How data-driven patient access can eliminate healthcare denials

by Steven Thiltgen 5 min read December 3, 2019

Recently I had the opportunity to present at a regional chapter of the National Association of Healthcare Access Management about the growing need for business intelligence to improve patient access functions, as well as revenue. In speaking with attendees, it became clear that automating the patient access workflow with real-time data can create a more efficient and accurate process.

Here’s how.

As the responsibility for paying healthcare bills increasingly falls to patients themselves, patient access can make or break the revenue cycle. From registration and verifying insurance details, to scheduling appointments and collecting cash payments—this is the front line for the financial side of the patient experience.

When you consider that half of denied claims occur earlier in the revenue cycle at the point of registration, improving those early-stage patient access processes is the obvious place for providers to look when seeking to minimize lost revenue.

Revenue loss in patient access is mostly due to errors in patient identification, inadequate data analytics and inefficient workflows. If front and back office teams were better connected and able to work together quickly to communicate and resolve issues, many of these errors could be prevented. Without reliable tools and workflows to support this, those teams often must resort to manual fixes for any errors that arise. Unfortunately, this takes time and effort, blocking opportunities to find new ways to improve decision making and business performance.

Healthcare is becoming more competitive. Providers must work to leverage the right data in the right way to safeguard profits and offer a better patient experience. That said, where should you start?

Doing more with less requires the right data insights

There are two sides to the solution: first, you need to be sure your data is accurate from the start. Around a third of denied claims are caused by inaccurate patient identification, while 12% of patient records are duplicates. Cleaning up your data with high-quality demographic data can help eliminate preventable denials.

Secondly, you need to be able to draw insights from your data to help make smarter decisions in the future. Let’s say you notice a spike in late payments from a certain population. Why is that? Looking at historical data on patient and payer behavior can point to emerging trends and help you figure out where to focus your efforts in response. Or perhaps you’ve recently added a new function to your patient portal. Analytics can help you see if and how patients are using it and evaluate its overall performance.

Once you have your data and analytics in place, you can start to use it to make improvements. Automating the patient access workflow with real-time data can create a more efficient and accurate process. It will also help link front and back office staff with shared systems that minimize errors and wasted staff time.

3 ways to use data analytics to streamline patient access

For providers looking to streamline their early revenue cycle processes using the power of data, three areas to focus on are:

  1. Creating a better patient experience

Increasing numbers of self-pay patients means patient loyalty is a growing priority for providers. Creating a positive, straightforward patient financial experience is essential for hospitals and health systems looking to reduce the stress and anxiety many patients feel when dealing with healthcare bills. Using data insights to identify the sticky parts in your patient access processes can help you spot opportunities to improve the consumer experience. For example, are patients receiving duplicate communications because the system is failing to update demographic information? Are there bottlenecks or backlogs that are creating stressful delays for patients?

A business intelligence tool such as Revenue Cycle Analytics can help you pinpoint the root cause of delays and errors so you can work to fix them—and level-up your patient experience.

When Martin Luther King Jr. Community Hospital (MLKCH) realized patient registration in their busy Emergency Room was a bottleneck and source for claim denials, they implemented an automated platform to streamline their registration process and improve the data being captured at the point of registration. Lori Westman, patient access manager at MLKCH says:

“We get fewer denials because we’re getting true verification data, and our patient volumes continue to increase. So the fact that we can take off two to three minutes, at least, on half of our registrations is speeding up the work for the team, and the turnaround time is much better for the patients.”

  1. Uncovering potential revenue loss

Analytics can show you exactly where your revenue cycle is losing money. Using appropriate benchmarks and custom KPIs, you can analyze accounts across the entire cycle to make sure your existing revenue cycle solutions are performing optimally and identify new opportunities for improvement.

By gathering together multiple data streams into a single dashboard, you’ll get an at-a-glance view of your revenue cycle performance, so you can drill down to the root cause of denials. This also helps link up your front and back office staff. Rather than working retrospectively to address issues as they happen, your back office team can use insights from whole systemdata reporting and analyticsto give front office staff immediate feedback on where denials are occurring.

  1. Monitoring payer rules and performance

With American hospitals footing the bill for more than $620 billion in uncompensated care over the last two decades, it’s vital to verify a patient’s insurance options as soon as they set foot in the hospital. With up to date information on payer rules and a robust process for finding missing coverage, you can avoid protracted negotiations with payers and focus on denials, rejections and exceptions.

A payer dashboard can also help you assess how payers are performing against one another, so your discussions around timely payments will be based in fact. By analyzing performance around pre-service, point of service and post-service, you’ll be better placed to work more closely with payers to minimize the risk of both late payments and denied claims.

Learn more about how data analytics and an automated patient access workflow can help eliminate costly denied claims, boost revenue cycle performance and improve the patient financial experience.

Steven Thiltgen is Director of Analytics Consulting for Experian Health

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Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

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