How AI Can Streamline Your RCM Process
The benefits and barriers to AI-enable revenue cycle management
Discussions surrounding AI in healthcare have really seen an uptick since the launch of ChatGPT. The chatbot forced many health professionals to seriously consider the role of smart technology within healthcare. The way AI will interact from a care perspective is still being debated, and will likely take years. However, health facilities already see its potential in streamlining the revenue cycle management (RCM) workflow.
According to the Center for Medicare and Medicaid (CMS), RCM teams spend on average 45% of their time on claims-related activities. The use of smart technology and its ability to analyze large amounts of data could see this percentage reduced, increasing cash-flow and reimbursement approvals. Additionally, nearly 9 out of 10 hospitals said they were already using AI in their RCM process in 2020.
Automize manual administrative tasks
Traditionally, the RCM process has been an error-prone and time-consuming process that results in lost revenue and decreased profitability. Automated RCM enables providers and RCM teams to offload the administrative tasks that typically delay reimbursement due to human error. This higher efficiency then decreases abandonments and sees more cash-flow.
This type of automation also allows providers to focus on delivering exceptional patient care in an efficient and cost-effective manner; benefiting the provider, patient and payer. This is the most typical use case of AI within the RCM process we see today, however, due to security concerns, this is often where AI use stops.
Identify reimbursement patterns and trends
AI’s data analysis capabilities and predictive technology allow RCM teams the ability to identify patterns and trends. The AI algorithms can analyze historical billing and reimbursement data to provide insights and predictions regarding revenue trends, reimbursement rates and patient payment behaviors. This helps RCM teams optimize revenue management strategies and improve financial forecasting.
Decrease cost and increase cash-flow
One of the benefits of incorporating AI into the RCM process is that it saves money while increasing profitability. By removing the potential for human error and, thus, any delays in the RCM process, automated RCM saves health facilities money in abandoned claims. Revenue cycle management teams can then focus on freeing up their staff for more value-added work instead of being bogged down by claims-activities.
By leveraging its machine learning capabilities and natural language processing, artificial intelligence is able to handle a myriad of administrative tasks. Some of these include:
Verifying insurance coverage
Coding medical procedures
Processing claim submissions
AI’s machine learning component also makes it perfect for predictive analytics. The technology is able to locate potential errors and flag them for review before submission. This can save hospitals and health systems from potential penalties and audits resulting from unintentional non-compliance.
Lower abandonment rates for claims
AI can analyze medical coding, documentation and billing data to identify potential issues or errors in claims before submission. It can flag missing information, coding discrepancies and potential denials, allowing staff to address these issues proactively, reducing abandonment. AI can also help in identifying patterns and trends related to claim denials, enabling organizations to make process improvement, remove inefficiencies and see greater revenue collection.
Emerging AI-driven RCM functions
It’s important to note that AI is still a relatively new and untapped technology. While hospitals and health execs have already invested in automating administrative tasks, it’s not currently an end-to-end solution. Below are some RCM functions we may see health organizations utilize more in the next couple of years.
AI-powered tools can automate the prior authorization process by analyzing clinical documentation, patient history and insurance requirements. They help determine the likelihood of approval, generate prior authorization requests and streamline the approval workflow.
Natural language processing for documentation
NLP, a branch of AI, can analyze and extract relevant information from clinical notes, physician documentation and other unstructured data. It can assist in coding accuracy, identify missing documentation and ensure proper reimbursement based on the captured clinical information.
Payment amount/timing estimation
AI-powered tools can analyze patient insurance coverage, benefits and data from the practice to provide accurate estimates of patient out-of-pocket costs. This helps RCM teams provide upfront cost estimates to patients and improves transparency in the billing process. Greater cost transparency may also reduce the amount of last-minute cancellations due to cost, resulting in better care outcomes.
The ability of AI to not only improve processes but build new ones based on data collection and machine learning is the next step in streamlining RCM workflows. As it learns your process, AI can identify weak spots and offer solutions that reduce denials and abandoned claims, increasing cash-flow and profitability.
Fraud detection and prevention
AI algorithms can continue to evolve in detecting and preventing healthcare fraud and abuse. By analyzing vast amounts of claims data, AI can identify anomalies, patterns indicative of fraudulent activities, and flag suspicious claims, helping RCM teams combat fraud more effectively.
Barriers to AI adoption within RCM
Currently, the adoption of AI technology in the RCM process is exhibiting a couple of key pain points that will need to be addressed. Revenue cycle and IT leaders are noting liability and privacy as their top concerns toward end-to-end integrations. Though, budgetary worries are nother top pain point cited by 76% of corporate executives. With any new tool or technology, it’s important to know what the liabilities are as much as the benefits.
Security concerns and slow maturity
When it comes to patient privacy, health leaders are smart to be cautious of AI integration. There are currently no universal federal regulations or standards for the use of AI in healthcare in the United States. However, existing healthcare regulations, such as those related to privacy, security and medical device approvals, apply to AI systems used in healthcare. These include regulations set by the:
Health Insurance Portability and Accountability Act (HIPAA)
Food and Drug Administration (FDA)
General Data Protection Regulation (GDPR)
Institutional Review Boards (IBR)
Due to the concerns surrounding security and liability, hospitals might be slow in maturing their AI programs. In 2020, 42% of hospitals are still in the emerging stage of maturity according to Change Healthcare.
For RCM execs, that percentage reached 80%. The study also revealed that 35% of respondents believed they would have fully mature AI usage by this year (2023). So far, there is no easily found data to confirm this prediction.
Data quality and integration
AI relies heavily on high-quality, well-structured data for accurate analysis and predictions. However, healthcare data can be complex, fragmented and inconsistently formatted, which can pose challenges for AI algorithms. Ensuring data quality, interoperability and effective integration across disparate systems can be a significant hurdle.
No universal AI revenue cycle model
AI models developed for RCM may not generalize well across different healthcare systems, settings or patient populations. The effectiveness of AI algorithms trained on one dataset or specific healthcare context may not directly translate to another. Adapting and fine-tuning AI models to specific environments or healthcare practices can be necessary.
Streamlining your reimbursement and payment process
There is still a lot unknown about healthcare AI and what it might bring to the future of the revenue cycle management. But there is no doubt that artificial intelligence will be an integral part of the future for reimbursements. As the technology continues to evolve to support some of your services, using AI as an end-to-end solution isn’t quite there yet.
Instead, offload your RCM to proven experts like OpenLoop! We provide intuitive telehealth technology customized for your business and reimbursement needs. Our end-to-end RCM service is built to improve workflows, decrease denials and offer complete flexibility from first visit to final payment.
Interested in what we can do for your organization? Get in touch here!
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