By Brian Robertson

It’s a good bet that almost all of us have wished for the ability to see into the future at one time or another. Unfortunately, that ability to predict with absolute certainty escapes most of us. When it comes to healthcare, however, there are a few trends that healthcare executives should keep a close eye in 2020.

Not surprisingly, a major shift from fee-for-service (FFS) to fee-for-value/value care as the dominant form of reimbursement isn’t one of them. We still have some miles to travel before value care is ready for prime time, especially since FFS is still much easier to administer and render payment than reimbursement for value (although value care is making strides in reducing the complexities). So while the healthcare industry will continue to move toward aligning reimbursement with the incentives to deliver higher-quality care at optimized costs in 2020, it will be more of a slow jog than a great rush.

That said, I believe the following four trends will be the ones to watch (and act on).

1. The need for analytics around self-pay solutions

This one requires zero psychic ability. The patient portion of healthcare bills has been on a steep, steady rise over the last few years and shows no signs of stopping. The Centers for Disease Control and Prevention (CDC) says that more than 45 percent of Americans aged 18-64 now have high-deductible health plans (HDHPs) through their employers — a roughly 25 percent increase since 2013.

To manage this major shift in payment responsibility, providers and payers need to gain an atomic-level understanding of the financial underpinnings of their patient’s/guarantor’s portfolios. They must then use this knowledge to develop new strategies that encourage patients to pay an “affordable” share of the cost.

Of course, affordable is in the eye of the beholder so payers and providers must understand what that means to patients, as well as patients’ ability and propensity to pay. Finding the “sweet spot” for optimal payment and liquidity requires deep segmentation that comes from analytics powered by deep data science and machine learning (ML).

The continued growth of patient self-pay will also make it critically important to offer convenient payment options such as Venmo, PayPal and other mobile payment platforms. Especially when you consider that a provider’s best chance of receiving patient payment is while the patient is still in the office/care setting. For larger out-of-pocket expenses, providers will need to create payment plans in line with the needs of their populations while again offering easy and broad payment at the point of care/sale.

Providers also need to offer clear, rational explanations of how much patients/guarantors owe, and the options available to pay that debt according to multiple studies. Those that can meet this standard are the ones who will succeed.

Larger deductibles and coinsurance costs also create a risk that patients will delay seeking care until their condition becomes unbearable or even forgo it entirely to avoid the unplanned expense. This approach is the polar opposite of value care, which relies on preventive care and early interventions to deliver better long-term health outcomes while reducing costs. This discrepancy must be resolved if we are ever to achieve true value care.

2. Advanced analytics will become a larger factor in denial management

One area where providers can have more direct impact on improving reimbursement is managing denials. At any given time, as much as 40 percent of a provider’s accounts receivable (A/R) portfolios are facing denials. This is an issue because, typically, only two-thirds of denials are recoverable. On the plus side, 90 percent are preventable, especially if you can attain a very granular root cause understanding of why claims are being denied.

Advanced analytics can offer that view, enabling providers to solve upstream issues so they can avoid most denials entirely. Providers can then take this information and use it to develop best practices that can be taught systematically to patient access staff, clinicians, coders and others. Getting to the deeper, root cause level of denials also often uncovers other important information that leads to continuous enhancements of systems of records and related software and subsystems such as EDI Claim Scrubbers.

Being proactive with good policies and procedures is a good start, but payer fee schedules, reimbursement guidelines and regulations are constantly evolving. With advanced analytics, providers can gain visibility into denials that question the clinical, medical necessity or missing information aspects of services rendered, giving them stronger footing to reach a mutual understanding with health plans more quickly. Providers can use deep and comprehensive longitudinal analytics to confirm they are meeting the terms of their contract and demonstrate why they should be reimbursed at the rates they negotiated with payers and in a timely manner. Both sides also gain insights that enable them to deliver more meaningful value to their patients/members.

Further enhancements can be made by using robotic process automation (RPA) driven by artificial intelligence (AI), now often referred to as Intelligent Process Automation (IPA). Manual claims processing is a laborious, expensive process that requires the revenue cycle staff to review every denied claim and spend way too much time digging into each issue. It is also susceptible to human error.

Intelligent Process Automation that tightly integrates advanced analytics and RPA has the potential to automate significant portions of the claim resolution life cycle. As a result, providers are able to collect more of the reimbursement they’re due while reaping tremendous time and labor savings as well.

3. The maturing of big data and the data economy

You don’t have to be a master seer to predict that the big data in healthcare organizations will continue to grow. What many get wrong, however, is that it will continue to be managed in different parts of the organization rather than becoming totally or perfectly centralized.

This persistent decentralization is a result of the reality that great use of big data is about driving actions and improving outcomes rather than the technology itself. To produce meaningful results, users must understand the business problems and many complex use cases that lead to problems. This knowledge depends on the users having a deep understanding of how things really work within the organization.

Normally, this awareness is contained at the departmental or team level, which means individual departments must get good at big data and analytics if the organization is going to take advantage of them. They can achieve this expertise on their overtime or get there faster by working with a partner that already knows how to manipulate the components and take meaningful action based on what the big data and analytics show.

4. AI becomes more critical to productivity and revenue optimization

When working with big data, AI and ML offer many advantages — not the least of which is their ability to spot patterns in data humans might miss while achieving tremendous scale. Once AI and ML gain more “experience” with the data they can continue to improve their decision-making ability on their own rather than requiring human input. In fact, AI is able to learn and improve based on the outcomes from every closed account whether it was paid in full with no issues or there were discrepancies.

As pointed out previously, AI and ML can review a batch of claims and determine why they were denied (as well as whether the denial was appropriate) in seconds, once its knowledge base is constructed — rather than the hours or days it would take humans to work through the same batch.

Because of its ability to work with larger quantities of claims at one time, AI can easily group similar claims together, enabling providers to call about batches of claims with comparable issues once, rather than calling about each individual claim. This process saves time, reduces costs (including the personnel needed to make these calls) and helps speed recovery of reimbursement that might have been lost otherwise.

When AI is enhanced with ML, it can use past performance to go right to the source of issues better and faster. Suppose errors in claim mapping or missing information create a batch of denials affecting a very large batch of claims. The AI will learn from this experience and can be trained to look for similar relationships and problems. This creates better insights that ensure continuous, 24x7 process or system improvements in the claims life cycle.

Value care may still be off in a distant future. But the path to get there is already unfolding before us. Act on the trends listed here and you will have a very happy 2020 — and beyond.

About the author: Twenty years ago, healthcare and analytics were third cousins. Today, they're nearly inseparable, thanks to a unique cadre of leaders, including Brian, who shared and operationalized a vision that analytics could redefine the way healthcare is managed. Brian has been a passionate pioneer and evangelist of the power of Big Data analytics as a force to disrupt the economics and quality of healthcare in the most positive way. And after two decades of democratizing data and putting it in the hands of the people who need it most, he begins every day with the same boundless passion to help today's enterprise compete and win on analytics.