Revenue cycle myths: Realistic ways to increase reimbursement

by Experian Health 6 min read December 4, 2023

Revenue cycle myths Realistic ways to increase reimbursement-blog

Could common revenue cycle management (RCM) myths be preventing healthcare organizations from getting paid in full? Does what constituted best practice a few years back still apply to revenue cycle operations today? Many providers are embracing new technology to strengthen their RCM processes, using automations and software to create more accurate and efficient billing and claims management workflows. But if these processes are built on shaky assumptions, the results will be sub-optimal.

As year-end financial reviews get under way, there is a prime opportunity to re-evaluate some long-standing beliefs about billing, collections and payments that, if not set straight, could limit financial performance in the year ahead. This article examines four of the most common revenue cycle myths and considers what providers can do to make financial growth a reality in 2024.

Revenue Cycle Myth 1: All patients are equally likely to pay

Reality: No two patients are alike – whether in their medical needs or financial circumstances. Providers know this, yet many rely on revenue cycle management solutions that lean toward a one-size-fits-all approach to patient payments. Instead, providers should consider RCM tools that use data and analytics to segment patients according to their individual financial situation, to create a more personalized and proactive approach to collections. This should take account of both the patient’s ability to pay (i.e., whether they can afford their bills), and their likelihood to pay promptly, which may be enhanced by offering payment options that are convenient and aligned to their personal preferences.

Collections Optimization Manager analyzes patients’ individual payment history and demographic information so their accounts can be routed to the most appropriate collections pathway from the start. Patients that are likely to pay quickly can be sent billing information automatically and presented with self-service payment options. Alongside this, Patient Financial Clearance pulls together credit and non-credit data to help providers identify patients who may need a little more guidance and connect them to suitable payment plans. It catches any individuals who may be eligible for Medicaid or charity support. Staff get accurate, at-a-glance data to help them have sensitive financial conversations with patients, and can avoid losing time chasing collections from patients who would never have been able to pay.

Case study: See how Stanford Health Care improved collections with a tailored, patient-focused approach to healthcare collections.

Myth 2: It’s hard to have meaningful pre-service financial conversations with patients

Reality: Contrary to popular belief, most patients are receptive, and even eager, to have financial discussions with their provider as soon as possible. Doing so need not be challenging. In the past, providers may have worried that broaching the money question could deter patients from seeking necessary care, or simply not prioritized such discussions. Billing and insurance can also be highly complex, which may lead staff to assume that patients would find conversations about these issues to be confusing or overwhelming. But it is for these exact reasons that providers should have financial discussions with patients as early as possible.

Experian Health’s 2023 State of Patient Access survey found that almost 90% of patients wanted upfront pricing estimates so they could plan ahead for their financial obligations – yet less than a third received one. Tools like Patient Payment Estimates and Patient Financial Advisor can calculate cost estimates, taking account of the patient’s claim history, deductibles and other insurance information, and automatically send these to patients before treatment so they know what to expect. These can also be combined with quick payment links so bills can be cleared before care. Giving patients consistent information through whichever digital channel they prefer means they will be better positioned to make informed decisions and discuss their situation with patient access staff if necessary. When patients are better informed and supported, they’re also less likely to end up postponing care due to cost concerns.

And with the same accurate data at their fingertips, patient access staff can serve as financial concierges, helping patients to understand coverage and copayments and check eligibility for relevant financial assistance programs. In addition to user-friendly data tools, providers should consider whether staff would benefit from additional training to bolster their confidence in leading compassionate financial conversations.

Myth 3: It’s impossible to know what patients owe across a system with a single look-up

Reality: Thanks to data analytics and digital payment technology, it is now pretty straightforward to consolidate a patient’s outstanding balance information from across an entire health system, and debunks common revenue cycle myths. Patient access staff can view a comprehensive summary of a patient’s insurance status, estimated liability and open balances from multiple providers, enabling them to have meaningful financial conversations with patients. Even if these discussions do not lead to immediate payment, they can still act as a reminder to nudge the patient to act soon, thus accelerating the payment process.

Selecting RCM tools from a single vendor makes it easier to integrate data from multiple workflows and generate a unified view of what a patient owes. When systems talk to each other, it’s possible for a single tool to leverage the data and create a better experience for patients and staff. For example, PaymentSafe® automatically brings together data gathered throughout the revenue cycle to streamline what was previously a disjointed and time-consuming process. With point-to-point encryption, it accepts secure payments at any point in the patient’s journey, using cash, check, card payments and recurring billing, through a single web-based application.

Myth 4: Revenue cycle management is “set-and-forget”

Reality: Revenue cycle managers may dream of setting up a system once and then forgetting about it, but the reality is that managing billing, claims and collections is an ongoing and evolving process that needs constant attention. Healthcare organizations must regularly review and adjust their RCM strategies to prevent missed revenue opportunities, manage compliance risks and promote operational efficiencies.

That said, data analytics and automated revenue cycle management tools do make it far easier for providers to stay on top of RCM demands. These tools help providers with everything from monitoring payer policy changes and identifying billing errors to personalizing patient communications and generating monitoring reports. Artificial intelligence takes it a step further, for example, by preventing and predicting claim denials. In this way, these tools reduce the need for extensive staff input, so staff can spend more time focusing on the issues that need more human attention. With up-to-the-minute reports covering multiple RCM processes, staff also have the information they need to optimize performance and find opportunities to boost reimbursement that may have been previously overlooked. So, while RCM is not quite a “set-and-forget” process, automations and analytics can simplify it significantly, so it’s less labor-intensive for staff and more efficient overall.

Debunk revenue cycle myths and proactively challenge assumptions to increase profitability

Debunking these revenue cycle myths is simple and achievable with tools that integrate a patient’s clinical and financial data for a fuller picture of what that patient needs. This is crucial as changing consumer expectations, economic drivers, and new technology reshape how patients, providers and payers interact with one another. Checking underlying assumptions in any RCM process is essential to root out potential misunderstandings and outdated thinking. Not doing so leaves providers vulnerable to inaccurate financial projections, mismatched strategies and poor patient experiences.

See how Experian Health’s industry-leading Revenue Cycle Management Solutions make streamlined billing and collections a reality.

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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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