Can Artificial Intelligence Implement Administrative Waste in Healthcare?
As the healthcare sector gears for digital transformation, machine learning, robotic process automation, natural language processing, predictive analysis, and cognitive process automation are among the tools that healthcare IT leaders can employ.
Administrative waste is a soaring concern in the healthcare sector, especially as a staggering volume of patient and health data is generated on a daily basis. Effectively managing this waste can save hospitals and healthcare organizations tons of cash.
Thanks to technology, healthcare providers can take the help of myriad IT tools to reduce administration waste. These include machine learning (ML), artificial intelligence (AI), and revenue cycle data analytics among others.
How Can Modern Technologies Curb Administrative Waste and Eliminate Revenue Cycle Anomalies Eroding Hospital Margins?
Several hospitals and healthcare organizations already have set afoot on this journey. Industry surveys reveal that nearly two-thirds of them in developed countries such as the U.S. are investing in AI integration. Besides this, robotic process automation is gaining traction among technologies at dispersal for hospitals and health systems.
Focus on task automation and eliminating the risk of human errors, particularly in the space of pre-authorization, eligibility, and patient account that is followed by collection management is facilitating robotic process automation uptake.
These technologies are designed on leveraging machine learning and large data sets to enable various AI engines and bots to successfully complete more complex and advanced operational tasks. Cognitive automation is often deployed to mimic the characteristics of complex processes requirement recommendations, judgment, and analytics for actions to be taken.
AI integration and machine learning can help healthcare organizations analyze how administrative waste is preventing people and management from performing their most crucial tasks more effectively.
The focus is on training technology to perform round the clock, every day. This however is considered an extremely powerful mechanism. Machine learning and AI can be implemented to continuously contextualize and monitor operations across all systems in a hospital or healthcare organization, starting from the level of procedure diagnosis to the claims entity.
This exercise also will involve monitoring medical records coding and other functionalities to identify bottlenecks causing waste.
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