AI/NLP based provider directories DQM

How do we solve this problem?

HiLabs offers a multi-pronged approach which uses a combination of NLP cum AI based technology. The Fine-tuned NLP extracts various information like TIN, market, address, specialty along with provider network names 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.

Taking the correct data straight from the horse’s mouth

HiLabs’ fine-tuned NLP will help you ingest the correct data from the contractor documents itself. HiLabs has customized its NLP encoder specifically for the healthcare industry through ingestion of vast array of relevant medical ontologies


Leveraging the knowledge from medical data

MCheck, HiLabs’ flagship product has been built on supervised learnings from millions of medical data, has identified key data errors in several provider legacy datasets through pattern-learning based anomaly detection

Inbuilt-OCR processing

Processing of scanned data through multiple combinations of thresholds and filters to convert it into a readable format for NLP engine. The Smart OCR designed by HiLabs can extract information from legacy data and written texts as well

Multi-Pronged Approach


Drill down analysis to identify root cause

MCheck is capable of carrying out rapid root cause analysis. It can drill down into multiple variables and pinpoint the exact problem in your dataset

Easy data ingestion with insured security

Quick data ingestion on our MGest platform which is compatible with most of the data source systems in the industry. Data security insured through virtual or cloud systems

Let’s make an impact

We at HiLabs are making businesses better for several of the country’s health plan. We understand your needs, are flexible to customization and are ready to bring in the change through our rich experience in this industry

About HiLabs, A cohort of experienced Engineers, Data Scientists and Physicians.

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