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What’s the Satisfaction Level of AI in the Healthcare Revenue Cycle?

Satisfaction level of AI in Healthcare

In a transformative leap towards innovation, most U.S. hospitals are set to revolutionize their revenue cycle management by embracing the widespread adoption of Artificial Intelligence (AI). Within a remarkably short span of three years, nearly all hospitals in the nation are gearing up to implement AI in the Healthcare Revenue Cycle. This strategic shift promises to streamline operations, enhance efficiency, and optimize financial outcomes. By leveraging the power of AI, healthcare institutions are poised to unlock new levels of accuracy, speed, and data-driven decision-making, marking a significant milestone in the healthcare industry’s journey toward a smarter and more prosperous future.

Research Summary

A recent study by Change Healthcare found that by the end of 2023, 98% of healthcare leaders anticipate using AI in RCM in some form.

Healthcare facilities already using AI in the Healthcare Revenue Cycle are working towards improving the entire revenue cycle management from medical coding to payer payments and cash flow. They are prioritizing certain processes to automate which include eligibility verification (72%), patient payment estimation (64%), prior authorization (68%), payment amount/timing estimation (62%), and denials management (61%).

According to the report, healthcare leaders’ satisfaction with the use of AI in RCM varies significantly by role. RCM decision-makers are the most satisfied, with 78% reporting satisfaction. However, only 25% of corporate leaders and 46% of IT leaders are satisfied with AI in the Healthcare Revenue Cycle.

The major aspect encouraging people to invest in AI is the existing challenges in healthcare RCM. The healthcare revenue cycle is a complex and challenging process, and there are many factors that can contribute to challenges in healthcare revenue cycle management.

Some of the most common challenges in healthcare revenue cycle management include:

  • Billing and coding errors. Billing and coding errors are one of the most common causes of revenue leakage in healthcare. These errors can occur for a variety of reasons, such as incomplete or inaccurate documentation, incorrect coding, or errors in the claims submission process.
  • Slow medical claim processing. The time it takes to process claims can have a significant impact on revenue cycle management. If claims are not processed quickly, providers may not be able to collect payments in a timely manner, which can lead to financial problems.
  • High patient deductibles and copays. As the cost of healthcare continues to rise, patients are increasingly responsible for paying a larger portion of their healthcare costs out of pocket. This can make it difficult for patients to pay their bills, which can lead to bad debt for providers.
  • Changes in healthcare regulations. The healthcare industry is constantly changing, and these changes can have a significant impact on revenue cycle management. For example, new regulations may require providers to change their billing and coding practices, which can lead to delays in claim processing and revenue leakage.
  • Limited access to skilled staff. The healthcare industry is facing a shortage of skilled staff, and this can make it difficult for providers to find qualified billers and coders. This can lead to delays in claim processing and billing errors.

These are just some of the challenges that healthcare providers face in managing their revenue cycle. By understanding these challenges, providers can take steps to improve their revenue cycle management and ensure that they are collecting the revenue they are owed.

Overall, the satisfaction level of AI in the healthcare revenue cycle is high. As technology continues to develop, we can expect to see even greater benefits from its use.

While people realize the power and benefits of AI, they are increasingly looking for reliable AI technology providers. MEDICODIO is a promising provider of AI-powered medical coding technology, offering a high-accuracy, efficient, and cost-effective solution.

MEDICODIO offers an innovative alternative to CAC technology. Our automated medical coding solution, CODIO uses AI to fully automate the coding process. CODIO’s engine reads and understands both structured and unstructured text in medical records to accurately identify the patient’s visit details and assign the appropriate codes with confidence.

The engine follows a multi-step approach, including breaking down sentences, relating them for context, compiling chart sections, and applying coding guidelines for accurate code assignment.

The tool uses NLP to identify relevant information in the record, such as the patient’s diagnosis, the procedures performed, and any other relevant medical terms.

The tool then uses machine learning algorithms to assign medical codes to the identified information.

Organizations interested in AI and seeking cost-effective efficiency gains in their medical coding can explore MEDICODIO’s ROI calculator to understand how the automated coding solution works.

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