In our last post, we discussed what our ideal version of an effective denial management process would look like. In this post, we are excited to discuss how to execute those strategies!

To execute the strategies discussed before, providers need niche technical capabilities. While a base model based on logistic regression or random forest could be a good starting point, incremental improvements are possible with a more intelligent analytical engine which back-propagates the error and tune dynamically. Newton AI is adequately equipped to do so and handle your voluminous data in quick time. In a way, it consolidates all intelligence from your denials data into one single platform and repeatedly pushes the workflow for the most optimal denial management. The automated denial categorization and follow up list helps providers improve their first pass payment rate and maintain a healthy days sales outstanding (DSO). The engagement model guarantees a minimum reduction in denial rate and assures continual improvements, making Newton AI a niche tool worth your time for exploration.

How do you currently manage denials at your end? What strategies have worked in your setting and have you been able to automate those? How do you see analytics playing a role in your initiative? We would love to hear your views!