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How Artificial Intelligence Is Optimizing RCM

It is widely accepted among the practitioners that Revenue Cycle Management (RCM) has become increasingly more complex day by day due to the abundance of tagged data. However, applying AI to revenue cycle management could be the technologyu2019s biggest break in the healthcare sector. For instance, Various manual and redundant tasks that are taking place in patient access, coding, billing, collections, and denials, can be automated using AI.

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How Artificial Intelligence Is Optimizing RCM

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  1. How Artificial Intelligence Is Optimizing RCM?

  2. How Artificial Intelligence Is Optimizing RCM? It is widely accepted among the practitioners that Revenue Cycle Management (RCM) has become increasingly more complex day by day due to the abundance of tagged data. However, applying AI to revenue cycle management could be the technology’s biggest break in the healthcare sector. For instance, Various manual and redundant tasks that are taking place in patient access, coding, billing, collections, and denials, can be automated using AI. What exactly AI does? AI is imitating intelligent human behavior with the help of various algorithms that find patterns and plan future actions to produce a positive outcome. AI is slightly different from other emerging technologies such as machine learning or robotic process automation, which can identify patterns like AI but focus on improving accuracy rather than achieving positive outcomes. We know artificial intelligence is helping optimizing revenue cycle management, but how? we have illustrated below: Role of AI in prior authorization The most stressed issue in RCM is prior authorization due to its transactional nature and AI is proven to be the best use case for this in the healthcare sector lately. Currently, various cases such as needing prior authorization, submit requests to payers, and check statuses of claims are identified with the help of real-time analytics and machine learning and these technologies are are leveraging AI. Moreover, AI and robotic automation can be used to allow providers or RCM partners to auto-correct the claims and prepare any supporting documents in advance.

  3. How Artificial Intelligence Is Optimizing RCM? Real time analytics by AI AI-powered RCM helps both practitioners and patients in decision-making. for instance, Providers need to know in real-time who is cleared to be treated financially and Patients need to know what they’re responsible for paying which enables patients to choose treatment options that are good for them. AI powered RCM reduced the risk of Claims Denial RCM is a mundane job. You have to manage many processes manually which includes entering the details of every patient, write the code for the procedures and perform a quality check. As the number of patients increases operating costs also go high and you need huge manpower to manage your revenue cycle. Adoption of AI and automation can reduce your operating expenses drastically. Clean claims submissions play a crucial role in insurance submission for healthcare practices. Recent research finds that Insurance claims cost hospitals approximately $262 billion annually, and this total doesn’t include unnecessary processing costs to insurers and intermediaries. AI helps you to identify potential denials and fix them before they go out the first time. AI also helps to detect missing charges before claims are filed so that a more complete claim can be filed and paid timely. AI can even be used in place of rules-based methods that are often time-consuming and difficult to maintain.

  4. How Artificial Intelligence Is Optimizing RCM? After the penetration of AI, the handling of claim denials can now be electronically grouped to tackle claims with similar rejections for faster turnaround. Future of AI in RCM RCM space is now adopting AI as-a-service which allows healthcare organizations to outsource AI technology, which enables providers to experiment with AI for RCM without heavy upfront investments. Moreover, AI-as-a-service is focusing on offering that maintenance aspect, as well as using machine learning capabilities and learning that’s taking place across a neural network to enhance that bot. The service enables providers to engage with AI to optimize revenue cycle management and even beyond. At MedisysData, we are continuously keeping track of the application of AI in the industry and monitor the benefits from a user perspective. We have successfully built bots to automate sub-processes in Revenue Cycle Management such as Transcription, Coding, Billing, and most importantly AR & Denial Management.

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