, Inc.
+1 8622066657
email us here
Visit us on social media:
Facebook
Twitter
LinkedIn