Astrata’s NLP technology can take documents in many different forms, including CCD, HL7, PDF and plain text. As part of your advancing data strategy, we’ll work with what you have in place already and help you evolve to better, faster and more scalable ways to consume this data. 

Astrata NLP technologies work with “unstructured data” – clinical notes that providers write or dictate during and after encounters. Astrata’s FHIR and CQL technologies work with “structured data”. These are the data collecting in discrete form such as claims data and codes, observations, and laboratory results. 

Fast Healthcare Interoperability Resources (FHIR) is a widely used data standard that helps systems and organizations interchange and use healthcare data with less manipulation. Quality Improvement Core (QI-Core) is the quality data model that NCQA is moving the industry towards. And the Clinical Quality Language (CQL) is a programming language that is designed to help automate and standardize the logic of quality measurement and decision support. Together, these new technologies will significantly change the way we measure quality. As CMS, NCQA start requiring these technologies, your organization will need to prepare for change. 

Astrata technology can transform your HEDIS operations: (1) move your team to year-round review across multiple lines of business, (2) empower your quality intervention efforts with more timely data, (3) reduce the burden on your providers and increase the value of quality data you provide them. Astrata’s integration capabilities help you transform gently, by working within your existing work processes and technologies. 

Yes. Astrata’s technology is designed to make it easy to create custom NLP measures. This can be added to our standard offerings for organizations that have more specific needs. 

Yes. Astrata technology includes many measures that are used in CMS Star Ratings, state Medicaid pay for performance programs, and the ACA Marketplace QRS program….

Astrata technology can be used for a huge range of measures, from population-measures used by payers (e.g. HEDIS), to provider-focused measures (e.g. MIPS). We also provide measures in a wide range of domains including primary care, subspecialties, and behavioral health. 

NLP accuracy is one of the most important factors in determining how much value your organization will derive. Vendors that are actively monitoring their accuracy should be able to give you performance metrics for specific measures and should separately measure false positives and false negatives. They should have a way to assess how well the NLP is working after deployed and over time, and they should be able to make changes to keep the system functioning within acceptable limits. They should be transparent about all of this with you.  

Yes. NLP can be an important first step in your HEDIS transformation, because it will help you change your operations and move to prospective, year-round quality reviews. But NLP can also help you improve your quality rating and...