AI with Reasoning: Revolutionizing Provider Data Management with Roster Automation

As AI continues to transform the healthcare industry, "AI with reasoning" is one of the most significant evolutions of the technology.  As a healthcare company specializing in provider data management for health plans, leveraging AI to streamline manual tasks in roster management can transform how teams function. But what exactly is AI with reasoning, and how does it benefit the roster management process?  

What is AI with Reasoning? 

AI with reasoning refers to advanced AI systems that simulate human-like decision-making, problem-solving, and task performance. These systems use an ensemble of different techniques layered on top of each other to understand complex data relationships and make informed decisions at scale. This capability is particularly valuable when a large volume of data requires frequent and time-consuming manual intervention. Since health plans can receive thousands of provider rosters monthly, requiring standardization and quality checks, roster management is an ideal use case for a solution that uses AI with reasoning.   

Benefits of AI with Reasoning in Roster Automation

1. Enhanced Data Accuracy

By mimicking the decision-making processes of experts, AI with reasoning reduces errors in incoming rosters that human teams might not otherwise be able to identify.  

2. Increased Efficiency

Automating tasks such as roster updates and data verification frees up human resources for strategic activities, increases overall efficiency, and allows healthcare organizations to allocate resources more effectively.  

3. Proactive Data Management

AI with reasoning enables proactive data management by identifying errors at the source and updating information in real-time. This ensures provider directories remain current and reduces the likelihood of patients encountering outdated information. 

The Role of AI with Reasoning in Roster Automation  

To understand the technology behind such an AI solution for roster management, it helps to first think about how a human subject matter expert would operate. 

Managing incoming rosters involves the complex tasks of ingesting, standardizing, and quality-checking large volumes of data. Analysts would look at an incoming file and manually format it into the health plan-specific format. In doing so, they would discover that some data is mislabeled or incomplete. To improve data quality, they must then cross-reference other data sources, such as past rosters or NPPES. Knowing these sources also have data errors, they might consult various external sources, such as provider websites or data aggregators.   

For an AI model to mimic the experience and reasoning required for an analyst to complete this process well, multiple AI components coordinate together, including Natural Language Processing (NLP) and Symbolic Reasoning. 

Natural Language Processing (NLP)  

NLP refers to an AI's ability to understand and interpret human language. This capability is crucial for extracting relevant information about providers and ensuring the accuracy of their data. 

How it Works:

Cutting-edge NLP involves deep learning models and training on large amounts of data, and a variety of data types (i.e., structured, semi-structured, or unstructured). In roster management, this includes provider contracts, past roster submissions, NPPES, and data already existing in the provider database. 

Example in Roster Management:

NLP allows a solution to read the data in incoming roster files and identify if it is mislabeled or incomplete. It can also read content on provider websites and other public data sources to identify if the address in the incoming roster is outdated, before it is loaded into the public directory.  

Symbolic reasoning refers to creating a knowledge base consisting of logical rules and domain-specific information to simulate human-like decision-making.  

How it Works:

The knowledge base used for symbolic reasoning consists of rules and axioms that a seasoned analyst would have learned over time. In roster management, this would include guidelines for how to interpret a provider listed at multiple addresses or with multiple specialties.

Example in Roster Management:

If an incoming full roster is missing multiple provider records, symbolic reasoning will allow a solution to identify these records as having been terminated by omission. This enables better reconciliation of provider records.  

What Comes Next 

AI with reasoning is revolutionizing healthcare provider data management by automating complex tasks, enhancing data accuracy, and improving efficiency. Implementing AI solutions capable of reasoning can enable teams to engage in higher-level strategic work to deepen relationships with providers and ensure timely access to care for their patients. As a company leveraging this technology, we are at the forefront of this transformation. 

Interested in seeing the impact of AI with reasoning on your team? Contact us to learn more.