Provider data is a critical revenue generating asset for health plans, health systems, and workers compensation organizations. An essential process for ensuring data accuracy is provider roster management. Roster management enables organizations to associate or disassociate a provider with whom they are currently affiliated with or in the process of contracting, to include within their provider data management ecosystem.
The impact of inaccurate data includes:
As a means to stay ahead of data accuracy, provider practices spend more than $2.7 billion and health plans spend more than $2 billion annually on inefficient and redundant work to ensure accuracy of directory data.
The multiplicity of managing provider data is a contributing factor in the failure or success of a provider data infrastructure. Provider data is exchanged in almost as many formats as there are exchanges; and rarely does an ecosystem of provider data conform to a single set of standards. Because provider data is so broad and varies widely, the complexity required to recognize and address these inconsistencies when it comes to provider roster management can overwhelm manual, deterministic approaches -leading to many data conflicts, data errors, or dead-ends.
This is further complicated by provider network expansions and changes causing provider dataset volumes to grow, with new datasets coming from additional disparate systems. As a result, the natural variations that arise require logic to become increasingly capable of adapting to these variations and changes. A logic that has typically required a costly internal IT lift, deep subject matter expertise for analysis, and time-consuming manual intervention. Until now.
Machine learning empowered provider roster management in the proven way forward offering programmatic data logic, matching, and validation.
How do we define Machine Learning
Machine learning is a subset of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning operates without human bias or time constraints, computing every data combination to understand the data holistically in both its short term and longitudinal evolution.
What does machine learning powered provider roster management look like?
A machine learning enabled infrastructure can reduce the resource related costs and effectively manage fluctuating provider data.
It is important to collect provider information from various trusted sources. Confirming the accuracy of these details is a pillar of master provider data management. Data collection, consumption, transformation, and validation helps to ensure that analytics engines are informed by meaningful information.
Here is what a machine learning process works:
Collection of comprehensive provider data is critical to identify and corrects data gaps, duplicates, and outdated information. Yet the multitude of provider data sources combined the disparity in formats and structures can create a challenge for collection and consumption before roster management can even begin. However, the machine learning powered “data machine” employed by Perspecta can better consume and process provider roster data from any source or format to cleanse and transform it into a standardized, accessible framework.
Perspecta’s process supports quarterly provider attestation and validation to stay ahead of evolving CMS “Transparency in Coverage” mandates via the “No Surprises Act,” and other compliance regulations.
As a trusted provider data expert, Perspecta can more efficiently and effectively take on the provider roster management workload, apply machine learning and expertise to help health plan, health care and workers compensation organizations reduce costs and free up internal resources to focus on other essential areas of their business.
Let us simplify your provider roster management process with our Machine Learning (ML) powered solution. Our proprietary algorithms continuously learn and improve through experience and the processing of provider data. It consumes, aggregates, maps, and harmonizes data from all of your disparate sources and formats, over and over again.