Before our collaboration, when company M submitted a batch of claims to a payer, they had little knowledge about which claims would be paid or denied. Our client had to wait until they received remittances to know:
- Will this claim be paid?
- Why will this claim be paid or denied?
- What can I do to ensure this claim is paid?
- How many claims in this batch will be paid?
In addition, there were many questions with very high value and yet very high uncertainty:
- What can I do to improve the likelihood of this claim being paid?
- Where should I focus my resources?
- How can I minimize the DSO?
Methodiq found that these questions could be answered by leveraging our client’s large dataset. By using machine learning techniques, we created predictive and prescriptive models, which helped to augment and empower the billing team to focus on the claims that were most at risk of being denied. Furthermore, we were able to help them answer these decision-driving questions:
- Which claims are most likely to be denied?
- Are claims more likely to be paid if we add some specific sets of information?
- Which claims are surely going to be denied and are not worth spending time on?
Answering these questions helped us to identify impactable events and optimize the billing team’s workflow.
We used an interpretable model, a method we use to allow us to explain how our model came to its conclusion. This helps our clients to track the reasoning for acceptance or denial, so they are better able to anticipate what information is required before they submit a claim.