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Sometimes it’s all in the cards. And, in the end, it’s usually not a winning hand for the healthcare organization. In this case, the “card” is the patient’s insurance coverage and the “hand” is the increasing amount of bad debt that can be avoided. For example, a patient presents his or her insurance card at registration. The patient’s employer recently changed plans, and the patient mistakenly pulls out the card for the old plan. The claim is processed using the expired insurance information, and the payer rejects it and reclassifies the account as self-pay. After a time, the account goes to collections, and the patient is sent letters and receives collections calls. Both are ignored because the patient has coverage and assumes the provider simply made a mistake. Unable to resolve the issue, the provider ultimately writes the account off as bad debt. When accounts like this one are misidentified, the healthcare organization loses revenue, time and patient satisfaction. Misclassifying accounts can happen because of registration errors, changing insurance or patient miscommunication. When an account is misclassified, it increases the likelihood the account will turn into bad debt, especially when the account is misclassified as self-pay. Even when caught during the collection process, misclassification errors can impact A/R days, payment speed and cash flow. So, how do you play your cards right? Using the most up-to-date payer data, healthcare organizations can systemically search for current commercial, Medicare and Medicaid insurance coverage. An automated process reveals and prioritizes potential active coverage, allowing staff to rectify any mistakes and file claims in a timely manner. Staff can even proactively identify and correct routine data entry errors, such as incorrect birth dates or transposed Social Security numbers, before the claim is submitted. While the organization improves cash flow and productivity, there also are patient benefits. Using data to identify the right insurance coverage upfront makes patient interactions more efficient. In addition, reduced payment misunderstandings and unnecessary collections calls drive overall patient satisfaction. Curious about how your organization can have a winning collections hand? Use data and analytics to improve the accuracy of upfront business processes and enhance the patient experience. Learn about one of our newest products, Self-Pay Coverage Finder℠, and see how automating the search for insurance coverage can positively impact your organization’s bottom line and the patient’s experience.

Americans who do not currently receive health insurance through their employers or a government program such as Medicaid or Medicare are now required to obtain insurance coverage or pay a penalty tax per the Affordable Care Act’s individual health insurance coverage mandate. These consumers can go to newly created health insurance exchanges (HIX) — offered through the state or federal government, depending on where an individual lives — to enroll in a private insurance plan. It’s definitely a patient-driven process. So, how can healthcare organizations help? They can take advantage of this opportunity to improve the patient experience by connecting patients with much-needed insurance, while simultaneously mitigating patient payment risk. Today’s patients want to be armed with as much information as possible. With this new initiative, healthcare providers can help patients navigate the various options offered through the exchanges by calculating how much patients might spend on insurance and by providing a comparison of plan benefits. However, it’s important for healthcare organizations to take this process a step further by screening patients to determine if they qualify for federal subsidies and beginning the enrollment process. Assisting patients in this way not only improves the patient experience, but also benefits the hospital by getting more patients enrolled with insurance, ultimately leading to higher reimbursement for services provided. That leads to the next likely question: how can healthcare organizations successfully aid in this process? Solutions powered by data and analytics are the key. By using a data-driven approach to HIX screening and enrollment, an organization can identify patients that meet the income criteria for subsidy payments and tax credits, and automate the enrollment process by prepopulating the state’s HIX application form. In much the same way that data is used to screen for various financial assistance programs such as Medicaid or charity care, HIX screening uses key information about a patient’s unique financial situation to accurately determine if the patient qualifies for subsidies to help them pay for their insurance. Interested in learning how you can improve the patient experience when it comes to insurance coverage? Check out our newest product, HIX Screening and Enrollment, and see how it can help support your patients as they begin to navigate the new aspects of healthcare reform.

We live in a choice-based society. Every day we are at liberty to make a myriad of choices. like where we live, where our kids go to school, what to eat, whether to exercise, which car we drive or what movie to see. This era of consumerism also means we have choices for healthcare. And since patients now have greater financial responsibility for their healthcare, they are becoming more selective about where they go to receive medical attention, looking not only for high quality but also positive, cost-effective and informative interactions. As patients become choosier, healthcare organizations must improve their commitment to being good stewards of the care experience. To help make things more manageable, many healthcare organizations are turning or already have turned to payment plans to become more patient centric. To that end, industry estimates point to around payment plans being leveraged for one in five outstanding patient accounts, and use of these tools has grown by more than 50 percent in some organizations. While this information may not be news to you, the crux of this approach is that all too often payment plans take “a one size fits all” approach, following a generic formula for all patients without regard to payment history, demographic information or other key financial data. The result? Default rates between 40-60 percent, defeating the overarching goal of reducing patient bad debt. All is not lost! With a data-driven approach to developing patient payment plans, healthcare organizations avoid the common pitfall with a win-win for both patient and organization. By using technology to analyze key information about a patient’s financial situation, an organization can accurately anticipate a patient’s propensity to pay. The technology can then review this information, along with other financial data and organizational policies — such as minimum payment amounts — and generate personalized payment plans that offer optimal terms and amounts. Taking a data-driven approach is a way to extend your organization’s commitment to compassionate care to the billing and collections process. Embracing this method allows business office staff to be responsive to the patient’s unique financial situation, just as the clinical staff is responsive to a patient’s medical status. Leveraging data to develop the optimal patient payment plan helps set the stage for a positive interaction, boosting patient satisfaction and ensuring patients choose your organization as their long-term healthcare destination. Not only does this improve the patient experience, but healthcare organizations are also better able to collect optimal payment in a timely fashion, knowing what each patient can safely afford. Want to learn more about how to develop personalized payment plans that meet the unique needs of patients? We’d be happy to help. Check out our latest product addition, Payment Plan Advisor℠.

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.

Experian Health is very pleased to announce that we’ve ranked #1 in the 2025 Best in KLAS: Software & Services report, for our Contract Manager and Contract Analysis product, for the third consecutive year. Contract Manager, when paired with Contract Analysis, empowers healthcare providers by ensuring payers comply with contract terms, identifying and recovering underpayments, and arming them with real claims data to negotiate contracts. This enables providers to negotiate more favorable terms and maintain financial stability.  Clarissa Riggins, Chief Product Officer at Experian Health, says, “In the ever-evolving healthcare landscape, our Contract Manager solution has once again been recognized as the #1 Revenue Cycle Management tool by KLAS for the third consecutive year. This prestigious ranking underscores the significant value our solution delivers to our clients by identifying underpayments and facilitating revenue recovery. We are honored to continue supporting our clients with innovative solutions that drive financial success and operational efficiency.”  Learn more about how Contract Manager and Contract Analysis can help your healthcare organization validate reimbursement accuracy, recover underpayments and boost revenue.   Learn more Contact us

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