Healthcare Strategic Growth Blog

Leverage Adjustable Confidence Scoring for Empowered Payer Negotiations

By Hannah Killian and James Porter

In survey research¹, a confidence level is applied to express how confident a researcher is in the data obtained from a population sample. A confidence level lets someone know if they can trust the research data to make decisions. 

Similarly, a confidence level (or score), when applied to all-payer claims data (APCD), indicates the trustworthiness of this data for making healthcare decisions. It’s a metric that ensures integrity and can empower a provider to use this data with greater assurance. 

Greater data confidence can be useful when pursuing a number of healthcare planning initiatives. In this post, we’ll discuss specifically how an adjustable confidence-scoring system is valuable when preparing for provider-payer reimbursement negotiations. Equipped with this tool, providers can:

1. Experience data with certainty, knowing that the cleaning and curation work has already been done 
2. Apply their intuition, since variability can be adjusted to allow for the application of local knowledge
3. Approach negotiations empowered by data insights, assured that the data they’re relying upon to make decisions is trustworthy

Let's get started discussing each of these factors in more detail. 

How frequently do you QA your data? Do you check for discrepancies in your data? Do you have a standardized method for identifying outliers in your data?

The value of data-driven decision making may already be apparent to your team. Or, maybe you've never recognized how Data QA can improve your growth planning process. Either way, this quiz will provide valuable insight as to how well your team is doing. 

Take this short quiz and receive:

• Immediate feedback as to how good your team is with Data QA
• Tips and best practices for improving your Data QA process
• Insights about the value of data-driven decision making