Healthcare finance sub-trends that will make a significant impact on healthcare in the new year.
It’s that time of year again where everyone seems to want to tap into their internal Nostradamus and make predictions for the upcoming year.
Often these prognosticators are given to wild speculation, predicting the healthcare equivalent of a future filled with flying cars. But when it comes to healthcare finance, it’s important for the predictions to be grounded in reality.
For example, it would be easy to predict this is the year value care or fee-for-value (FFV) in all its iterations begins to replace fee-for-service (FFS) as the dominant payment system. But it’s unlikely to happen for one simple reason: the soup is only half cooked. Nearly everything in healthcare takes time. And while the alignment of incentives to deliver high quality care at optimized costs is continuously, yet gradually improving, FFS remains much easier to administer. Payments for value can be highly complex.
With that in mind, there are several healthcare finance sub-trends I believe will make a significant impact on healthcare in 2020.
Here are four:
1. Self-pay solutions and analytics will play a bigger role in the healthcare finance ecosystem.
According to the Centers for Disease Control and Prevention (CDC), more than 45% of Americans aged 18-64 who have employer-based insurance now have high-deductible health plans (HDHPs). That percentage is expected to continue to grow over the next few years, which means providers and payers will need to have an atomic level understanding of the financial aspects of their patient/guarantor portfolios.
They will also need to get really good really fast at understanding how to get patients to pay “an affordable” share of cost. Affordability, propensity, and ability to pay all require deep segmentation and analytics. That understanding will come through advanced analytics, powered by data science and machine learning, designed to find the “sweet spot” of optimal payment/liquidity. Equally important is offering patients payment options that are most convenient including Venmo, PayPal, and other mobile payment platforms.
Study after study indicates patients/guarantors want a clear, rational understanding of how much they owe and what options they have to pay it. The providers and payers who succeed will be the ones who can deliver clear and, dare I say, rational pricing in the eyes of the healthcare consumer/patient.
Providers and health plans must also understand as deductibles and coinsurance continue to go up, many patients are delaying or foregoing care due to the cost. That goes completely against the goals of value care, with its focus on long-term health outcomes.
Providers and payers will need analytics to determine how to help make healthcare affordable for all their populations. Providers in particular will need to develop payment plans that work and continue to make payment available simple and easy at the point of care/sale.
2. Denial Management will increasingly be driven by advanced analytics.
Denials are the bane, or supervillains, of healthcare providers’ existence. At any given time, as much as 40% of providers’ accounts receivable (A/R) portfolio is in some state of denial placing tremendous stress on full realization of net patient revenue. With more of a provider’s revenue already at-risk due to patient self-pay, it becomes more important than ever to avoid denials to the greatest extent possible. Especially given that only two-thirds of denials are recoverable but 90% are preventable.
The more providers deeply understand why their claims are being denied with very granular root-cause understanding, the more they can solve things upstream and systemically including training all staff involved in the claim cycle.
Deep root cause understanding often provides important information on ensuring continuous enhancements to systems of records, related software, and sub-systems such as EDI Claim Scrubbers.
Advanced Analytics can also give providers insights into clinical and medical necessity type denials so health plans and providers can come to a mutual understanding faster. With deep and comprehensive longitudinal analytics, providers can demonstrate conclusively they are meeting the terms of their contract and can hold health plans accountable for paying negotiated reimbursement in a timely manner. In addition, deep analytics allows both sides to have data and insights to design more meaningful value for their patients/members.
This, incidentally, is where robotic process automation (RPA) driven by artificial intelligence (AI) can be a game-changer. With manual processing, revenue cycle staff often have to review every denied claim and with far too much time spent to research problems, it’s a time-consuming, expensive and laborious process still prone to error. Intelligent Process Automation, where advanced analytics and RPA work in harmony has tremendous potential to better automate significant portions of the claim resolution lifecycle.
3. We all live in a data economy now and big data will continue to mature.
Healthcare organizations overall will continue to take more advantage of big data in 2020. That being said, it will still be managed in different parts of the organization rather than becoming totally or perfectly centralized.
Great analytics is about driving action and has little to do with technology. The real value of analytics is about driving data driven outcomes rooted in deep understanding of business problems, understanding many complex use cases where problems often arise, and how things really work within the organization.
That knowledge is still most often at the departmental or team level, which means for the organization to get good at big data and analytics the individual departments must get good at it. Either on their own or by working with a partner that is good at all the components required to work with and most importantly take meaningful action on big data.
4. AI will become increasingly important for productivity and revenue optimization.
Much of the value of AI and ML is in its ability to spot patterns in data that a human might miss, and at tremendous volumes/scale with continuous and ongoing decision-making improvements as it accumulates more “experience.” Put simply, AI learns from every closed account whether paid at full reimbursement with no issues and every other iteration of payment discrepancy.
Humans can spend a lot of time reviewing a claim to determine why it was denied. Once its knowledge base is built, AI can do the same work in seconds. It can also group similar claims together, which means instead of calling about problems with each claim individually, providers can make calls about batches of many claims that have the same issue. That not only saves time and cost, (including reducing the number of people needed to call about claims) but helps providers recover reimbursement faster that may have been lost otherwise.
Through machine learning, AI gets better and faster at going right to the source of issues based on past performance. For example, if a provider has a batch of denials because of errors in claim mapping or missing information, which may have impacted a very large batch of claims, AI is always learning from this experience and can be trained to look for other relationships and problems with better insights to ensure continuous process or system improvements in the claim lifecycle.
Healthcare still has a long way to go before we can ensure high quality and affordable outcomes for all. However, 2020 definitely holds promise for some leapfrog moments.
Brian Robertson is CEO and founder of Visiquate and 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. 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.