Everything Data Thought Leadership

Back-office

In the healthcare industry, revenue cycle management is crucial for maintaining the financial stability of hospitals and clinics. One of the most significant challenges in this process is reducing the number of days in accounts receivable (AR), which is the amount of time it takes for a healthcare provider to collect payment for services rendered. Reducing AR days is essential for maintaining a positive cash flow and ensuring the financial health of healthcare organizations. Machine learning can help healthcare providers identify bottlenecks in their revenue cycle and enhance cash flow by optimizing their AR process.

Strategic

Healthcare fraud is a persistent issue in the industry that leads to significant financial losses and negatively affects patient care. With the rise of electronic health records and the increasing complexity of medical billing, identifying and preventing fraudulent activity has become more challenging. However, machine learning is proving to be a powerful tool in detecting billing irregularities and preventing healthcare fraud.

Front-office

Healthcare providers face several challenges when it comes to managing revenue cycle operations. One of the biggest challenges is the high rate of claim denials and rejections. According to a report published by the American Medical Association (AMA), the average denial rate for medical claims is around 5% to 10%. However, the cost of resubmitting denied or rejected claims can be expensive and can impact the financial stability of healthcare organizations. Therefore, improving clean claim rates is critical for the financial health of healthcare providers.

Front-office

The prior authorization process is a critical step in the revenue cycle management (RCM) process for healthcare organizations. This process ensures that patients receive the appropriate care they need while also ensuring that the healthcare provider gets paid for their services. However, the prior authorization process can often be a source of frustration for both patients and providers due to delays and denials. In recent years, healthcare organizations have turned to data-driven RCM solutions to streamline prior authorization processes, reduce delays, and ensure coverage.

Back-office

As healthcare costs continue to rise, it is essential for healthcare providers to effectively manage their revenue cycle to remain financially stable. One of the significant challenges in revenue cycle management is managing claim denials. According to a recent study, approximately 9% of claims are denied, which can lead to significant financial losses for healthcare providers. However, leveraging data, machine learning, analytics, and automation can help healthcare providers sustain and improve their denial management processes.

Strategic

As healthcare organizations seek to optimize their revenue cycle operations, there is growing interest in the use of machine learning to generate data insights. While visual analytics tools are often used to present these insights, there is a growing understanding that narrative-based communication may be more effective for conveying complex insights and driving action.

Back-office

The healthcare industry has always been complex, and one of the most significant challenges facing healthcare providers is revenue cycle management. One of the most common issues that healthcare providers face is claim denials, which can be caused by a variety of factors, including coding errors, lack of documentation, and eligibility issues. To address these denials, healthcare providers often rely on the 835/837 analysis, but is this enough?

Back-office

In healthcare revenue cycle management, credit balances are a common occurrence. A credit balance is an amount of money that a healthcare provider owes to a patient or insurance company for overpayment or over-adjustment of claims. Resolving credit balances is crucial in maintaining financial stability and compliance for healthcare providers. Traditionally, healthcare providers have relied on manual methods to resolve credit balances, which can be time-consuming and error-prone. However, with the help of machine learning and data analytics, credit balance resolution can be optimized, improving efficiency and accuracy.

Back-office

In today's healthcare landscape, one of the biggest challenges for healthcare providers is collecting patient payments. The rise in high-deductible health plans has left patients with a larger portion of the bill to pay, and as a result, healthcare providers are seeing an increase in patient responsibility for payment. This has led to a rise in uncollected debt and a need for healthcare providers to improve their collection efforts. In order to improve collection efforts, healthcare providers can leverage data to enhance patient payment predictability.