By Ritesh Ramesh, CEO of MDaudit. MDaudit were shortlisted for ‘Best SaaS Product for Healthcare‘ in The 2024 SaaS Awards.

The healthcare industry is undergoing a remarkable transformation driven by advancements in artificial intelligence (AI) and automation.

These technologies are reshaping revenue cycle management (RCM), making it more efficient, secure, and analytically advanced. As organizations strive for revenue integrity, adopting cloud-based solutions that integrate AI and automation has become essential.

Navigating the Winds of Change

The healthcare sector is experiencing both supportive and challenging trends. Post-COVID, demand is back and surging, leading to increased patient and elective surgery volumes. Health systems are still trying to be operationally efficient and make healthy margins. They are making technology investments in automation, analytics, and AI to help in this area by reducing administrative and manual tasks across their value chain. Consequently, hospital operating margins improved to nearly 3.5% in March 2024, up from less than 1% the previous year.

On the other hand, labor shortages and rising supply and personnel costs have driven up care costs by nearly 10% in 2024. Additionally, increased denial rates and a surge in complex third-party audits have exacerbated reimbursement issues, threatening the financial stability of healthcare organizations. To navigate these challenges, healthcare providers are leveraging AI and automation to analyze vast amounts of charge, clinical, and payment data. This approach helps identify billing, coding, and denial management errors, providing a clear understanding of issues that affect financial performance. By pinpointing risky providers, coders, payers, and codes, organizations can develop data-driven strategies for corrective action and achieve revenue integrity.

The Rise of Cloud-Based AI in Revenue Integrity

Merely increasing technology budgets is no longer sufficient for healthcare organizations. They must prioritize the return on investment (ROI) from these investments. This is why AI and automation have quickly become indispensable in healthcare RCM.

AI and automation tools enable rapid analysis of extensive data sets, revealing patterns and anomalies in billing, coding, and denial management. These tools provide critical insights into the providers, coders, payers, and codes that contribute to financial performance issues, allowing for effective corrective strategies.

Common applications of AI, automation, and machine learning in healthcare RCM include:

• Identifying undercoding and overcoding behaviors.
• Predicting the likelihood of claims being denied by payers.
• Applying natural language processing to clinical documentation for accuracy.
• Responding more rapidly to external payer audits to retain revenue.
• Understanding compliance risks across the billing, coding and revenue continuum.

Amid high RCM staffing shortages, AI and automation also provide a significant productivity boost. According to the MDaudit 2023 Benchmark Report, compliance and revenue integrity departments using these technologies reduced their team size by 33% while performing 10% more audit activities compared to 2022.

Healthcare providers are following the lead of payers in embracing AI and automation. These technologies are revolutionizing the insurance industry and driving cost reductions. Advanced AI-enabled denial management technology plays a crucial role in optimal billing compliance, employing a hybrid auditing approach and RCM tools for a comprehensive risk-based strategy.

Cloud-based AI platforms are increasingly integrated across healthcare organizations, democratizing data insights, breaking down communication silos, and eliminating denial-related revenue leakage. This approach enables collaborative access to actionable information, driving informed decision-making and ensuring compliance with regulatory demands.

Storytelling Data and Revenue Integrity

Data is where AI truly shines. Its ability to rapidly aggregate, analyze, and democratize large volumes of data and transform it into meaningful, actionable insights. Forward-thinking healthcare organizations recognize the power of integrated cloud-based analytics and AI tools in optimizing the revenue cycle.

AI-powered storytelling data presents trends and predictions to support decision-making, revealing the root causes of revenue cycle issues and forecasting financial targets. This narrative-driven approach empowers billing compliance, revenue integrity, and HIM/coding professionals to uncover patterns, trends, and anomalies.

For example, AI-driven storytelling data helped a 200-bed Level III Trauma Center improve revenue and establish risk-based auditing practices. By leveraging pre-built analytics and peer benchmarking tools, the health system regularly ingested charge data from claims, identifying billing compliance insights. Within a few months, the health system completed 1,000 cases and identified $182,000 in corrective charges, all while strengthening its revenue strategies.

Cloud-Based HCM in Action

Consider a Texas-based, rapidly growing physician-owned health system with multiple locations. Despite tripling in size, the health system’s corporate compliance team struggled with low billing audit volumes due to manual processes. Additionally, they faced challenges in establishing a culture of compliance and staying current with regulations.

The health system implemented an integrated billing compliance and revenue integrity platform to address these issues. This platform supported a robust hybrid auditing strategy, increasing monthly retrospective audits by 900% and prospective audits by 200%. Hospital billing DRG audits expanded from 100 to 800 encounters per month and accuracy rates improved from 78% to nearly 100%.

Other outcomes included:

• Provider accuracy improved by 22%.
• Coding accuracy reached 99.9%.
• Monthly retrospective audits increased by 900%.
• Prospective audits increased by 200%.

The adoption of a cloud-based, integrated platform allowed the health system to conduct different scopes of audits every 90 days and leverage benchmark data to compare nationally. This brought greater awareness to the corporate compliance team’s efforts and established a solid foundation for future growth.

Humans in the AI Loop

While AI excels at uncovering outliers in large data sets, it is not a standalone solution. Human intervention is essential in developing, adopting, and continuously training AI to ensure accuracy and precision.

Three areas where human involvement is crucial include:

1. Building a strong data foundation: Ensuring proper metadata, data quality, and governance is key to enabling AI to function efficiently.
2. Continuous training: AI tools require ongoing education to understand the latest regulations, trends, and priorities in healthcare RCM.
3. Appropriate governance: Human validation is necessary to ensure AI outputs are safe and accurate.

By combining AI with human expertise, healthcare organizations can optimize AI accuracy, productivity, and ROI.