Hilabs Inc.
Member Level: SponsorVendor Overview
Description of Services: HiLabs was founded at Yale with the mission of refining dirty health plan data. Our team of healthcare professionals has worked alongside data scientists and AI experts to perfect our platform's ability to accurately discover data patterns and errors related to provider, claims, clinical, and value-based care data. The result is an AI platform, MCheck, powered by algorithms validated in peer-reviewed research that serves 4 out of the 10 largest health plans and multiple regional plans across the US. MCheck's results are widely recognized:
MCheck Provider: Improving Provider Data Accuracy End-to-End
- 65% to over 91% provider directory accuracy for a national plan in 19 different markets in less than a year
- 100,000+ provider rosters ingested and standardized
- 3 million+ records auto-updated
MCheck Clinical: Driving Value Through Interoperability
- 34 billion+ records analyzed
- 96% decrease in clinical data acquisition efforts
- 95%+ accuracy in HEDIS/STAR gap discovery
MCheck Payment Accuracy: Reducing Claims Overpayments due to Operational Errors
- $8 million overpayments detected for a single claim type in a single market
- 4 billion+ claims analyzed
- 97% accuracy in anomaly detection
MCheck Value-Based Care: Achieving the Triple Aim through Data Quality
- 1000's of member misattributions detected
- 100's of rules automatically created by data pattern discovery automatically
- Detects downstream and upstream errors
Primary Service: Provider Data/Provider Data Mgmt
Secondary Service: Automation, Workflow and IT Services
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7272 Wisconsin Avenue
11th Floor
Bethesda, MD 20814
507-421-5531
http://hilabs.com
Documents
Recording: Improve Your Provider Directory Accuracy and Increase Your Network Adequacy Scores
Join HiLabs to learn about ghost networks, identify the root causes of ghost networks, and describe a technology solution to solve the ghost network problem.
Presentation: Improve Your Provider Directory Accuracy and Increase Your Network Adequacy Scores
Join HiLabs and learn how you can use advanced machine learning algorithms and AI to automatically clean your physician directory. Millions of people rely on health plan provider directories to find their doctors. However, over 80% of doctors have directory inaccuracies in key data elements which is misleading for consumersââ?¬â?a key area of concern for Congress. Plus, improving provider directory accuracy actually increases network adequacy scores.