The role of machine learning (ML) and artificial intelligence (AI)
by Howard Koenig... It is important to break down data silos to access and transform data that is complete, usable, and standardized to apply advanced analytics to enable more informed decision-making for all stakeholders within the provider network enterprise. The biggest challenge is keeping provider data accessible, secure, and relevant, as it must be managed across multiple clouds, external and internal entities, platforms, applications, and attributes.
It is critical for health plan and workers’ compensation organizations to access provider data solutions with intuitive analytics built into their data management solutions. This helps to streamline and optimize the provider data management process across business units and systems. This begs the question – “How?” Artificial Intelligence (AI) and Machine Learning (ML) enabled analytics, data cleansing, and data management can drive innovative new business insights to help these organizations better understand the needs and experience of consumers and injured workers.
ML and AI-powered analytics has the greatest impact on data management costs and revenue and present a competitive advantage. According to a study from Forrester and IBM, 89% of enterprise decision makers agree that scaling AI leads to competitive differentiation.
The intuitive presentation of provider data can drive business initiatives such as better customer engagement, optimizing claims management, and build a stronger, higher performing provider network. ML and AI can help organizations by processing and presenting holistic program information from such as transaction data, performance data, pricing data, demographic data, and consumer-related activity. To accomplish this, it is important that analytics are available on a “self-service” and ongoing real-time basis, and not reliant on external reports and timelines. This way internal stakeholders (and even consumers) can take an active part in managing provider data.