When it comes to auditing for quality standards of care or reviewing an escalated claim, nothing is more important than detailed patient data. Even with attempts to standardize data sets used for compliance with quality of care standards such as HEDIS, or when payers need to enlist aid from independent medical reviewers, the reality is that the most useful data (and also often the data with the most detail) is always hard to identify and analyze; most of it is stored as highly-variable patient charts including notes and other unstructured information, making associated processes involved with locating and reviewing the information a difficult, costly, and error-prone process.
A Smart & Efficient Review Process
Parascript Records Extraction and Review enables insurers and services providers the ability to reliably create individual records from a single PDF submission. Using NLP-based techniques, the software analyzes the text of the file and identifies key service-related attributes in order to separate one record from another.
Once the records are separated, additional text parsing, including the ability to transcribe handwritten data is employed to enable reviewers to quickly locate relevant data to aid with the identification of diagnoses and prescribed treatments.
The overall goal is to provide an assistive workflow and automate as much of the data preparation as possible in order to provide the reviewer with the data they need to efficiently do their work.
Parascript Record Review, powered by Smart Learning provides assisted automation at scale. Simpler reviews can achieve a high level of automation and even the most complex records are sorted and prepared for review, maximizing the time a reviewer can make decisions vs. prepare data.
