New joint offering solves critical need for real-time accuracy and attested providence of provider information
Denver-based healthtech companies Orderly Health and BurstIQ are partnering to offer an integrated machine learning and blockchain solution for best-in-class provider data management. The integrated service will be offered to health plans, provider systems, health information exchanges, public health agencies, credentialing services and other healthcare solution providers.
As studies by the Center for Medicaid and Medicare Services (CMS) and others show, provider directories have accuracy rates of less than 50%, which can result in inappropriate referrals and data sharing, out-of-sync scheduling systems and inaccurate quality reporting – all of which can lead to fraud, higher credentialing costs, and incorrect patient billing.
Regulators, including CMS, are increasing penalties against health systems and payers who fail to increase the accuracy of their data.
The new integrated solution developed by Orderly Health and BurstIQ brings together the immutability and traceability of blockchain, complex data ownership and granular consent, best-in-class data security, and AI-enabled data science – enabling the most accurate, trusted provider data management system in the industry.
The joint offering leverages BurstIQ’s HIPAA-compliant enterprise blockchain platform, BurstChain, which manages complex data ownership and permission-based data access on the industry’s only fully regulatory compliant blockchain-based data management network.
Orderly Health’s unique machine learning engine, OrderlyData, utilizes a variety of client-based and proprietary data sources to improve the accuracy of provider data to ≥90%. The joint offering will provide end-to-end traceability and auditability of provider information as well as the ability to manage access to and ownership of provider data at all levels.
“This partnership addresses a critical need,” says BurstIQ’s CEO, Frank Ricotta. “Provider data plays a key role in every interaction and every transaction in the healthcare industry. We are excited to be collaborating with Orderly Health to bring this much-needed solution to market.”
“Data is only valuable if it’s both accurate and accessible,” adds Kevin Krauth, CEO of Orderly Health. “The combined offering of BurstIQ and Orderly promises tremendous benefit through secure, accessible storage on the blockchain, while also ensuring clean and accurate data through OrderlyData’s machine learning solution. We’re thrilled to partner with BurstIQ to bring our combined offering to market together.”
About Orderly Health
Orderly Health uses data science to help health plans deliver world-class member experiences across the patient journey. OrderlyData, Orderly’s provider data accuracy solution, leverages machine learning and billions of public and proprietary data points to automatically assess and repair inaccuracies in health plan directory data. OrderlyData lifts to 90% or more, including provider demographic data, network status and accepting new patient status, resulting in an improved experience for members and cost savings for health plans of 75% or more. Orderly Health is a graduate of Techstars and 500 Startups.
For more information visit: www.orderlyhealth.com | Twitter | LinkedIn
About BurstIQ
BurstIQ™ is an industry-leading blockchain enablement company, offering enterprise-level blockchain solutions for the health and healthcare industry. The company offers a HIPAA- and GDPR- compliant platform that seamlessly leverages blockchain, advanced security, Big Data capabilities and machine intelligence to enable a global health data network through which healthcare businesses and individuals can access, control, monetize and gain insights from their health data. The company provides platform-as-a-service (PaaS) solutions to healthcare institutions, insurers life sciences/pharma companies and government agencies at the state, national and international level.
For more information visit: www.burstiq.com | Facebook | Twitter | LinkedIn