For medical labs and other healthcare providers, revenue cycle management (RCM) dashboards provide a lifeline for navigating the financial complexity within the healthcare industry. Complicated patient care issues and ever-changing policies introduce a maze of potential issues to your billing process, but an effective RCM platform identifies the denials and other reimbursement issues impacting your business.
But a billing system alone only offers so many insights. It’s a terrific resource for identifying revenue trends, but it’s primarily concerned with “rear view” outputs. Even the best system only tells you what already happened, and often without enough time to resolve the problem before payment is due. On its own, it doesn’t allow your team to be proactive about issues within your revenue cycle process.
However, by achieving a clearer picture of your data, you can generate the kind of proactive analysis that empowers you to resolve billing problems faster. Better still, you can generate a view of your business that aids with better payment collections and more accurate forecasting. But you have to gain a clear understanding of your data first.
Why Is Data an Ongoing Challenge for All Types of Healthcare Providers?
Organizing all of your data (including revenue cycle data) into a single, reliable source is a constant struggle. From the moment a patient visit takes place and all the way through payment, your business collects and stores data in multiple systems. For example, a lab orders a test and collects complete insurance and patient information at the time of the order. The information is then stored within their Lab Information System (LIS), but the billing system only receives the data needed to support a claim.
However, when a problem arises and a claim is denied for missing information, your billing system has no visibility into the information stored in the LIS. To gain an accurate view of your billing process, you need to bring your operation-level data and billing data together into your RCM.
And the definitions between your data sources don’t always align. Unifying standards such Current Procedural Terminology (CPT) and diagnosis codes offers some relief. But details such as patient demographics and charge details often vary. A field may be identified by a certain name at one provider only to be called something different at another. Organizations often speak their own language, and these processes are deeply ingrained.
Further, plenty of vendors may promise their ability to organize all your data into a single environment. But it’s ultimately no different from trying to find what you need in a messy garage.
You need to ensure your data is orderly and clean before attempting analysis. If you have garbage data coming in, even the most cutting-edge analytics tools will produce inaccurate results.
Data Quality Is Essential for Medical Labs and All Healthcare Providers
For labs and medical practices, you need to unify all the details of your revenue cycle data. Your first priority in this effort is ensuring data quality for the information your RCM dashboard analyzes.
Data quality ensures your business information is accurate, complete, and consistent before analysis. You and your teams need to understand your data sources to ensure every detail of your operations workflow and revenue cycle makes sense.
For example, imagine your lab is expecting to receive 100 orders per day from a client. Data quality thresholds establish that each record should have a minimum charge of $50 for every single record. If you receive 100 records one day and the total charge is $40, your team can recognize immediately that something is wrong.
Establishing expectations for your data enables you to better troubleshoot any unexpected results. Otherwise, someone on your team may see a dramatic drop on your RCM dashboard and take inappropriate action without recognizing source data problems.
Trust is Essential to Preserving Data Quality
Ensuring data quality for your revenue cycle builds trust in your dashboard. If your team doesn’t trust the numbers your RCM dashboard is drawing from or its conclusions, they’ll simply stop using it. Then they’ll go back to manual and ungoverned processes to find their own solution.
And as your users fall back on Excel and the systems they trust, your organization now works from multiple versions of the truth. A report generated by someone in operations may exclude one cohort of orders while your finance team still incorporates all of them.
Even if you’ve effectively centralized your data, all that work goes out the window if it’s not used by everyone. To ensure the results from your dashboard remain trustworthy and adopted across your organization, you have to establish a data governance program.
Why Data Governance Requires Organizational Investment
Data governance is essential to any analytics program. Many software tools may promise to deliver the kind of data governance to support your organization’s needs. But the right software isn’t enough. True data governance can only be achieved by gathering your cross-functional teams in a room to define every aspect of your organization’s data.
Your RCM platform has to be working from a shared version of truth to generate meaningful conclusions. Consequently, you need to ensure sales, operations, and everyone else in your organization agrees on their definitions. You should have a unified standard for what constitutes a claim, a charge, and a payment, or any other detail of your revenue cycle. Once your teams are in alignment, you can adapt your RCM dashboard to develop a clearer picture of your cash flow.
You need to establish the right foundation for your business to see the benefits of an analytics program. However, the journey toward a creating centralized, governed model for your RCM doesn’t have to be as difficult as it may sound.
Reinventing Your Organization’s Approach to Analytics Can Be an Agile Process
Setting up your data to extract the full value from your RCM dashboard doesn’t have to be a start-to-finish undertaking. You can begin by focusing on a single domain or subject area in your organization and approach your analytics in a more agile way.
For example, you don't need a three-month governance project to incorporate an AI-driven analytics tool like RevenueIQ™ into your RCM dashboard. The right partner can develop an approach to tackling your data piece by piece. At Gistia, we build the foundation that merges multiple data domains to ensure high-quality data. With this in place, you’re able to incorporate AI and machine learning to enjoy the benefits of proactive analysis.
Maybe you want to start by only looking at two KPIs: Your first pass yield and your denial rate. Those two pieces are all you need to get started using RevenueIQ™ in your RCM. Then, you can go back and incorporate additional metrics such as forecasting as the next stage of your project.
By tackling this endeavor one metric at a time, your organization can gain clearer visibility into your revenue data in a way that reduces expenses and improves profitability. If this sounds like an approach that will help your healthcare organization, we should talk.