How do we solve this problem?

Our team of healthcare technologists and doctors went through iterations for years and achieved an out-of-the-box solutions. These solutions area combination of AI, healthcare knowledge, and customized big data frameworks.
  • MCheck, an artificial intelligence-based platform automates the analysis of health data errors at scale

  • MCode, which is a combination of AI, healthcare knowledge, and customized big data frameworks, detects under-coding in Medicare Advantage data

  • MSure, The unguided AI solution detects up-coding in Medicare Advantage data and ER visits by performing comparative data quality and completeness of MA data

Business value provided to the client by-

  • Carrying out comprehensive testing via pattern-based comparison of data between source and target and accommodates transformation rules within data patterns. The robust Pattern Learning and multiple testing iterations identifies the Inter-systems gaps.

  • 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

Supervised and Unsupervised training


Multi-Pronged Approach for Issues in Provider Directories


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