Beyond Static Billing Reports with RCM Analytics

The only 'insight' mechanism available to healthcare organizations from the very beginning and still widely used today is 'the report.'

But, as our organizations mature and prosper, the same old reports we have counted on for decades are no longer sufficient. 

Looking specifically within healthcare revenue cycle, another variable must be considered: time. The typical cycle for an RCM leader to 'see' the issues impacting their AR is roughly 90 days, and the feedback loop is with finance. 

This means that finance finds out first why something's off, leaving RCM to seem like the culprit.

And that, my friends, is why some things need to change.

Your mission: Optimize the revenue cycle and enhance financial health

After patient care, financial health is the other key priority for healthcare leadership. Whether your billing is in-house or outsourced, you always look for ways to improve financial performance and reduce costs. You are doing your best to make informed decisions on allocating resources and managing your RCM as a business.

But to make things even harder, you need to stay compliant with ever-changing policies and regulations while working tirelessly to keep operations running smoothly. Regardless of your best efforts, it's like 3d chess with time constraints.

In this article, we'll discuss how to move beyond surface-level static billing reports and understand what options are available to you as a replacement.

The Limitations of Static Billing Systems

Typically, someone in your position constantly asks for billing reports, and you often have to rely on resources who are juggling multiple priorities. This usually delays getting the data you need to make critical decisions.

When you do receive the data you asked for, at first glance, these RCM billing reports may seem to provide valuable data points. However, remember, you probably asked for a report because finance pointed out something's off. This means that you are trying to decipher what happened 30/60/90 days ago. And that defeats the whole purpose. Now you're in reactive mode, trying to find out which groups of claims you need to try to appeal before your appeal window expires.

Which in turn highlights two problems: 1) you're looking at what happened only from a billing system's perspective 2) you're looking at past events that do not show what is happening right now 3) you're now in reactive mode, taking you from any strategic forward-looking 'preventive' strategies

And the most critical limitation is... Typically, these reports are extracted from your billing system only. As you know, claims are 'built' from 'order data,' and this order data is put together in the upstream workflows... that is a problem because, typically, these 'upstream' systems, like the EHRs or the LIS, are not under your control. So now you have to deal with other business leaders' time and priorities to get the data you need to drive strategic 'prevention' and avoid being caught flat-footed.

So, you're not only dealing with the same complexity from manual 'billing' reports only but now pulling data from multiple systems makes it even harder. 

If you're nodding, you've already taken the first crucial step: recognizing that your current billing system reports aren't giving you the whole picture. 

Simply put - static reports are holding you back. 

From Static Reports to Timely Actionable Insights

The basic concept of analytics is commonplace in all industries. 

It's a basic building block of any marketing operation. You don't have to go too far to look at examples, such as Google Analytics. This provides marketers with real-time information to understand how their campaigns are performing (without mentioning the unlimited applications in other sectors)

But for some reason, in the healthcare space, and I imagine it has to do with the complexity of the data itself, we've been slow to adopt this concept. 

The difference in the concept is massive, though. Imagine of the idea of 'analytics' as a service that 'tells you' what's going on right when things are developing. 

To illustrate my example, here are some typical use cases of what analytics applied to the revenue cycle could do for you as a leader:

• Receive alerts when denials are trending up

• Receive an alert when a specific service line is having issues

• Be able to 'on demand' analyze data from multiple systems (without having to ask for reports)

• Be able to monitor your back-end billing efficiency

• Identify what service line is being reimbursed at a better rate by state, by payer, by provider

And the opportunities are endless. These are just some examples of what analytics can do, that manual static reports are just not setup to do.

Dashboards alone are NOT analytics

Hold on. Yes, analytics use 'dashboards' to present relevant data to the analyst. However, a dashboard on its own is completely useless. It is just a visual representation of some data points, and unless that data is structured in a way that drives meaningful insight, then the dashboards are useless.

This is a persistent trap we see our clients fall into. Often, they share their PowerBI or Tableau dashboards with me, only to then say, "But I need a report from the billing system to verify that the data is accurate."

See the problem?

How do we achieve accurate RCM Analytics?

This can get very complicated quickly. Since this article is written for someone in a leadership role (not a technical role), we will stick to the high-level concepts. You can rely on your IT team to help build an actual analytics program (and we can help, too!).

Garbage in, garbage out

The first concept we need to grasp is that good data is essential for achieving accurate RCM Analytics. This sounds simple, but it's not. 

First, you need a system that can collect and store all of your revenue cycle data in one place. This includes everything from patient demographics to insurance information, claims data, payments received, and more.

The data must be accurate and in its 'dormant state'; there shouldn't be a need to manually import it into Excel and run pivots to' clean' it.

If anything, there could be an ETL-based process that normalizes, cleanses, and transforms the data into a format that can be used for analysis. Second, you need to have a way of ensuring that the data is accurate and up-to-date. This means having a system in place that can automatically validate claims, identify errors or discrepancies, and notify you when something doesn't look right.

There's a lot more to this, but establishing a data quality program ensures that the information your Revenue Cycle dashboard analyzes is reliable and trustworthy, which is critical to efficient decision-making.

Implement Agile RCM

Agile is not a new concept, but we are making sure the organizations we work with embrace it. As we say, RCM is supposed to be a 'cycle,' but it's treated linearly.

While Agile RCM doesn't directly enable revenue cycle analytics on the surface, implementing an Agile RCM program forces your organization to think differently about how to operationalize RCM and 'forces' a true analytics platform to be implemented in order to close the feedback loop that drives iterative improvements within a specific time window.

In other words, you are looking to 

1) Baseline (create an initial performance baseline) 2) Build (implement changes to your RCM workflows) 3) Measure (with Analytics, measure the output vs your expected hypothesis) 4) Learn (identify what drove the change) 5) And 'build again,' and so on. 

To implement Agile RCM:

1. Define your goals and objectives (including a time window)

2. Identify the data you need to measure progress toward those goals

3. Collect that data in a consistent way

4. Analyze it to generate insights that can be used to make better decisions

5. Take action based on those insights

Agile RCM also forces us to focus initially on a single domain or subject area and gradually expand to other areas. This reduces the risk of potential setbacks, helps build momentum, and secures stakeholder buy-in.

Looping in AI

Once the basic data foundation is laid, you can leverage advanced analytics capabilities powered by artificial intelligence and machine learning. But not before. Otherwise, it's just like adding fuel to the fire of 'bad data in, bad data out.'

The goal when we implement AI (specifically, machine learning) in these situations is to enable a shift from reactive, retrospective analysis to proactive insights. A properly trained AI model can identify changes in patterns and bring in a human at the right time. The objective is to give you an upper hand by allowing you to catch things before it's too late, and if we take it far enough, it will enable you to predict and solve problems before they impact your bottom line.

It's much better than relying on Joe to run reports out of your production system, right? 

Getting Started with Data-driven Analytics RCM

The good news is that the path forward doesn't have to be overwhelming if you struggle to gain reliable insights from your revenue cycle data. 

Let's discuss how you can leverage your existing resources (although Gistia can help!)

Your team's expertise is invaluable. They can do this without a lengthy, costly overhaul. Here's how to get started:

1. Take an agile, incremental approach: Focus on a few key metrics initially, such as first-pass yield and denial rates. This allows your team to gradually build confidence and expertise.

2. Establish strong data governance. Unify your separate data sources to create a trustworthy base for analytics.

3. Use advanced analytics. As your team gets more comfortable, add AI and machine learning (you might need 3rd party help here)

4. Expand strategically. First, get a good view of critical KPIs. Then, grow your analytics program to include more areas, like forecasting and predictive modeling.

So that's it. We covered the five steps of building an analytics program. It may seem like much work, but it's worth it. 

By following these steps, you'll be miles ahead of the industry standard. You'll help your organization make better decisions and sleep better at night, knowing your RCM performance is being watched 24/7.