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How AI and ML Can Manage Your Third Biggest Payer: Patients

Brian Robertson, Founder and Chief Executive Officer, VisiQuate

After Medicare, Medicaid, managed care and commercial health plans, the remainder of a hospital or health system’s revenue comes from patients. Those balances already comprise more than 30 percent of a large health system’s revenues and are rising rapidly. An analysis released last year found an 88 percent increase from 2012 to 2017 in total hospital revenue attributed to patient balances after insurance reimbursement.

Patient balances, however, are often harder to collect, requiring numerous outreach attempts and labor-intensive financial education and even negotiating. Capturing all net revenue is increasingly important considering that profit margins have come under pressure like never before. Moody’s reported that fiscal 2017 nonprofit hospital margins fell to 1.6 percent, the lowest level the rating agency has ever found. Moody’s cited “significant income contraction” as a major reason.

Fortunately, advanced applied analytics, including artificial intelligence (AI) and machine learning (ML), are making patient revenue capture more cost-effective for healthcare organizations while helping streamline the rest of the revenue cycle process. By optimizing the resources devoted to collecting from patients, as well as Medicare and commercial health plans, AI and ML can ensure that minimal costs are expended on recouping outstanding balances while sustainably fulfilling a healthcare organization’s charitable and community mission.

Changing Payment Landscape

Patient balances are rising for numerous reasons. First off, Americans who have health insurance through their employers are increasingly being pushed into high-deductible plans, making them less confident that they can afford care compared with traditional plans, according to the National Center for Health Statistics. And let’s not forget that the U.S. adult uninsured rate reached its highest level in four years during the fourth quarter of 2018.

Health Systems employ armies of workers focused on following up with such patients, as well as payers, devoting countless hours to resolving a variety of complex claim problems including denials, underpayments and slow payers. These laborious tasks often involve numerous phone calls and expensive payer portal inquiries to determine why a claim was denied or why a patient hasn’t paid their bill, then leaving the staff member to conduct ongoing research information in various systems, request medical records, all with the goal of getting the claim paid or balance resolved and as quickly and completely as possible.

AI and ML can upend this process, predicting denials and other claim problems based on results from millions of other claims historically submitted to the same payer. In this respect, the AI system is effectively pre-empting the payer’s denial, by alerting staff to the specific missing or incomplete information that is likely to cause the denial in the first place.

Although collecting outstanding balances from patients will likely never be as automated as submitting claims and appeals with an insurance company, AI and ML are helping carry some of the administrative load. AI algorithms “learn” by performing repetitive, high-volume tasks and building knowledge as they complete those tasks over time – with the ultimate goal of approximating human intelligence to quickly solve complex problems.

Such algorithms, for example, predict and segment which patients are more likely to pay so patient account representatives can devote their time and energy toward those accounts. Representatives can also visualize which accounts would qualify for financial assistance, so a faster and more patient friendly solutions can be reached sooner.

By automating important – but also routine – RCM tasks, health systems can free staff to work on more productive, highly-complex endeavors that require human intervention, while reducing labor costs, mitigating preventable revenue leakage and improving the overall patient experience.

Creating a Retail-Like Experience

As the consumerism of the healthcare paradigm continues to take center stage, the experience delivered to patients must, too. Healthcare providers must now forge long-term relationships with patients, similarly to retail-centric experiences. Consider your preferred airline carrier where you are signed up for miles and rewards. They know your preferences and patterns. They communicate with you in the manner you’ve designated and can offer targeted value offerings to enrich the consumer experience and ultimately loyalty.

This is where AI and ML can be leveraged to discover the types of communications individual patients are most responsive to. Delivering said retail-like experiences can ensure more predictable and even an uptick in net revenue as the industry shifts from episodic healthcare to building more long-term healthcare consumer loyalty.  We must pivot, putting AI and ML to work, gathering longitudinal consumer intelligence and thereby creating a continuous learning curve focused on value to the most important constituent – the healthcare consumer.

Similarly, AI can change billing from a claims/health plan-focused process to one that is more patient-focused and retail-like. Retailers invest billions of dollars in market research and software development to personalize the shopping process, both in-store and online. Healthcare can learn from that experience and apply similar algorithms to whatever combination of variables have been shown to yield results from that individual patient – or patients who embody similar characteristics.

Those variables may include factors such as preferred method of payment (Apple Pay, Venmo, debit card, interest-free payment plan, etc.), preferred channel of communication (text, email, phone call) as well as different times of the day or month to deliver communication and different types of content within the communication itself.

Patients’ lower account balances sometimes confound patient financial services departments because they lack the resources to chase these low-value amounts, and staff time is typically devoted toward touching higher-value accounts. The result is that these low-dollar accounts, which add up over time, are either written off or sent to collections, leading to unrealized or delayed revenue. By better automating the billing process through AI and catering it to individual patient needs, health systems can more efficiently manage and monitor low-value accounts, increasing net revenue performance and enabling staff to focus on more complex tasks. 

Patient as Payer is Now a Reality

We have indeed entered a new “patient-as-payer” paradigm, which has now forced health systems to thereby assume more financial risk of patient nonpayment, while also taking on more financial risk from payers in value-based contracting arrangements.

Writing off these balances is not a luxury healthcare organizations can afford. Outreach, communication and advising payments on fulfilling their responsibility, however, need to be strategic to minimize additional administrative costs. Injecting applied analytics, AI and ML into the RCM process should be the inevitable next step for health systems to achieve much needed financial relief and patient loyalty.

AI, artificial intelligence, machine learning, ML, payments, revenue cycle

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