​Machine Learning is a Provider Roster Management Game Changer

by April Stiles 

Provider data is a critical revenue generating asset for health plans, health systems, and workers compensation organizations. Roster management is an essential process for ensuring provider data accuracy. 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:

• Negative consumer experience
• Bad press within the market
• Provider abrasion
• Undue administrative burden
• Fines or sanctions by regulatory organizations​

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.
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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 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:

1. Identify all data collection points and provider rosters internally and externally: It is essential to collect provider data from all existing sources and formats to ensure comprehensive data for aggregation, mapping, cleansing, and harmonizing within a machine learning enabled roster management process.
2. Create a master data set: Bring all systems and stakeholders into alignment by creating distinct lists and nomenclature to facilitate accurate record linking.
3. Apply a machine learning process: Machine learning, when done properly, yields no waste. Machine learning adapts and learns as provider dataset volumes grow. It applies the capability to increasingly manage and adapt to the natural variations that arise. A machine learning-enabled data process detects metadata properties and/or patterns across multiple fields in attempting to reconcile incoming records.
4. Benchmark data: This process must include quality checks of the information flow to identify problem points, and monitor and correct errors that may occur during processing.  A machine learning workflow can apply fuzzy & probabilistic matching to compare your data with other data sources for accurate benchmarking.
5. Validate and reconcile data discrepancies: A data validation test is vital to provider roster management because it provides insight into the scope or nature of data conflicts. Enable a machine learning enabled, digital process to simplify provider attestation of the accuracy of their practice information and better manage provider rosters. This can greatly reduce the “human effort” required to validate data. Data validation can be enabled by several automated, rules-based processes or natural language processing to identify, remove, or flag inaccurate or anomalous information, leaving behind a clean provider data set.
6. Create a master data index (single source of truth): Ensure that this data is updated into a secure, accessible master data index. This can help to enable consistent use of data across departments and business entities. In addition, this “single source of truth” can promote accountability using timestamps and audit trails for every update and edit. Overall, this machine learning optimized provider data can improve user experience, data accuracy, access, and regulatory compliance.
7.  Ensure ongoing access and sharing of provider data: By understanding user needs, potential application machine can optimize, and share provider data across the collective business units and illuminate patterns for optimal analysis. Accurate and complete data has the potential to transform the process of stakeholder decision-making, reduce costs, and ultimately promote healthier outcomes.

​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.
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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.

Learn more about Perspecta's Roster Management solution

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