When describing the amount of revenue cycle data available to healthcare organizations, many providers use words like “massive” and “daunting.” The reality is that providers have access to more data than ever before, yet they often don’t know what to do with it. There are literally hundreds of revenue cycle metrics that healthcare organizations can collect, resulting in a volume of information that if not managed well can be overwhelming.
The key to getting the most out of revenue cycle data is making it actionable, and that is where data analytics come in.
A relatively new idea for healthcare
Data analytics, sometimes known as business intelligence, involves examining raw data to identify patterns, discover risk points, uncover opportunities and draw conclusions. Although other industries have been using data analytics for years, healthcare is somewhat new to this world, especially when applying the concept to the revenue cycle.
Recent results of a national survey of healthcare organizations, conducted by Porter Research and Navicure, found only 45 percent of survey participants used a data analytics and reporting solution to analyze their revenue cycle. Of those that didn’t have a solution or were not looking for one, 36 percent didn’t think they needed it, and nearly 50 percent didn’t have the necessary time, budget or resources to employ such a solution. Those organizations using a data analytics solution place value on revenue cycle analytics. According to the survey, 73 percent of respondents viewed revenue cycle data analytics to be a top priority.
The benefits of data analytics
There are a number of reasons why organizations might find data analytics valuable when assessing financial operations. First, they can identify chief revenue drivers. Consider the example of a small Midwestern specialty physician practice that wanted to pinpoint its main sources of revenue. Using data analytics, the organization was able to identify the top 10 CPT codes and how much revenue each code brought in. This allowed them to see not only areas of excellence but opportunities for improvement.
Data analytics can assist organizations in enhancing efficiency with payer reimbursement. Perhaps there are delays in submitting claims because charges are not arriving in a timely fashion, or maybe there are opportunities to send out cleaner claims to prevent denials and back-end rework. Data analytics can uncover these issues, helping organizations tighten their revenue cycle processes.
Strategies for making data actionable
One of the challenges with data analytics is that organizations have a hard time knowing where to start developing this kind of program. The following are three key strategies to keep in mind.
Make a plan. Data analytics is not something one staff person can dabble in on the side. Fully leveraging business intelligence requires an organization-wide commitment, especially from senior leadership. Leaders need to formally commit the time, budget and resources to the project, and they must be willing to pursue the opportunities that emerge from the data. They should develop a plan for how the organization will use data analytics and define the expectations of the effort. For example, they should determine if the organization is trying to improve claims accuracy, reduce denials and/or decrease days in A/R. Having a strong plan that includes manageable goals can ensure the project stays on track.
Leverage technology. An automated data analytics and reporting system can seamlessly communicate important information and highlight areas of focus. Here are some key features to look for when reviewing different options:
Review reports regularly. Organizations that engage with their data analytics solution on a regular basis are more likely to see benefits from the program. By reviewing reports daily, organizations can identify and resolve problems in real time. Stakeholders should also meet weekly to discuss larger trends that require long-range improvement plans, making sure that any initiatives are in line with the organization’s strategic goals.
Taking the revenue cycle to the next level
Setting up a data analytics program can help an organization get the most out of its revenue cycle. For example, the Midwestern physician practice mentioned before was able to reduce its charge lag by 37 percent and decrease days from claims submission to payment by 34 percent. These changes led to better cash flow for the practice and a healthier revenue cycle. Organizations that embrace this kind of program can not only improve efficiency but also drive accuracy, resulting in a financial operation that is ready for the future.
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