The Value of Having Confidence in Your Data

The Value of Having Confidence in Your Data

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. 


Experiencing Data with Certainty

Stratasan’s APCD includes the following data: commercial, Medicare fee-for-service, Medicare Advantage, and Medicaid. It provides insight into all sites of care—from hospitals and ASCs to physician offices and urgent cares. With over 1.5 billion claim lines, claim-level detail, and nation-wide coverage, it also has a lot of variability and “noise.” That’s why it must be cleaned and curated before providers can leverage it to draw reliable market conclusions. 

Before Stratasan’s APCD is loaded into the Market Reimbursement Analyzer and passed on to providers for analysis and negotiation prep, it’s curated from the claim-level detail during Stratasan’s elemental data processing service. During this process, the experts verify trends, connect codes, validate formatting, and QA the data. Additional proprietary cleansing is applied to the unit information, procedure modifiers, and outlier trimming for the most refined market insights. As part of the curation process, a confidence score is applied.

This can be an arduous and time-consuming process, but one that our team is well-equipped to do. By taking care of this work ahead of time, end-users of the data don’t have to. Instead, they can spend more time on the important work of using this data to make informed decisions.

Applying the Intuition of Local Knowledge

Healthcare data has a lot of nuances—market variation and local dynamics that, when stripped away, can lessen the value of the data. Data that’s been overly-modeled in a “black box,” using unknown formulas that can’t be adjusted, is data that has been stripped of its nuance.

It’s important that the data curation process accounts for the expertise of the one cleaning the data as well as that of the end-user who brings their own local knowledge when analyzing a dataset. The analyst cleaning the data can’t assume to know the exact use case and so the data shouldn’t be cleaned or modeled so thoroughly as to take away the ability for end-users to inject their unique market insights. Differences in variability can come by market and specialty. So, depending on the situation, end-users may need to leverage the data differently and should be able to without modeling to get in the way.

That’s why the confidence score in the Market Reimbursement Analyzer is adjustable. It defaults to show an 80% level of confidence with 10% variability, but this can be adjusted by the end-user. As the confidence percentage is increased, the variability in the data goes down.

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Approaching Negotiations Empowered by Data

We want our confidence scoring process to be accessible and easy to understand—no black-box modeling here. This is important to us because transparency leads to confidence, which in turn means our partners are empowered to use our data to make more informed decisions. 

In the case of provider-payer negotiations, data confidence can strengthen a negotiating position and help organizations complete the next payer meeting with internal goals met.

The right data insights can: 

  • Validate assumptions about your payer before you get to the negotiation table
  • Show how payer contracts compare to the market
  • Show how reimbursement rates have changed over time from facility to facility

Having confidence in your data, and the ability to adjust allowed amount variances within your data will lead to a stronger negotiating position and more empowered payer negotiations. 

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The Takeaway

When preparing for payer negotiations, organizations need to be equipped with data they can trust and adjust based on market-specific factors. The need for data confidence is obvious—the more the better, right? But since 100% data confidence isn’t possible, particularly when referring to APCD where there will always be outliers and variability, it’s critical to find a sweet spot of data reliability that leaves room for nuance and flexibility based on market-specific factors. 

For more information about the Market Reimbursement Analyzer, and the adjustable confidence score built into this tool, schedule a discovery call with one of our experts today. Let’s talk about how you can walk into your next payer-provider negotiation equipped to ensure you’re getting paid what you’re worth.

Article by Hannah Killian, Director of Analytics, and James Porter, Director at Ancore Health

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1 Surveys Research: Confidence Intervals and Levels



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