Strategic

Healthcare organizations are turning to machine learning to revolutionize revenue cycle management. Machine learning enhances workforce performance by optimizing resource allocation, prioritizing claim follow-up activities, setting competitive wage rates, designing effective performance incentives, enabling career planning, and guiding Performance Improvement Plans (PIPs). By leveraging data-driven insights, healthcare providers can improve efficiency, reduce costs, and ensure high-quality patient care in an increasingly complex healthcare landscape.

Back-office

As the realms of artificial intelligence (AI) and automation continue to evolve, their potential to redefine revenue cycle management (RCM) in healthcare is becoming increasingly apparent. However, despite these advancements, the revenue cycle best-practice methodology behind managing account inventory in health systems has remained largely static and inefficient. This creates significant revenue and cost risk, particularly in an era characterized by shifts in payer policies, workforce shortages, and loss of institutional knowledge, all of which exacerbate the challenge of overturning denials. To counter this risk, leading revenue cycle teams are adopting a more strategic approach to denials management—one that harnesses analytics in groundbreaking ways to bolster inventory workflow and optimize revenue.

Strategic

Artificial intelligence (AI) has become a buzzword in recent years, but what does it actually mean? Artificial Intelligence (AI) is a branch of computer science that seeks to create machines and computer programs that can simulate human intelligence, including the ability to learn, reason, and make decisions. Over the last few decades, AI has made significant progress, transforming industries such as finance, retail, and transportation. Healthcare is one industry that has been particularly impacted by AI, with machine learning playing a critical role in optimizing the revenue cycle management process.The revenue cycle is the process of generating revenue for healthcare providers, which includes everything from billing and coding to claims processing and payment collection. For healthcare providers, the revenue cycle is a critical function that ensures that they are paid for the services they provide. However, the revenue cycle process can be lengthy, complicated, and prone to errors, which can lead to payment delays, denials, and lost revenue.This is where AI and machine learning come into play. By automating processes and using data analytics to identify patterns and trends, healthcare organizations can streamline the revenue cycle process and reduce errors, leading to faster reimbursement times and increased revenue.

Back-office

The cost of healthcare in the United States has risen steadily over the years, leading to an increasing number of patients unable to pay for their medical expenses. This has created a significant challenge for healthcare providers, who must find ways to ensure that they are paid for their services while also ensuring that their patients have access to the care they need. One solution to this challenge is the use of third-party patient financing, which can help providers manage their revenue cycle while also providing patients with affordable payment options.

Back-office

Accounts receivable revenue cycle management is an essential function of any healthcare provider, as it directly impacts the organization's cash flow. However, managing accounts receivable can be challenging, especially when there are staff shortages. Fortunately, advances in technology have made it possible to use machine learning to overcome these staffing challenges and improve the revenue cycle management process.

Back-office

In healthcare, the accounts receivable follow-up process is an essential component of financial management. However, it is a complex and time-consuming task that often requires extensive resources, including skilled labor and technology. In recent years, many healthcare organizations have been turning to robotic process automation (RPA) to streamline their accounts receivable processes. While RPA can help to automate repetitive tasks and reduce the workload of human workers, it is not always enough to ensure accurate and timely reimbursement. This is where machine learning comes in.

Strategic

Visual analytics design is the process of creating effective visual representations of complex data in order to gain insights, communicate information, and facilitate decision-making. As the amount of data available to organizations continues to grow, visual analytics is becoming increasingly important. However, not all visual analytics designs are created equal. In this article, we will explore some best practices in visual analytics design to help you create effective and impactful visualizations.

Middle-office

The International Classification of Diseases (ICD) is a globally recognized diagnostic tool used by healthcare providers to classify and code diseases, injuries, and other medical conditions. The World Health Organization (WHO) recently released the 11th revision of the ICD, which includes new codes, updates to existing codes, and improved functionality. As healthcare organizations transition to the new version, there are steps they can take to ensure a smooth and successful transition, including the use of data analytics.

Strategic

The COVID-19 pandemic has brought about a significant shift in the healthcare industry, with a growing emphasis on telehealth services. While remote care has been available for some time, it has become increasingly important in light of the pandemic as a way to reduce the risk of transmission and ensure patient safety. However, the shift to telehealth has presented new challenges when it comes to billing and reimbursement, which has led to the adoption of data-driven revenue cycle management (RCM) solutions.