Artificial Intelligence (AI) is making significant changes in healthcare, from helping detect lung cancer and identifying gene mutations associated with autism to addressing social determinants of health and managing chronic conditions. The technology is also being used to automate and optimize clinical workflows, contributing to improved outcomes, reduced costs, and greater satisfaction among both patients and healthcare providers.
The increasingly important role of Artificial Intelligence in healthcare is also prompting some healthcare providers to consider how the same technology can streamline and optimize other manual yet critical processes within healthcare, including revenue cycle management.
Artificial Intelligence and Healthcare Revenue Cycle Management
Revenue cycle management involves numerous processes that require healthcare organizations to collect, organize, analyze, and act on large amounts of financial and claims-related information.
Accounts receivable (A/R) management is a particularly important part of the revenue cycle. However, managing A/R effectively can become difficult when large amounts of data have to be reviewed and processed manually.
There is a significant amount of information associated with medical accounts receivable. Capturing all of that information, structuring it properly, and turning it into meaningful and actionable insights can be a major task.
Analysts and revenue cycle staff often need to pull and review large amounts of data to make informed decisions. This manual effort can make it difficult for organizations to understand exactly how effectively their employees are managing outstanding accounts.
A/R management remains a critical component of the revenue cycle. At the same time, many revenue cycle processes continue to depend on manual activities that can be costly and time-consuming.
Challenges With Manual A/R Management
In a traditional A/R management process, representatives may work through lists of claims that are segmented according to a particular payer.
This approach can become inefficient when representatives are unable to complete their assigned claim lists before a new aging report is generated. At the beginning of the next reporting period, representatives may have to start the process again with another list of outstanding claims.
As a result, some claims may not receive consistent follow-up, while representatives continue working through large volumes of accounts.
This type of process can potentially result in lost revenue because of inconsistent follow-up and inefficient use of staff time.
Recognizing these challenges can make it clear that improvements are needed in processes that have a direct impact on the financial performance of a healthcare organization.
Technology can play an important role in streamlining these largely manual processes. Automated workflows and analytics can help organizations better understand their A/R and improve the way outstanding claims are managed.
Applying Artificial Intelligence to Claims and A/R Management
Claims are an ongoing part of the healthcare revenue cycle, and healthcare practices need effective strategies for managing unpaid claims in a timely and efficient manner.
A solution that uses criteria-based algorithms can help automate the A/R management process and support improvements in both short-term and long-term workflows.
Manual processes often involve significant variation. The way a representative works through a list, processes an aging report, determines which claim should be addressed first, and manages the available workload can vary from one person to another.
These differences can make the overall process less consistent.
An automated workflow can help minimize this variation by establishing specific criteria for determining which claims require attention.
Rather than having representatives spend time working on claims that do not require immediate follow-up, the system can use predefined criteria to direct them toward the appropriate accounts.
Prioritizing Claims Based on Specific Criteria
An automated A/R workflow can prioritize claims based on specific factors.
For example, representatives may be directed toward a high-value claim because of the amount of money involved. A lower-value claim may also receive a high priority when it is approaching a time-sensitive filing or other deadline.
This approach allows the value and timing of individual claims to be considered when determining which accounts should be addressed.
Instead of treating every outstanding claim in exactly the same way, the workflow can help representatives focus their attention according to the criteria established within the system.
This can make the A/R follow-up process more organized and consistent.
Using a Guided Workflow for A/R Follow-Up
An automated workflow can also simplify the process for revenue cycle representatives by providing them with a clear path for completing their daily A/R follow-up activities.
The system can function much like a GPS for the representative. After logging into the workflow system, the representative can begin working through the claims identified by the system.
This reduces the amount of manual thinking and prioritization required from the representative because the system determines which claims should be addressed according to the established criteria.
Instead of manually reviewing a large aging report and deciding which account to work on next, representatives can follow the workflow provided by the system.
This approach can help create greater consistency in the way A/R follow-up activities are performed.
Early Results From Automated A/R Management
The implementation of an automated workflow and analytics solution can produce measurable improvements in A/R management.
In one example, overall accounts receivable decreased by 15 percent, while monthly cash collections also increased after the solution had been implemented for only a few months.
The longer-term benefits can extend beyond the initial improvements.
As the system continues to analyze A/R follow-up information, it can use that data to guide future workflows. The algorithm can continuously examine the characteristics of claims and identify patterns associated with different follow-up outcomes.
This ongoing analysis allows future workflows to become more informed by the data collected from previous A/R activities.
Continuously Improving A/R Workflows
An algorithm can continually analyze A/R follow-up data to determine which characteristics of a claim may lead staff to pursue more aggressive follow-up and which characteristics may result in a smoother follow-up process.
As this information is analyzed, the workflow can use the findings to help direct future A/R activities.
This creates a process in which the system continuously evaluates available information and uses that information to support more effective workflows.
The result is a more data-driven approach to A/R management rather than relying entirely on manual decisions.
Managing the Human Component of the Revenue Cycle
Automation and analytics can also provide greater visibility into the human side of revenue cycle management.
The system can help managers understand how effectively representatives are performing their follow-up activities by showing information such as how many claims they work on during a particular day or even within a particular hour.
This level of visibility makes it easier to understand what is happening within the A/R process.
For example, managers can examine why claim follow-up may not begin until a certain time of day or why activity may decrease later in the day.
These insights can provide opportunities to ask questions, identify potential barriers, and better understand the workflow.
At the same time, greater visibility can also help organizations recognize and acknowledge employees who are performing their work effectively.
Improving Staff Workflows
Greater transparency into workforce activity can help identify barriers that may be affecting performance.
Managers can use this information to better understand existing workflows and provide employees with appropriate coaching to improve the way they perform their A/R activities.
More efficient workflows can also help healthcare organizations manage their existing workforce more effectively.
Instead of immediately adding additional full-time employees as the organization grows, improving workflow efficiency can help existing staff handle their responsibilities more effectively.
Artificial Intelligence and the Future of Healthcare Revenue Cycle Management
Artificial Intelligence is making significant advances in clinical healthcare, but its applications also extend to the financial side of healthcare.
Revenue cycle processes involve large amounts of data and numerous manual activities. Applying AI, automated workflows, and analytics to these processes can help healthcare organizations streamline the way claims and accounts receivable are managed.
By automating the revenue cycle, healthcare organizations can improve the efficiency of financial processes while giving staff more structured workflows and better visibility into their activities.
Ultimately, these technologies can help healthcare providers devote more attention to what remains at the heart of their work: patient care.