Digital Revenue Cycle Management: Critical Care for Providers
Healthcare providers must transform their revenue cycle management (RCM) operations with new digital solutions to maximize all the reimbursement dollars due them, as they work toward a zero-touch claims processing environment.
Many healthcare providers struggle to get fully reimbursed for the care they provide. Hospitals are writing off 49% of the patient’s financial responsibility as bad debt. Only 32% of patients pay their full medical bills, and that number is expected to drop to 5%, an effect often attributed to the growth in high deductible insurance plans and increasing co-pay amounts. Federal and private payers, meanwhile, expect providers to follow increasingly strict reimbursement rules as they move toward value-based payment contracts. Yet on the provider side, tedious manual processes, from patient registration through revenue reconciliation, lead to errors, underpaid or denied claims and, ultimately, revenue shortfalls that cannot be overcome.
It’s time for providers to deal with these issues head on by treating RCM operations as a profit center, making the function as efficient and subject to the same financial controls as any other part of their operation. Every RCM operation must become digital, incorporating automation, artificial intelligence (AI) and blockchain solutions to accomplish business goals. In today’s healthcare landscape, digital RCM (dRCM) solutions augment human expertise and experience, tackling routine, manual processes and provide insight while humans perform complex, value-add activities. Automating even one task, such as verifying eligibility using robotic process automation (RPA), can generate substantial time and cost savings to reinvest in additional technology solutions in the dRCM journey.
Providers may use dRCM based tools to estimate their costs of collection and quantify benefits, demonstrating dRCM’s value and profitability. Providers can deploy dRCM solutions relatively quickly to optimize and increasingly automate RCM operations, following a path that eventually will lead to no-touch claims processing. Along the way, providers can generate millions in annual savings by streamlining processes, using skilled RCM personnel more effectively and achieving optimal claims-submission accuracy with AI-based predictions about payer reimbursement behavior.
Solve immediate business challenges
Based on the common problem areas we see in provider RCM operations; we suggest the following opportunities for getting under way with dRCM.
Front office (Intake)
Start upstream with patient eligibility verification and prior authorization.
While payers have offered electronic eligibility verification for some time, many providers still call payers or log in to payer portals to manually verify patient eligibility verification. Using RPA, chatbots and automation can improve revenue generation and claims quality. Chatbots and intelligent voice recognition capabilities enhance customer engagement, such as after office hours coverage. One of our pharmacy clients achieved immediate operational efficiencies of more than 30% by using RPA for eligibility verification and more than 20% with prescriber follow ups.
Additional opportunities to consider include automating patient appointment scheduling and reminders, and leveraging wearable devices such as smart watches as a personalized communication channel to enhance patient engagement. Helping patients keep appointments improves capacity utilization and saves money: missed appointments cost the industry $150 billion a year.
Using RPA to automatically extract or post information from or to operational system(s) to populate patient registration data reduces data entry errors. Front office inefficiencies, such as capturing wrong or incomplete data, contribute to 60% of denied claims.
Share patient responsibility estimates.
Few providers offer patients an estimate of their financial responsibility at point of service, which is a missed opportunity to collect or help patients with alternative payment arrangements. In the short term, patient estimator solutions can help collate data from health plan and provider contracts so that payers can generate patient estimates to share with the patient. In the future, AI-based solutions will draw on financial records and social determinants of health to classify patient propensity to pay. Providers can then avoid write-offs by proactively offering payment plans and counseling and help patients identify funding sources.
Mid office (Billing)
Automate claims submission.
RPA combined with machine learning (ML) enables providers to increase first-pass accuracy in claims submission by eliminating human error and increasing accuracy in repeatable tasks such as claim generation, claim edits based on payer contract(s), and submitting clean claims to a clearinghouse and payer systems.
To eliminate manual checking of claim status, an event that drives all accounts receivable (A/R) functions, leverage RPA to determine status from payer websites and autocorrect and resubmit rejected claims for a quick and timely resolution. AI or machine learning and cognitive systems can evaluate all claim denials and execute intelligent rules-based workflow management, prioritizing tasks according to business goals and routing specific denied or underpaid claims to specialist teams for resolution.
These dRCM A/R solutions help reduce costs to collect and shift the operational focus from resolving edits on all claims to focusing on those claims where the provider has the greatest expectation of reimbursement. dRCM tools reduce handling time and the number of transactions that require manual intervention, so RCM personnel may focus on exceptions that require higher skills, judgment and experience. Days sales outstanding (DSO) and reimbursement rates improve, while dRCM also plugs revenue leakage: typically, charge capture errors and inadequate claim edits cost up to 1% of annual revenue.
Set intelligent priorities for appeals.
Most providers we work with have not quantified their costs to appeal denied or underpaid claims, nor do they have standard procedures or rules for which claims to pursue. Predictive algorithms and machine learning unearth insights about which claims to appeal based on past payer payment trends and the cost of doing so. That is, spending $100 of effort to successfully appeal two claims, each worth $1,300, is a better business decision than spending $100 on what an algorithm indicates will likely be a failed attempt to collect a single claim worth $2,600. These insights can be fed into rules engines and machine learning algorithms, which then automatically identify and give priority to the highest value recoverable claims in workflow queues. These solutions, combined with RPA, can automatically generate supporting document packets for initiating the appeals process.
Reducing denials prior to claim submission.
ML-based tools and predictive analytics can help providers find important patterns in historic claim submission and payer reimbursement data. ML systems learn from providers’ claims denial and rejection histories, then scan claims in real time and flag errors for immediate remediation before submission. On an average, 63% of denied claims are recoverable and providers spend roughly $118 per claim on appeals, making this a particularly quantifiable endeavor.
Detailed payer reimbursement analysis improves finance decisions too. Take a health plan that always disputes a specific claim code and reimburses 80% vs. 100% of a claim’s value. Instead of basing revenue projections on the full claim amount, the insight enables more realistic forecasts and thus better decisions about managing cash flow and financial reserves.
Payment posting, patient billing and revenue reconciliation.
RPA and AI can automate payment posting, patient bill generation and accounts receivable follow up. AI-based solutions can better match outstanding A/R to the reimbursements and deposits received. This can reduce cash posting time by up to 70%, based on our findings.
Begin now from where you are—keeping an eye on the future
Identify business functions across the RCM value chain where automation and AI can bring value and leverage existing data when training machine learning and AI algorithms. Preliminary training data sets can include current contracts, A/R trends, days sales outstanding trends, past claim rejection and denial reports. Target incremental improvements initially, beginning with RPA systems that essentially mimic human activities, only tirelessly and with more accuracy. Apply the lessons learned from these implementations and leverage additional data generated as you progress toward more comprehensive intelligent process automation and AI-driven solutions.
Incorporate change management planning into all opportunities to help staff understand how bots and ML tools will enhance their working environment. Choose tools that augment human expertise and experience. Clients report that RCM professionals welcome automation when they understand that the tools free them for more interesting value-add work that uses their specific skill set.
Finally, use the insights gathered to prepare for new payment models, including value-based reimbursements. Provider costs of delivering services and financial performance under specific payers are the foundation on which to negotiate equitable outcome-based agreements.
Blockchain technology will transform the healthcare revenue cycle. Being prepared to accept micropayments and execution of smart contracts via blockchain will be advantageous as “whole person care” takes root and providers seamlessly partner with third parties to incorporate non-medical services, such as transportation and food delivery, into patient care plans. Experiment with blockchain now by developing use cases and exploring collaborations with other organizations in the healthcare value chain.
Whether launching with eligibility verification or a blockchain pilot, providers must operate dRCM as a critical, strategic function to maximize revenues today and prepare for new payment models. dRCM tools enable providers to achieve these goals and adapt to the industry’s changing economic reality with financial strength and agility.
Srivaths Srinivasan, Director Cognizant Consulting-Healthcare, Ruchi Mishra, Senior Manager, Cognizant Consulting-Healthcare and Kinshuk Kaushik, Manager, Cognizant Consulting-Healthcare, contributed to this article.