Medicaid

How do we solve this problem?

In the under-coding space, HiLabs offers a proactive approach to detect data errors corresponding to under-coding using a combination of Medical Ontologies and historical data as an input in the AI engine. The AI engine also called MCode has been built on 30+ M members record from CMS data and analyzes every possible slice of data. It also gives the client the flexibility to be customized according to the health plans need and data. If you think this is not enough, it also provides the client with auditable facts.
Regarding issues related the provider directory, HiLabs offers a multi-pronged approach which uses a combination of NLP cum AI based technology. The Fine-tuned NLP extracts various from the single source of truth-provider contract document. The implementation centric ML engine finds the incorrect networks, shows the root cause and provides solution by adding the right network values provided from NLP.

Business value provided to the client by-


  • To put AI into action, all results generated by the algorithms are supported by auditable “facts” and not based solely on a probabilistic model.

  • The tool can be easily integrated with healthcare customized NLP to extract issues in provider network directories

  • The implementation centric ML engine finds the incorrect networks, shows the root cause and provides solution by adding the right network values provided from NLP

  • The platform’s algorithms run on a powerful, statistical, multi-model and self-learning engine to identify data errors, with minimal manual input

Multi-Pronged Approach for Issues in Provider Directories

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