Prior Authorization Automation
In a healthcare reality that still relies upon a significant use of fax machines, a modern approach meets and embraces its 19th century heritage. The upshot: requests have quicker turnaround times with a higher amount of efficiency.
Using advanced analysis including deep learning, submitted requests are organized and key data is automatically located which significantly accelerates the review process. In many cases, requests can be automatically processed removing the need for any manual review. As for supporting documentation, using NLP-based techniques, the text is parsed and interpreted in order to aid the reviewer with locating the specific patient data relevant to completing the review.
Health Claims Processing/Adjudication Automation
The challenges of claims processing, in the era of machine learning, seem like they should be a problem solved long ago. The reality is that over 90% of claims are handled through auto-adjudication. Costs continue to rise due to the need to handle non-standardized claims such as those sent in via forms (often faxed) and claims that require submission of additional supporting documentation. Both require a different approach using advanced computer vision along with machine learning.
With Parascript Virtual Drop-out, powered by deep learning neural networks, poor quality faxed CMS1450 and 1500 forms are analyzed and perfected to the quality of a drop-out ink form. This leaves only the data and eliminates the underlying form structure.
For claims that are accompanied by correspondence, Parascript advanced handwriting recognizers and text analysis can automatically transcribe the information into structured, machine-readable data to aid with either auto-adjudication or manual review.
Explanation of Payment and Receivables Automation
Parascript is the leader in payment automation technology for banks worldwide. When it comes to processing provider receivables, the challenge is enormous. Payment data is complex and can span multiple pages of a document leading to a slow, cumbersome intake process.
Easily turn complex, unstructured data into clean, tagged information. Parascript software is specially trained to deal with complex EoP, claims, correspondence and payment data. The software accelerates the receivables process through advanced document and data analysis which efficiently and reliably locates data needed to verify and balance payment information.
Workers' Compensation Claims Processing
Most claims are handled with straightforward processes until they get tied-up in litigation. That’s is where the complexity of unstructured information meets the power of Parascript deep learning and NLP capabilities.
Using advanced text analysis including natural language techniques, Parascript software easily identifies each document within a claim and then locates the specific information required. Even data hidden within prose-like verbiage is located and presented to the reviewer. Cases are handled in a fraction of the time typically required by an unassisted manual process, and the quality of the resulting data is significantly improved.
With Parascript software, health plans and plan administrators can efficiently process more claims with a higher level of quality and quicker turnaround.
Medical Record Retrieval and Review
While most medical records use structured databases, the richest source of patient data is still stored within unstructured documents. The ability to efficiently access and use this data is costly.
Parascript records extraction and review enables insurers and services providers 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 to separate one record from another.
Once the records are separated, additional text parsing is employed to locate data for an efficient review. The result is a more-efficient and reliable review process.
Health Plan Data Analysis and Conversion
Highly standardized health plans are relatively easy to convert into a set of business rules enabling the automatic handling of typical claims. When it comes to adjudicating claims based on customized plans such as those with self-insured policies, converting plan policies into data that can be used for automated processing is almost impossible.
Parascript is the insurance data expert. The process of converting complex unstructured data starts with sophisticated analysis using a combination of natural-language text analysis and machine learning to quickly locate required plan data. The entire workflow is guided, easy and highly efficient. The result is plan data is up-to-date and accurate in the shortest amount of time possible.
Innovation in Healthcare & Insurance
IDP: Machine Learning and the Training Required for Reliable Results
No machine learning to-date works with zero training, but advances to reduce the training required for reliable results are underway. Details here.
Digital Insurance: Insurance processes optimized for cost, but at-risk for disruption?
In insurance processes, document automation improves agility while reducing cost & risk. Join us for an in-depth look at automation tactics & opportunities.