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Compared to other industries, healthcare tends to be more resilient to economic turbulence. But the weight of the pandemic, labor shortages, rising costs and increasingly complex reimbursement structures are squeezing hospital margins. A Kaufman Hall National Hospital Flash Report in July 2023 found that many hospitals underperformed, and the gap between high-performing hospitals and those struggling continues to widen. Providers must find new and effective ways to improve revenue cycle management, should any new uncertainties emerge. With pressure mounting to increase efficiency and reduce expenses, more providers are turning to automation and artificial intelligence (AI) to eliminate unnecessary manual work and optimize revenue cycle management processes. For example, Stanford Health Care leveraged automation to reduce their cost to collect. Banner Health improved patient collections with transparent price estimates. Schneck Medical Center zeroed in on claims management and incorporated AI to reduce denials. In the face of a cashflow crunch, healthcare providers increasingly turn to data-driven revenue cycle management (RCM) strategies that span the entire patient journey. This article lists six of the most effective income-generating digital RCM strategies that providers are using to maximize profits. Building blocks of a healthy revenue cycle At its core, revenue cycle management is about ensuring providers are fully reimbursed for the care they provide. The true ROI is much broader – efficient financial and administrative processes for patient billing, claims management and collections contribute to better care, satisfied patients, high-performing staff and good financial health. Realizing these benefits calls for revenue cycle processes built on three principles: Efficiency – streamlining processes to reduce resource utilization across the entire billing cycle Accuracy – ensuring all patient and claims data is correct and complete to avoid denials and delays Transparency – giving patients, providers and payers relevant and timely information, so they can act with confidence in each financial transaction. To achieve this, providers are moving away from slow, costly manual systems. Digital RCM tools are becoming non-negotiable. 6 data-driven strategies for effective revenue cycle management 1. Increase efficiency in patient access Revenue cycle management starts when the patient books their appointment and ends when the final bills are settled. Claim denials and delayed payments often arise from data errors and miscommunications in the early stages of the patient journey, which means patient scheduling and registration processes are critical to streamline RCM. With automated, data-driven patient access tools, providers can simplify tasks across the patient journey, so patients can move from one stage to the next with as little friction as possible. Fewer errors mean delays and disappointment are more easily avoided. Automated registration and online self-scheduling can also lead to savings through more efficient use of staff time and reducing the number of appointment no-shows. Experian Health clients find that online tools allow them to make relatively minor adjustments to their workflows, with a major impact on productivity. 2. Deliver accurate and timely patient billing Patients want the payment process to be as painless as possible. In multiple surveys, Experian Health has found that patients are worried about the cost of care, while 63% of providers believe patients frequently postpone care because of cost concerns. Clear, comprehensive estimates, billing and collections practices can make it easier for patients to navigate their financial journey. And with the end of continuous Medicaid enrollment, it's likely that more patients will find themselves unsure of their coverage situation, and in need of greater support to manage the financial process. For Stanford Health, the key to improving revenue cycle management centered around patient billing and collections. To achieve the dual goals of improving the patient experience and increasing collections, they used data-driven insights and automation to remove uncollectible accounts, prioritize accounts with a high propensity to pay, find missing coverage and reduce the manual workload. Collections Optimization Manager helped Stanford Health identify the best possible collections strategy, by scoring and segmenting patient accounts with the highest propensity to pay. Coverage Discovery® supplemented this strategy by checking for any unidentified primary, secondary or tertiary coverages that can potentially reduce self-pay amounts and avoidable charity designations. As a result, Stanford Health achieved a $4.1m increase in average monthly payments and efficiency gains of $109k per month. 3. Provide transparent price estimates Experian Health's State of Patient Access 2023 report suggests that fewer than three in ten patients know how much their care will cost in advance, while nine in ten consider it important. Delivering accurate pre-care estimates to help patients plan for bills could therefore be an easy win to improve the patient experience and recoup more revenue. Banner Health used Patient Estimates as part of a wider strategy to improve patient collections. This solution generates detailed estimates of the patient's financial responsibility along with recommendations for payment plans and financial assistance, if appropriate. Listen in as Becky Peters, Executive Director of Patient Access at Banner Health, talks about streamlining the patient registration process and improving patient access with pre-care estimates. 4. Effective claims management Perhaps the biggest opportunity to improve revenue cycle performance lies in claims and denial management, which accounts for a major proportion of wasted healthcare dollars. Summit Medical Group Oregon–BMC paired Enhanced Claim Status with Claim Scrubber to submit cleaner claims the first time and avoid lost revenue. These tools help providers submit accurate claims and monitor claim status to prevent denials and resolve issues quickly. For Summit Medical Group, this led to a 92% primary clean claims rate, and a reduction in accounts receivable days and volume by 15%. Experian Health also offers a new solution that leverages machine learning and artificial intelligence for predictive reimbursement. AI Advantage™ uses AI to predict and prevent claim denials based on historical claims data. In the first six months, this solution helped Schneck achieve a 4.6% average monthly decrease in denials and decreased time spent on denials by 4x. 5. Easy ways to pay (plus clear pricing and payment policies) How easy is it for patients to pay? This simple but important question points to another vital element of effective revenue cycle management. A compassionate and convenient patient payment experience that matches consumer experience in other industries can encourage earlier payments. Easy digital options are especially important for millennial and younger patients: research by Experian Health and PYMNTS found that 60% of younger patients are looking for digital services. Experian Health's patient-friendly payment tools are designed to help patients navigate their financial responsibilities with confidence and ease. For example, PaymentSafe® allows providers to securely collect payments anytime, anywhere, including mobile payments and patient portals. 6. Operational efficiency with automation, data and analytics RCM processes generate vast amounts of data, providing valuable insights into the organization's operational performance, revenue trends and areas for improvement. Being able to parse and translate this data into actionable insights is essential to determine the right strategies to pursue to optimize financial performance. But this in itself can be a major lift. Revenue Cycle Analytics is a web-based tool that breaks down data into actionable insights across billing, reimbursement and payer performance, presenting KPI data via comprehensive dashboards. Effective revenue cycle management strategies from start to end From labor shortages to rising costs, healthcare providers are finding creative ways to manage cash flow. While each healthcare organization’s needs and goals are different, understanding these six key strategies of successful revenue cycle management can help hospitals manage their revenue cycles more effectively and efficiently, while responding to new uncertainties. Find out more about how Experian Health helps healthcare organizations leverage automation and AI to streamline processes and boost revenue cycle performance.

With the ability to be applied across many different areas – from disease prediction to claims management and administrative tasks – data and analytics in healthcare is booming. In fact, according to a Grand View Research report, the global market for data analytics was valued in 2022 at $35 billion and is expected to increase at a compound annual growth rate of 21.4% until 2027. So, why the rapid growth? How can healthcare data analytics be used across the healthcare revenue cycle? The role of data and analytics in healthcare Historically, there has been a large amount of healthcare data being generated, but the industry has struggled to properly leverage this data into useful insights that improve patient outcomes, operations, or revenue. Today, with increasingly advanced data analytics, healthcare providers are using real-time data-driven forecasts to stay nimble and pivot quickly in rapidly changing healthcare and economic environments. And there is more data collaboration between healthcare organizations to convert analytics-ready data into business-ready information, thanks to the ability to automate low-impact data management tasks. Data-derived intelligence is also now easier to share with colleagues, third parties and the public. Types of healthcare data analytics methodologies and tools Healthcare data analytics involves several different types of methodologies and tools – all of which can be applied to various aspects of revenue cycle management. For example, descriptive analytics allows organizations to review data from the past to gain insights about previous trends or benchmarks. Predictive analytics, on the other hand, uses modeling and forecasting to help predict future results. When a strategic course of action is needed based on certain data inputs, prescriptive analytics is used. If a provider wants to take a deep dive into raw data to uncover patterns, outliers, and interconnection, they may employ discovery analytics. There are also generally three categories of technology-driven tools that can help collect and convert raw data into usable insights during the revenue cycle, including: Solutions that gather data from a wide variety of sources, such as patient case files, machine-to-machine data transfers, and patient surveys Programs designed to scrub, validate, and analyze data in response to a specific question being researched Software created to leverage the results produced by the analysis into actionable suggestions that be applied to meet specific goals Applying data analytics to maximize revenue “There are many things driving near-constant change in the healthcare revenue cycle, including shifting reimbursement, evolving value-based payment models, growing regulatory pressures, and increasing provider risk and patient responsibility,” says John Menard, VP of Product, Analytics, at Experian Health. “Healthcare organizations are also adapting to value versus volume reimbursement models, requiring revenue cycle leaders to lean into leveraging data analytics to improve not just operational efficiency, but patient financial experience and quality outcomes as well." Here's a closer look at how data analytics can help with revenue cycle management: Assessing patient finances From registration to collections, data analytics can play a key role at every step of the patient journey – and revenue cycle. Not only can the right data analytics tools help healthcare organizations better assess a patient's individual financial circumstances, but they can also help providers create accurate estimates and payment plan recommendations. Data-driven technology can help providers reduce surprise billing through more transparent pricing, helping patients navigate the cost of care and providing more timely patient communication. Digital solutions can help improve the patient financial journey by: Providing a self-service patient portal – With a solution like PatientSimple, patients get convenient 24/7 access to self-service account management tools. They can use the online portal to log into their healthcare account to securely process payments, request or review payment estimates, and schedule appointments. The portal also provides patient access to pricing information, plus the ability to apply for financial assistance or set up payment plans. With easy-to-use patient online tools, patients are more likely to meet their self-pay responsibilities and providers get paid more quickly as a result. Offering payment solutions – To collect payments with confidence, healthcare providers can utilize comprehensive data collection and advanced analytics through a digital solution like Patient Financial Clearance. With this solution, providers use a patient's financial data to quickly assess a patient's propensity and likelihood to pay prior to treatment. When appropriate, providers can then offer empathetic financial counseling and connect those that potentially qualify to financial assistance programs. By applying data analytics to this payment solution, healthcare organizations can increase point-of-service collections while reducing bad debt—in real-time. Providing patients with more accurate estimates – A recent Experian Health study found that 4 in 10 patients said they spent more on healthcare than they could afford. However, when patients know the expected cost of their care up front, they feel more empowered and make better decisions. Patient Estimates lets providers create more accurate estimates, eliminate manual tasks and improve patient satisfaction. Plus, it allows providers to automate and standardize their price transparency practices, which can help healthcare organizations meet regulatory requirements, create a more positive patient experience and increase revenue at the point of service. Reduce denied claims According to Experian Health's 2022 State of Claims survey, denied claims are on the rise with 42% of providers reporting that denials increased in the past year. 47% of respondents also said improving clean claims rates was a top pain point. Digital solutions can help providers reduce denied claims and increase revenue by: Automating claims management – With a solution like ClaimSource®, providers can automate their claims management systems – helping to ensure claims are clean before they are submitted to a government or commercial payer. Using an automated solution also allows providers to streamline the claims management process from a single web application. With ClaimSource, providers can easily analyze claims, payer compliance and insurance eligibility. Plus, it allows staff to prioritize their workload and focus on high-impact accounts – resulting in claims denial rates of just 4% compared to the industry average of more than 10%+. Optimizing efficiencies through artificial intelligence – Incorporating artificial intelligence (AI) into an automated claims management solution enhances the claims process in two key moments: before claim submission and after claim denial. AI Advantage™ integrates seamlessly with ClaimSource to continuously learn and adapt to ever-changing payer rules. The solution features two AI offerings, AI Advantage – Predictive Denials and Denial Triage, which can be customized to prioritization thresholds. Verify insurance and patient information Missing patient healthcare data can be a headache for providers to hunt down but looking for active coverage is often necessary. Providers must contend with a range of factors impacting patient coverage – including forgotten coverage, inadequate coverage, patients being misclassified as self-pay and regulatory changes, particularly with Medicaid and Medicare coverage. Implementing digital solutions can help providers use data to verify and find missing patient health insurance coverage, optimize patient collections, and boost revenue by: Utilizing automated, real-time insurance verification – Verifying patient coverage prior to service using a digital solution, such as Experian Health's Insurance Eligibility Verification. This tool can help providers experience fewer payment delays and claim denials. Plus, verifying insurance with automated insurance eligibility and benefits data improves cash flow, reduces claims denials and speeds up payments, including Medicare reimbursements. Patients also feel empowered with accurate payment estimates and accelerated registration, leading to a better patient experience overall. Improving collections with better data – With Collections Optimization Manager, providers can screen out bankruptcies, deceased accounts, Medicaid and other charity eligibility ahead of time. Through targeted collection strategies, providers can leverage actionable insights to focus on high-value accounts. Plus, predictive algorithms and data-driven rules help providers route and distribute accounts to the right collectors and agencies, controlling overall collection costs. This solution also connects providers to live support from an experienced optimization consultant that will help develop a tailored collection strategy through data evaluation and industry knowledge. Finding unidentified coverage – In 2022, Coverage Discovery tracked down previously unknown billable coverage in 28.1% of self-pay accounts, finding more than $64.6 billion in corresponding charges. Providers can use Experian Health's Coverage Discovery solution at any point in the revenue cycle to look for previously unidentified coverage – maximizing insurance reimbursement revenue and reducing accounts sent to collections, charity, or bad debt. Coverage Discovery also automates self-pay scrubbing and proactively identifies billable Medicare, Medicaid, and private insurance options, using a mix of search, historical information, proprietary data sources and demographic validation. See how the right data and analytics can help providers better understand their patients, streamline operations, and improve revenue.

Many hospitals and health systems are rethinking their responses to the growing challenge of healthcare claims management. After all, claims are becoming increasingly more complex. Payer policy edits are changing at a scale not seen before. And the legacy of the pandemic continues to take a toll on administrative workflows. In Experian Health's State of Claims survey 2022, providers reiterated the urgent need to optimize claims management – and the mountains of wasted dollars that are the by-product of preventable denials. Could artificial intelligence (AI) and machine learning (ML) be the key? What does the future of healthcare claims and AI look like? The internet is buzzing with excitement about the AI revolution, but the adoption of AI technology in healthcare has been slow, compared to other industries. Providers may be unclear about implementing AI effectively or struggle to see a route around barriers to adoption. This includes concerns around legacy systems and data interoperability. That said, the uptake of AI in healthcare shot up by 167% between 2019 and 2021, as organizations spotted opportunities to leverage new technology to reduce denials, optimize processes and identify patterns. Now, the AI genie is out of the bottle. As the trend continues to grow, providers that fail to embrace these technological advances risk falling behind as their competitors race forward. This article looks at AI's role in the future of healthcare claims management, and specifically, how it can help providers streamline claims processing, recoup more revenue and gain a competitive edge. The growing challenge of healthcare claims management In Experian Health's State of Claims Survey 2022, providers said reducing denials was their number one priority. It's clear to see why. There have been more than 100,000 payer policy changes between March 2020 and March 2022. Staffing shortages continue to put pressure on both front-and back-office teams. Increasing patient volumes and changes to insurance coverage means more claims to process – with more complexity to boot. Looking ahead, providers need to find more efficient ways to manage and utilize increasing volumes of claims data to alleviate staffing pressure, improve productivity and future-proof against unexpected events. Failure to do so could be an expensive mistake, especially when margins are already tight and the economic landscape remains shaky. Digital claims management: from process-automation to pattern-spotting The survey suggests providers are increasingly turning to automation to improve claims management, with 78% saying they were likely to replace their current solution to achieve lower denial rates in the coming year. Upgrading claims technology, automating the tracking of payer policy edits, and automating patient portal claims reviews were the top three strategies for reducing denials. Automation can generate years of ROI by executing repetitive and error-prone administrative tasks at speed and at scale. A few examples of automation in action are tools like: ClaimSource®, which manages the entire claims cycle, creating custom work queues and automating the claims process for greater efficiency and accuracy. Claim Scrubber, which automatically reviews every line of every claim to check for errors, so claims are clean the first time, prior to submission.  Denials Workflow Manager uses automation to help providers eliminate manual processes, prevent errors and increase reimbursement. AI takes this a step further, by analyzing vast amounts of information to find patterns and make predictions that support better, faster decision-making. Clarissa Riggins, Chief Product Officer at Experian Health explains why providers should embrace AI in claims and denials management: "Claims submissions and managing claims after denial are highly manual processes – and they are both extremely error-prone. AI/ML can learn from the data patterns in your claims to provide insights on where your claims are being denied most frequently. These solutions can also provide decision support to staff to help them to prioritize the work within their current claims processes, to avoid unnecessary denials in the first place and then to optimize their work to ensure a cleaner claim rate." While many providers see the potential of AI to streamline claims operations, prevent denials and accelerate reimbursement, others are hesitant to invest or are stumped by logistical barriers. Legacy technology, data compatibility issues and staff skills gaps can all put the brakes on AI implementation. But the AI train is showing no signs of slowing, and providers that fail to jump aboard could get left behind. With the right tools and an experienced vendor, implementation can be simplified. AI Advantage™ – the engine for predictive denials and denials triage Experian Health's new AI-powered denials management solution uses a two-pronged approach to predict, prevent and prioritize denials. First, AI Advantage – Predictive Denials identifies claims that may be at risk of being denied, based on analysis of historical payment data and payer decisions. This gives staff time to intervene and make any necessary amendments before the claim is submitted. The second element, AI Advantage – Denial Triage, applies an algorithm to segment denials based on the likelihood of reimbursement. This means staff can focus on high-impact resubmissions, rather than simply prioritizing high-value claims that may or may not be paid. Rob Strucker, Product SVP at Experian Health, explains that AI Advantage™ is continuously learning in real-time, so that predictions are increasingly accurate: “We look at the provider's own information for this type of service for this payer, and how those claims have been adjudicated. From that, we can score each claim in terms of its probability of being denied or claimed, and then based on that probability score, trigger an appropriate alert.” How Schneck Medical Center optimized healthcare claims management with AI Advantage™ AI Advantage™ proved to be the solution Schneck Medical Center was looking for when they set out to reduce denials. Within six months, Experian Health's AI-powered solution enabled Schneck to reduce denials by an average of 4.6% each month. Staff reported that the probability thresholds calculated by AI Advantage™ were highly accurate, facilitating a more efficient approach to reworking claims. Processing time was cut from 12 to 15 minutes to less than 5 minutes per claim. Clarissa Riggins says that AI Advantage gives staff confidence that they're spending their time on the right tasks: "When you have an algorithm that can evaluate the probability that a denial will be overturned, you can make sure that staff are working on the claims with the most potential for yield. Taken together, these solutions can help ensure that hospitals and health systems are getting paid for the good work they do in delivering care." Thanks to the tool's predictive capabilities, staff now have the insights (uncovered from within their own data) to prevent denials before claims are submitted, and to speed up rework should any be denied. As claim denials continue to increase in number and complexity and healthcare costs continue to grow, providers are feeling the impact on their revenue and margins. AI can ease the pressure by optimizing the healthcare claims management process. Find out more about how AI Advantage™ can help providers improve healthcare claims management and prevent costly claim denials.