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How can Automated Business Analysis help Pharmaceuticals track payer type changes?

Pharmaceutical companies operate in an environment where they are being pressured to bring new products to market quickly and still ensure a solid profit margin. And, the amount one payer provides can increase or decrease the revenue mix quickly and drastically. Given the payer mix fluctuates frequently and each payer actual pays a different portion for each drug, predicting revenue for a pharmaceutical can be difficult.

Because of this fluctuation in payment, payers are seen as more of a barrier than a customer currently. It’s important pharmaceutical companies understand what is happening with payer data so they can stay on top of payer changes. This ensures pharmaceutical executives aren’t surprised with monthly or quarterly revenue results.

So, how do you plan for growth or decline with these changing dynamics? Wouldn’t it be best if payer mix changes were communicated daily so that pharmaceutical executives can focus on effective drugs and optimize the delivery of those drugs? Pharmaceuticals have payer data in-house to analyze and identify big swings in payer types to stay ahead of the curve. So, why haven’t they analyzed the data?

Tracking Payer  Type Changes Can be  Complicated

The reality is it’s a lot of data to analyze and there is a shortage of data scientists, roughly at least 50-60% less than what the world needs. Plus, think of all the parameters you’d need a data scientist to analyze: prescription date, issue date, prescription, payer type, payer region, payer state, HCP, etc. Imagine trying to analyze all that data across every day, every drug and every region. It would take someone weeks.

Tracking Payer Type Changes with Automated Business Analysis
Tracking Payer Type Changes with Automated Business Analysis

An Automated Business Analysis platform would ease this effort. Daily payer data could be analyzed and a solution such as Outlier would provide you insights every morning with what changed in the payer mix. Outlier does this by looking at time series data and applying artificial intelligence to identify changes in payer data. This is accomplished quickly allowing the data scientists within the organization to focus on more specialized projects. With Outlier, pharmaceutical executives can quickly see if an HMO has increased in the current payer mix and plan for the impact to gross margin.

Outlier analyzes any type of data, including compliance, marketing or supply chain too. Simply connect your cloud-based or SQL datasets to Outlier, and let it analyze the data. It’s helped this pharmaceutical company track revenue changes via purchases and returns. If this sounds interesting to you, sign up for our pharmaceutical webinar.

Our first blog on using Automated Business Analysis in  Pharmaceutical was about  discovering  changes to supply and demand indicators.  Our third blog in this series is about   compliance. Stay tuned.