Revenue cycle teams have been swimming in EHR data for years and AI innovation gives a lifeline on how to deal with it all.
With hospitals collecting more and more data and rising expectations from patients around their healthcare needs, the case for artificial intelligence (AI) adoption is compelling. AI is now gaining traction in healthcare because of its ability to help generate insights from large amounts of data – offering a much-needed helping hand to overburdened staff. For healthcare IT leaders, there is much to gain from the insights, recommendations and operational support that AI promises.
One revenue cycle director noted: “When you invoke data science, assuming it’s programmed correctly, you should have confidence something will be performed consistently across a population of accounts. So for our internal audit folks in hospitals and health systems, reliability is a big component.” Read more
As Revenue Cycle initiatives shift to value-based models, AI innovation is starting to help analyze the data quickly and find profit opportunities in the data collected from patients and payers. Teams can leverage AI tools to make collection processes easier, meet patient demands for cost and care education, and personalize the billing options available case-by-case.
Costs can often impact a patient’s decision to pursue care, so facilitating an open dialogue with patients ahead of time improves the healthcare experience and can help with collections because the patient will have visibility to their out-of-pocket costs upfront. Read more
What do IT leaders think?
Healthcare IT executives are excited, yet puzzled by the potential of AI technology and how to feed quality data to the AI machines.
CIOs realize that in order to draw quality conclusions from AI queries of EHR, data first needs to be in a codified and structured format. CIOs are cognizant of the fact that we won’t realize the full potential of AI for healthcare until we clean up and organize the vast amounts of clinical data created over the last couple of decades as EHR adoption has grown.
Big challenges face innovators until the technology can link disparate clinical data and concepts and map them to standard nomenclatures such as ICD-10, SNOMED, RxNorm, and LOINC. Some CIOs express these data concerns but still are excited about AI technology innovations. Read more
Price transparency goals for next generation AI
ClaraPrice is taking on the revenue cycle challenges of complex billing and advanced technology using cleaner ICD-10 data.
Users could then easily filter pricing and DRG data organized into a structured format via AI technology in on-demand tools to accomplish price transparency goals and improve the patient finance experience.
The most impactful applications of AI in healthcare will be created by integrating AI deep into the user interfaces and workflows of hospitals, and by embedding it almost invisibly into solutions for the consumer environment. Read more