How to Digitally Transform RCM

How to Digitally Transform RCM

In our last blog post, we discussed goals ApexonHealth strives to achieve. What we didn’t mention is how we achieve these goals.

Newton AI is the ApexonHealth answer to Digital Transformation of the RCM life cycle. Given our experience with all aspects of the RCM life cycle, we focused our energy on evaluating sub processes that benefit from digital technologies. Our aim was to build a set of digital components that use open technologies, Opex cost models (Cloud), inter-operate 3rd party systems (API), scale-able technologies (e.g. Hadoop), allow for continuous improvement (run small experiments continuously), enhance user experience (technology complexity hidden from users), and augment team productivity by using machine learning and sophisticated analytical models all within an established security framework. We have built tools from intelligent ingestion of data right through Executive Dashboards. Within the Newton AI platform, there are specialty specific components as well multi-purpose components that can be used across specialties. Several these solutions have been deployed in On Premise, Cloud and Hybrid modes.

Our successful models have been built using a multi-disciplinary team of RCM, coding, data science, statisticians and software architects. Over two years ago, we dedicated the above team and gave them a set of business use cases. Through constant experimentation we both succeeded and failed with a variety of RCM use cases. This has been an iterative journey for us where the cycle of exploratory data analysis, data collection/cleansing, building/testing analytical models, time studies, Natural Language Processing, Medical Data Dictionaries, operational integration etc. is an ongoing way of life. The result is a set of prebuilt analytical models, intelligent data ingestion/management software, visualization etc. Our experience has taught us that one size does not fit all. As an example, denials are dependent upon patient population, payer mix and a host of other factors. What our pre-built models allow us to do is rapidly explore and test business outcomes. We have frequently achieved transformative change within 6/8 weeks of using our current accelerators. Let us show you how Machine Learning and Data Sciences are transforming RCM.

Digitally Transforming RCM

Digitally Transforming RCM

Technologists love creating new terms; new ones are invented every day. Most technical terms have broad definitions and tend to mean different things to different stakeholders. A common explanation of Digital Transformation revolves around social, mobile, analytics and cloud (SMAC). When RCM practitioners hear this common interpretation, we try and relate these terms to RCM processes. However, many of these technologies have limited impact on the RCM cycle and, at this time, certainly not a transforming impact.

Given ApexonHealth lives and breathes RCM every single day of the year, we wanted to implement digital technologies that have a transformative impact on our ability to deliver daily production workloads. Each organization needs to set their own goals; at Apexonhealth we set our selves the following goals to justify the investments required for Digital Transformation of our production RCM processes.

Design Goals for RCM Automation team @ ApexonHealth.

  • End customer revenue increases by at least 2%
  • Cost of operation reduces by at least 30%
  • Turnaround time improves by at least 40%
  • Quality levels increase by at least 5%

The above is to be achieved without compromising compliance, security and privacy requirements. As we all know RCM business processes have their unique challenges in terms of volume of data, privacy, coding ambiguities, lifetime of a claim etc. It is therefore important to remember that we are seeking transformation of business outcomes and not using technologies for the intellectual satisfaction of the automation teams. Given that digital technologies are rapidly evolving, at ApexonHealth we focused our efforts on the leading edge and not the bleeding edge. Some examples of use cases we still believe are at the bleeding edge are facial recognition for insurance eligibility, block chain for reimbursement etc. Though the aforementioned, use cases will significantly enhance the efficiency of reimbursement. We believe the required eco-structure and industry infrastructure is yet to be established.

Keep up with our blog to learn more about our innovative solution that solves all these concerns while preventing new ones.

What goals do your company strive to achieve? What would your ideal solution for those goals look like? We’d love to hear your thoughts.