FinThrive, a healthcare revenue cycle management software-as-a-service (SaaS) provider, has officially announced an expanded investment into its predictive and preventative revenue cycle management platform to solve the problem of revenue loss in health Industry. According to certain reports, the update kicks off by enhancing FinThrive Analyze, the platform’s dedicated analytics offering. Breaking down data silos to generate a holistic view into an organization’s revenue processes, FinThrive will launch industry’s first, true enterprise-grade data lake, which can unify data from all FinThrive solutions with EHR and other third-party data. While keeping you informed at all times on the overall picture, this comprehensive view also goes a long distance when the agenda is to target specific revenue leakage drivers, using actionable insights, and improve the bottom line. Owing to that, FinThrive Analyze now has on the offer an insights hub where a Machine Learning (ML)-based data visualization solution is available to deliver advanced end-to-end intelligence, KPI tracking, predictive modeling and performance forecasting, benchmarking, as well as an option to leverage data from multiple sources. Next up, it presents integrated analyzers that include simple modular analytics solutions embedded into FinThrive platform workflow solutions, complete with KPIs, charts and graphs to efficiently identify performance and results.
Moving on from Thrive Analyze, FinThrive has also introduced Active Insurance Discover, which brings #1 Best in KLAS coverage detection to proactively run full coverage searches on all accounts starting at the point of schedule. This particular solution will work in collaboration with the company’s real-time insurance discover solution to capture all available coverage before care, thus ensuring patients are financially counseled in a proper manner. Not just that, the combination can even be expected to prevent eligibility-related denials before they can happen. On top of that, it will also install into the mix those machine learning scoring models, models which let back-office revenue teams to focus on accounts with the highest probability of delivering revenue. Such a focus directly cuts down on eligibility-related revenue loss. The solution has already went through an initial testing phase, which saw one health system realizing a 53% improvement in revenue capture, derived by Insurance Discover end users, after migrating to the Machine Learning-based worklist prioritization.
Rounding up the highlights is FinThrive’s bid to provide FinThrive Authorization Manager as part of the company’s patient access suite of solutions. Quite similar to FinThrive Analyze, this authorization manager is basically poised to provide predictive analytics and automation to front office teams. This should, on its part, streamline each stage of the authorization process, from determination and submission to status monitoring and successful approval. Furthermore, the solution in question will also bank upon machine learning to predict when a prior authorization will be required for a patient. As for the credentials of this ML model, it has been notably trained on trillions of unique datapoints from the FinThrive Clearinghouse.
“FinThrive takes a unique approach to development, in that they ask their customers for regular feedback during the planning process to ensure the solutions they build will fit our need. Now that an actual solution is installed in our hospital, we couldn’t be more excited. It’s clear when they told us they were building an RCM Platform designed by revenue professionals, for revenue professionals, they meant it.” said Jessica Howell, Director of Patient Financial Services at Midland Memorial Hospital
