Executive Roundtable | Analytics

HFMA Executive Roundtable: Pursuing a Data-Driven Revenue Cycle

Sponsored by Change Healthcare Sponsor Block
Change Healthcare Sponsor Block
Executive Roundtable | Analytics

HFMA Executive Roundtable: Pursuing a Data-Driven Revenue Cycle

By monitoring key performance indicators and employing data analytics, healthcare organizations can uncover critical revenue cycle performance opportunities. In this roundtable, several revenue cycle leaders discuss how they use data analytics and the benefits they’ve seen.

As healthcare organizations face evolving regulations, shrinking payment margins, and increasing healthcare consumerism, they are looking for ways to optimize revenue cycle performance. What many healthcare organizations are finding is that by monitoring key performance indicators (KPIs) and employing data analytics, they can uncover critical performance opportunities within front- and back-end operations. In the following roundtable, sponsored by Change Healthcare, several revenue cycle leaders discuss how they are using data analytics and what benefits they’ve seen.

How do you use data analytics to improve revenue cycle performance?

Jeff Young: Cleveland Clinic manages to a series of KPIs aimed at unearthing revenue cycle improvement opportunities. We are constantly watching for areas in which to increase efficiency and accuracy, as well as foster a positive patient experience. To that end, we have rich data sets, which include our patient billing data and insurance claims data—all of which are available for analysis, visualization, and interpretation.

Looking to the future, our focus is going to include more emphasis on predictive analytics and automated decision making. We’re currently working on use cases that employ predictive analytics to drive denials management, work queue prioritization, and patient balance collection.

Brad Cook: Presbyterian Healthcare Services has made substantial investments in data analytics, deeply embedding this function into each aspect of our operation. Our goal is to improve the patient experience as it relates to the work we’re doing in revenue cycle. A few ways in which we’re using data insights are to measure staff efficiency, monitor claims denials, guide contract management, and keep tabs on various business metrics. Because we’ve made significant progress on descriptive and diagnostic analytics, we are now moving toward predictive and optimization analytics to glean the most value out of available information. For example, we are beginning to use data analytics to proactively identify issues that may result in denied claims or patient dissatisfaction. Once we know a claim has several attributes that may lead to a denial, we flag that account in a pre-billed work queue so we can evaluate it and fix any issues. By being more proactive, we can recognize issues in advance and address them before claims go out the door.

Margaret Schuler: Using data to drive the revenue cycle is akin to a pilot using measurements to fly a plane. You can steer an aircraft through the fog as long as you have precise and real-time data. Similarly, with strong data analytics, an organization can guide its revenue cycle in the right direction, even amid constant change—capitalizing on opportunities and avoiding pitfalls. At OhioHealth, we measure everything in the revenue cycle, from beginning to end. We developed a standard reporting package that trends KPIs on a monthly basis. The revenue cycle leadership team meets each month to go over the KPIs, discussing what’s going well and what’s not, as well as the action plans we have in place to address issues. By regularly meeting, we keep data in the forefront of everybody’s minds and make sure we’re on top of any concerning developments. It’s so critical to frequently review data to see where you are and what changes you need to make. The revenue cycle is not static, and if you let performance get away from you, it can take months to recover.

To provide transparency around the metrics we collect, we use a variety of scorecards, which our data analytics team puts together. We have scorecards for our vendors, payers, associates, and so on. Plus, we have one that shows overall revenue cycle performance. Our payers receive their scorecards via secure email each month. Within these tools, payers can see how they stack up to others in terms of days-to-pay, aging, and denials—although the data are kept anonymous. The data are normalized by volume, allowing for more accurate comparisons. Our associates meet with their managers monthly to go over their scorecards. The idea is to catch problems early. If a staff person isn’t meeting his or her targets, we can provide training and continue to work with the individual to educate him or her on how to improve. Because there are 12 scorecards per year, there are no surprises during the annual performance review.

In the end, measurement is a way of life for us, and it makes any path forward objective and clear. Everyone understands what the goals are and are working toward them. This takes the subjectivity out of revenue cycle management and lets us rely on clear-cut information instead of impressions and hunches.

What are the key performance indicators (KPIs) you track? Why are these helpful?

Young: We’ve identified several “true north” KPIs that we routinely monitor, including but not limited to days receivable outstanding, incoming denials, the first-pass denial rate, bad debt, and other write-offs. Several of these are set up to be viewed daily, and some less frequently, such as weekly or monthly. Although some are comprehensive measures of past performance, others serve as leading indicators of potential changes and can point to possible challenges on the horizon.

Cook: We manage metrics from each area of the revenue cycle. For instance, within patient access, we closely measure point-of-service collections because that’s a strong indicator of how well we collect money from patients and avoid uncompensated care. We also track registration metrics, including whether preregistration occurred and how complete it was. Registration accuracy can prevent denials—and this is one of the largest areas of denials due to the sheer amount of data our registrars are required to capture. Monitoring registration metrics allows us to focus coaching with employees to ensure we’re optimizing their workflow and customer interactions.

In the middle revenue cycle, we measure many different performance indicators, but, most notably, we monitor charge lag. It’s critical that charges are entered into the system as quickly as possible to facilitate timely claims processing. In addition, we closely measure coding accuracy to ensure accurate reimbursement and compliance with regulatory rules. We also keep a close eye on the uncoded claim volume, which is typically one of the largest drivers of our discharged, not final billed. The quicker we can code our claims, the better our A/R days and cash flow.

We measure most of our KPIs in the patient accounting area—which is our central business office. This group serves as the conduit for feedback to other revenue cycle areas because they can see issues that lead to denials. Many of the KPIs we collect are interconnected, and patient accounting looks at them in tandem to be certain there’s not a problem. For example, the department watches accounts receivable and cash collections as a percent of net patient revenue. If accounts receivable is trending favorably, but collections as a percent of net patient revenue is below 100 percent, it may be an indicator of revenue leakage resulting from write-offs.

Another key patient accounting metric is the final bill not submitted. This captures the claims that have left the electronic health record (EHR) but are sitting in our claims clearinghouse. This could be a black hole for providers that are only reviewing EHR data and aren’t paying attention to their bolt-on clearinghouse systems.

Finally, one of the most important metrics to watch is contract compliance. Healthcare organizations should have a complete understanding of their contracts and whether claims are being paid according to the contracted rates. Oftentimes, payers may make changes or have configuration errors in their systems, which can result in underpaid and overpaid claims. We take a hard look to make sure we’re being paid correctly and provide feedback to our payers, which can positively affect everyone.

Jason Williams: For the most part, organizations know the kinds of things they need to measure—indicators around cost, productivity, and revenue realization. Basically, these are the things that the HFMA’s MAP Keys clearly delineate. However, it’s important to drill down into these high-level KPIs to truly learn what’s happening in an organization. For instance, is your staff uniformly contributing to revenue cycle results? In what areas do specific staff need to improve? Are all your payers paying in a timely and accurate fashion? Which are priorities for your payer relations team to work with? From a consumer perspective, how are specific service lines contributing to revenue cycle performance? Although there may not be meaningful differences in what organizations track at a high level, there is variety in how effectively they delve deeper to identify specific issues.

Are there certain areas where data analytics plays a more significant role than others?

Schuler: It’s almost cliché now, but I would say the most crucial task is monitoring and understanding denials. Denials can substantially tie up an organization’s receivables, and without a clear handle on when and why they’re happening, an organization will leave money on the table and struggle to execute long-term change. It’s also difficult to have a discussion with payers if you’re not tracking denial data or mining the root causes.

Williams: Each area of the revenue cycle can make significant contributions to an organization’s financial performance, and they all have room for improvement. To move the needle, revenue cycle departments must embrace the shared opportunity to drive improvement. However, this requires a degree of data liquidity so information can easily move between functions. It can be hard to realize change if a department doesn’t have all the relevant data. For instance, when patient access staff are trying to improve patient liability estimation to lay out expectations with patients and enhance the financial experience, they may struggle to make improvements if they don’t know if the estimates are accurate. If data about accuracy don’t come back to the front end, then the patient access team can’t tighten up liability estimation, and they may miss the chance to optimize performance. Likewise, on the back end, when someone is preparing to call a patient to collect on a balance, he or she needs to know what expectations the patient has and what estimates he or she has received. Was the patient given an estimate? Was it relatively accurate? Were there changes in care that would impact the final bill? By sharing data across and between departments, an organization can make improvements that span the entire revenue cycle.

What are some barriers to making KPIs actionable? How have you overcome those barriers?

Cook: Getting to the root cause of an issue or discrepancy can be difficult. Let’s say you see something that’s outside the normal range. The reason behind the anomaly doesn’t usually automatically present itself; you have to roll up your sleeves and dig in, and that can be a painstaking process. To identify root causes, we have created cross-functional work groups that meet weekly and investigate problems. For example, we have a work group that spans our business office and patient access team, and a work group between our patient accounting team and utilization management team. During these meetings, the groups explore issues specific to their departments because they’re the subject matter experts and can help understand what’s happening, offering realistic suggestions for improvement.

Once a group determines a root cause, it implements interventions and develops a control plan to evaluate the effect of those interventions over time. The graphical depictions of these plans can be quite telling. We may see hundreds of denials every week, and then after an intervention is in place this number may drop to zero or just a few dozen, which is exciting. Note that even after the metric improves, we still monitor it to verify that the intervention takes hold and the problem doesn’t resurface. This is so important because we may think we resolved an issue and then a payer change causes it to spike again, so we keep watching these metrics in perpetuity.

Schuler: Disparate data is another barrier. As a revenue cycle leader, I need to be able to look globally at the entire receivable, and if I can’t do that because the data are not normalized, then that can be an impediment. Normalizing data can be tricky. It requires you to bring subject matter experts together with the technology vendor and define what an apple is, so you can compare apples to apples. Because we have different technology solutions throughout the health system, we have to spend time and effort normalizing the data to compare them correctly. We have recently purchased a business intelligence tool to streamline this process.

Young: An additional challenge is driving usage throughout the enterprise; a lot of dashboards and data sources are underutilized. Sometimes there are just too many things going on, or there’s too much information available, and it’s overwhelming for the user community. We have several initiatives aimed at enhancing data utilization. For instance, one of the efforts I’m most excited about is a collaboration with our embedded continuous improvement team, where we are designing standard work around reviewing KPIs for our senior management group. The idea is to create an easy process for managers that routinely focuses their attention on those primary metrics that are most important.

Williams: Reviewing and responding to data must be a leadership priority and cultural norm. Merely having data capabilities is not enough. That’s like having a lawn mower in the garage and then assuming your lawn will automatically get mowed. Not only must organizations commit to leveraging data, but staff also need the skills and the time to appropriately apply the information. This can be tough because everybody is busy. In a lot of cases, hospitals are trying to do more things with less resources, and without leadership prioritizing data analytics and improvement, this effort can slip through the cracks.

What lessons learned would you share with other organizations that want to use data analytics?

Schuler: Don’t take your eye off the ball. If you lose sight of your KPIs, things can quickly start to fall apart. As such, it is critical for each of your stakeholders—senior leaders, payers, vendors, and associates—to have a scorecard that shows current data. You also want to make sure you have the rigor and discipline every single month to keep up with changes in KPIs.

It’s also wise to set realistic but bold performance targets. Although we define some targets based on comparisons with our peers, most thresholds are based on our internal KPI trending. We’re able to benchmark data across our 10 hospitals, and we know which ones are higher performing and which ones need to improve.

Regardless of where we pull our performance targets from, we align them across our revenue cycle senior leadership team, as well as among our directors and managers. These KPI targets are reflected in performance reviews, ensuring everyone remains focused on and held accountable for achieving the same goals.

Cook: Try not to prioritize too many metrics at once. There are probably a hundred different KPIs you can monitor, and if you try to review every KPI that exists, you’re likely to fall victim to paralysis by analysis. So, it’s important to identify the top five to 10 KPIs that affect your financial performance and customer satisfaction, and home in on those. Although Presbyterian follows more than just five to 10 metrics, we have been doing this work for a long time. We started with denials and expanded from there. Now, we can review multiple metrics because most of them are within an acceptable range, and we only have to focus on a couple that are out of range.

My other piece of advice is to never stop learning. This may require thinking about new technologies or new data sources. For example, one interesting area we’re delving into is natural language processing. We’re starting to mine information to pull out categorical issues, which will help us proactively detect customer concerns. We are hoping to use this information to boost patient satisfaction with clinical care and the revenue cycle. Essentially, this technology searches written text and/or speech and pulls out keywords. We can then see verbatim what occurred around those keywords and try to isolate a problem to be fixed. This is still an emerging program for us, but it is another area we’re exploring as we continue to search for ways to enhance the patient experience.

Williams: To be successful, an organization must never stop leveraging data for improvement. There’s often a misperception that once you achieve a certain level, you can take your foot off the gas. But in the space we’re in, things are always changing. What I’ve seen time and again is that top-performing organizations tend to be the most active. They’re always looking for something. They’re testing the benchmarks to see whether they’ve set the bar too low or whether there are further opportunities to improve. They know that the only constant is change, and to embrace that change, they must look beyond the status quo and seek out what’s next. Data analytics is much more than collecting data and monitoring performance measures over time; it’s about finding opportunities and reaching out to seize them.


HFMA Roundtable Participants

 
 
 

Brad Cook is vice president, revenue cycle management for Presbyterian Healthcare Services in Albuquerque, N.M.

Margaret Schuler is system vice president, revenue cycle for OhioHealth in Columbus, Ohio.

Jeff Young is senior director of financial reporting/analytics for Cleveland Clinic in Cleveland.

Jason Williams is vice president for business strategy and analytics for Change Healthcare in Atlanta.


About Change Healthcare

Change Healthcare is inspiring a better healthcare system. Working alongside our customers and partners, we leverage our software and analytics, network solutions, and technology-enabled services to enable better patient care, choice, and outcomes at scale. As a key catalyst of a value-based healthcare system, we are accelerating the journey toward improved lives and healthier communities. Learn more at www.changehealthcare.com.

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