The focus on leveraging modern technologies in the healthcare industry has traditionally been towards clinical research, remote diagnostics, and data management. However, as the industry grows, equal focus is necessary on the application of technologies in revenue cycle management. According to a recent study, the annual health spending in the US will add up to $4.3 trillion by 2023. Human errors and process inefficiencies in revenue cycle management can be a real drain on the profitability of healthcare providers.
Artificial Intelligence (AI) lends itself very well in high-transaction environments driven by business logic. More than 9 in 10 leading businesses have ongoing investments in AI, whereas more than half the businesses have reported a boost in efficiency after having implemented AI. Similarly, in the healthcare industry, AI-enabled automation will revolutionize revenue cycle management for healthcare providers leading to improved operational efficiencies, faster revenue realization, and enhanced cash flows.
Predicting potential denial
A great amount of time and cost can be saved if a potential case of claim denial can be identified at the provider’s end before the claim is submitted. Providers can leverage Machine Learning (ML) to analyze historical rejection data to identify patterns associated with rejections. AI can help to bring more predictability and efficiency in claims processing. Automated notifications can be sent to relevant people in the system to prevent claim denials and achieve faster throughput.
Enabling corrections and adjustments
AI doesn’t just stop at flagging a claim as potentially problematic. It can also be leveraged to find the root cause for a possible rejection and suggest necessary corrections. The staff can then analyze this input before taking appropriate actions. A claim corrected before it reaches the insurance company can save a lot of time and money for the provider and help in faster revenue realization.
Improving claim quality & correctness
A significant number of claims are rejected due to human errors. Consequently, improved claim quality and correctness can make a huge difference to the profitability of healthcare providers. Guided by pre-programmed clear logic, AI can be highly effective in detecting errors in claim forms. This improves staff efficiency and helps in significantly improving their claim acceptance ratio.
AI for analyzing end-to-end revenue cycle management
As the implementation of AI grows for revenue cycle management, it will keep building a data model that can be further leveraged to empower staff efficiency for providers and payers. AI can be used to analyze historical process data related to billing, claims, collections, etc. Such an analysis can be leveraged for improving the end-to-end processes, leading to resource specialization and improved operational efficiency.
AI can be leveraged to predict patient payment behavior based on previous bills data, demographic data, payment methods, and so on. AI can notify healthcare providers to raise early warnings about a patient’s likelihood of defaulting on a payment plan.
The foundation of AI-enabled automation for revenue cycle management is a good data analytics platform. Going further, to leverage such a platform effectively, organizations need data experts who will orchestrate the system and its effective implementation. Using AI-enabled automation for revenue cycle management is an endeavor that will directly impact the bottom line of healthcare providers through improved operational efficiency, minimized claim rejections, and faster realization of revenue, and eventually a better patient experience. In the future, AI is going to be at the center stage to bring together clinical data with administrative and payment data for healthcare providers. This is the revenue cycle management of the future.
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