Claim-Level Revenue Prediction: Allocate Your Resources with Precise Cash Flow Numbers

Ten years ago, healthcare organizations could depend on:

• collecting 90 percent of their revenue from payers
• having year-over-year labor and supply at a rate on pace with inflation
• selling their organizations at the very end of their tenure, possibly decades in. 

Today:

• just 70 percent of revenue comes from payers and patients are proving difficult to collect from (as we explore in our upfront collections post.)
• hospital labor costs soared by 20.8 percent between 2019 and 2022 and supply expenses per patient increased 18.5 percent between 2019 and 2022. An MGMA stat poll shows that 92 percent of respondents reported that costs have continued to rise. 

• In an active merger and acquisition environment, many physician groups and practices change hands every 3 years. 

With these trends diminishing provider revenue, investors and buyers are scrutinizing books more rigorously than ever before. Physician groups and management services organizations often focus on denial management for revenue improvement, but there are more subtle ways to shore up revenue. Providers shouldn’t overlook improving contract management, upfront collections, and charge capture, as these revenue leakage points combined can also amount to significant recoveries and improved EBITDA. 

One revenue spot often overlooked is claim-level revenue prediction. Imagine knowing which of your high-value claims are at risk of denial so you can perform the extra review that catches errors and omissions. Your denials would decline and your patients would get the treatments they need faster. 

What if you could take a payer’s proposed change and run it through your system to get your own figures of how that change would impact your revenue? You’d not only have the data to back up your change rejection, but you would put the payer on notice that you won’t be rubber-stamping their changes without thorough analysis. 

The power behind claim-level revenue prediction is predictive analytics, just one of the five types of healthcare payer analytics. While the healthcare industry has been slow to adopt the revenue insights predictive analytics delivers, providers see enough value in predictive analytics to drive its market growth to a CAGR of approximately 24.4% from 2023 to 2030, according to Grand View Research. 

This article helps you make your decision about whether you’d like to help pioneer the use of claim-level revenue prediction in your physician group or stand on the sidelines until the tactic has proven itself more thoroughly. 

What is claim-level revenue prediction?

Claim-level revenue prediction is an advanced analytics process that uses historical data, machine learning, and possibly optical character recognition to forecast the likelihood of a specific claim being paid, denied, or partially paid by insurers before it is submitted. This predictive modeling occurs at the individual claim level, allowing for granular insights into expected reimbursements. It is a key element of net revenue forecasting, the system-wide financial modeling technique that projects anticipated cash inflows. 

Where claim-level revenue prediction fits into net revenue forecasting

Claim-level revenue prediction provides a more accurate and granular forecast of individual claims compared to broad net revenue forecasting. Granular insights can help team leaders better allocate resources within revenue cycle teams, cutting costs. It estimates both the amount and timing of payments for individual claims.

The detailed insights from claim-level predictions inform strategic decisions about service lines, payer mix, and operational improvements. 

Key aspects of claim-level revenue prediction include:

1. Data analysis: It utilizes comprehensive data from various sources, including patient records, billing systems, payer contracts, and historical claim outcomes.

2. Machine learning algorithms: Advanced algorithms learn from patterns in historical data to make predictions about future claim outcomes.

3. Real-time assessment: It can provide instant insights into the potential outcome of a claim before submission.

4. Risk identification: The system can flag high-risk claims that are likely to be denied or underpaid.

Benefits of claim-level revenue prediction

Given the following benefits, it’s easy to see why claim-level revenue prediction is becoming more important in healthcare revenue cycle management. 

Claim-level revenue prediction has proven to:

Improve financial planning and cash flow management

By estimating the expected reimbursement for each claim before it is submitted, organizations can accurately project future revenue, identify and prepare for any cash flow issues, and allocate resources and investments. The last thing you want to do is miss payroll or limit patient volume. 

Enhance denial prevention

Predictive analytics at the claim level can identify claims that are likely to be denied before they are submitted. This intervention sets revenue cycle staff up to proactively correct errors or missing information. Clean claims avoid costly denials and rework. 

As HFMA Senior Vice President Rick Gundling recently told Chief Healthcare Executive, “ "Predictive analytics in revenue cycle management isn't just about forecasting; it's about empowering teams to make proactive decisions that improve financial outcomes and operational efficiency."  

Optimize workflow prioritization

Claim-level predictions allow revenue cycle teams to delegate high-value or fast-paying or more specific claims to the staff members most experienced with these separate issues. Prioritized work limits administrative costs.

Improve payer contract negotiations

Detailed claim-level data and predictions provide valuable insights for payer contract negotiations. It models the financial impact of contract changes proposed by payers and supports arguments for improved reimbursement rates. This data positions providers to negotiate better contract terms.

Detect payer underpayments

Claim-level data identifies underpayments or denials by specific payers so staff can notify and recover the revenue already earned. It can break underpayments down by CPT code, provider location, provider, and payer. 

Enhance revenue integrity

Claim-level revenue prediction supports revenue integrity efforts by identifying potential coding and charge capture issues. When it finds services that are underbilled or overlooked, providers win appropriate reimbursement for the care provided. 

Accelerate cash collections

By accurately predicting when specific claims will be paid, organizations can develop follow-up strategies for unpaid claims, thereby reducing days in accounts receivable. 

All of these benefits contribute to revenue cycle optimization. 

Revenue prediction in healthcare today 

Traditional methods of claim-level revenue prediction in healthcare have typically relied on manual processes and basic statistical approaches. Staff analyzes historical claims data, applies average reimbursement rates, and makes broad assumptions about payer behavior and denial patterns. You can see why the net revenue forecasting of so many healthcare organizations can turn out wildly inaccurate.

Staff depend on their experience and knowledge of payer policies to estimate the likelihood of payment and potential reimbursement amounts. Of course, the data they draw from memory varies from staff member to staff member.  While these approaches provide some insights, they are often time-consuming, prone to human error, and lack granularity and accuracy. In today’s complex healthcare reimbursement environment, precision is paramount. Investors and buyers are combing those books. 

 As organizations strive for financial stability and operational efficiency, they are turning to advanced technologies and data-driven approaches.

The rise of predictive analytics

Predictive analytics is making its way into modern healthcare financial management. Organizations leverage it to forecast patient volumes, optimize pricing strategies, and improve population health management. It can bolster net revenue forecasting accuracy as well.

By anticipating financial trends, healthcare providers make more informed decisions and proactively address potential challenges. This shift towards data-driven forecasting fuels a strategic approach to revenue cycle management – a shift away from the seat-of-your-pants revenue management model used until the day when the EHR mandate sparked the healthcare technology revolution 15 years ago. 

AI and machine learning fuel revenue forecasting

Traditional (rather than generative like ChatGPT) artificial intelligence and machine learning are leading healthcare finance’s modernization. These technologies uncover complex patterns in vast amounts of healthcare data, far beyond what human analysts can achieve. Revenue cycle technology can now use algorithms to analyze everything from claims data to clinical notes, thanks to advancements in natural language processing. The result is a new generation of sophisticated and highly accurate predictive models that are transforming revenue forecasting capabilities.

The growing importance of claim-level prediction

Within the broader field of revenue forecasting, forward-thinking organizations are experimenting with claim-level prediction. Pioneers are innovating deep learning models to predict payers' responses to claims before they are even submitted. 

• UnityPoint Health implemented predictive analytics models to assess readmission risk scores for patients. While not directly related to revenue forecasting, this application helped them reduce readmissions by 40% over 18 months, which likely had positive financial impacts.

• Corewell Health used predictive models to prevent 200 patient readmissions, resulting in $5 million in cost savings. Again, while not specifically for revenue forecasting, this demonstrates the financial benefits of predictive analytics in healthcare.

These models aim to forecast which claims are likely to be denied and estimate payer response times. By providing this level of granular insight, these tools are enabling revenue cycle staff to prioritize their efforts, focusing on high-value denials and increasing first-pass payment rates.

Challenges in achieving claim-level revenue prediction

Despite the promising advancements in revenue forecasting and claim-level prediction, healthcare organizations face significant challenges in implementation. Data quality issues, difficulties in data integration, and concerns about data privacy and security are common hurdles. Many organizations are still in the process of developing effective net revenue forecasting processes, highlighting the complexity of this transition.

Emerging forecasting trends and solutions

To overcome these challenges and fully leverage the power of predictive analytics, healthcare providers will need to invest in data engineering and analytics technologies. Cloud computing and advanced analytics platforms are opening up new possibilities for data-driven decision-making. Additionally, specialized revenue cycle management solutions are being developed to help providers maximize collections and streamline their RCM processes.

Reinforce your revenue to afford advanced technologies  

The impact of predictive analytics solutions on revenue cycle management will be profound. With this robust technology, providers can predict the likelihood of claim denials and implement proactive measures to reduce revenue leakage. 

Still, while significant progress has been made in net revenue forecasting and claim-level revenue prediction, the space still has considerable room for growth and improvement. 

Right now, you can turn to an established solution for sweeping in revenue. MD Clarity’s RevFind contract management tool compares every actual payment to the one listed in the contract. When it locates an over- or underpayment, it immediately alerts staff. RevFind can accomplish this lucrative task because it ingests, digitizes, and analyzes all contracts. It not only uncovers individual underpayments, but it also identifies payment trends, makes terms and fees searchable, finds your best and worst payers, triggers contract renewal deadline alerts and so much more for physician groups and MSOs. 

In addition, RevFind’s contract modeling features show revenue cycle leaders how much revenue they’ll win or lose if proposed payer changes are accepted. Why not enter your own rates and terms to see how it impacts contract performance? RevFind does that too. The scenarios you can model are unlimited. 

Schedule a demo to watch RevFind uncover what your underpayments amount to, which payers owe you, and which CPT codes trigger the most underpayments. 

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