Healthcare Analytics, Population Health Management, Healthcare Big Data

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Can Big Data Solve the Health Insurance Transparency Problem?

Could on-demand health insurance with a big data analytics foundation help consumers make smarter financial decisions and cut wasteful spending?

Big data analytics and health insurance price transparency

Source: Thinkstock

By Jennifer Bresnick

- Along with housing, food, and transportation, healthcare is one of the biggest monthly expenses for American families – and the specter of an unexpectedly high bill for a major illness or surgery haunts the tens of millions of individuals currently living paycheck to paycheck.

Even non-emergency care that can be planned somewhat in advance, such as a joint replacement, can come with unpredictable costs and uncertain results, including for the patients who try their best to gather information about their financial responsibilities before undergoing a procedure. 

Trevor Fast, Plan Design Lead at Bind
Trevor Fast, Plan Design Lead at Bind Source: Xtelligent Media

Unfortunately, neither payers nor healthcare providers are known for being particularly forthcoming about their pricing structures. 

Chargemaster rates are typically jealously guarded secrets, and pricing negotiations are usually held behind closed doors.

READ MORE: Meeting the Challenge of Healthcare Consumerism with Big Data Analytics

Some healthcare systems appear to have no qualms about tacking on as many fees and line items as possible to get the most out of frustrated patients who often don’t feel as if they have any other choices.

As erosion of the ACA continues to force premiums upward and insurers shift more costs to patients in a strategic effort to drive waste out of the system, consumers are starting to demand more tools from their payers and providers to make better decisions about the cost and quality of their care.

Increasing pressure around price transparency from regulatory agencies like CMS will likely help, but big data analytics is truly their strongest ally in this quest. 

Marrying pricing data with quality metrics and clinical pathways to identify the highest value, lowest cost providers for specific procedures and services is the first battle.

The exponential growth of claims data, EHR data, quality measurements, and outcomes data, coupled with the potential of machine learning to make sense of it all, is slowly but surely allowing insights into a complex and highly variable environment.

READ MORE: Verma: CMS Will “Use Every Lever” for Promoting Interoperability, Data Access

Exposing that information to beneficiaries – and making sure they can take action around it – is an equally challenging task. 

While many major insurers have offered price transparency tools for years, utilization rates are low and some studies suggest that these online comparison features aren’t actually doing much to cut costs.

Part of the problem may lie with the fact that trying to show beneficiaries high costs without designing plans that are flexible enough for patients to actively choose cheaper, higher quality services will not encourage providers to compete for business around outcomes and cost.

Value-based reimbursements are one way to curb wasteful spending and bump up quality on the provider side, and pay-for-performance initiatives had have some degree of success when risks and rewards are correctly balanced.

But a new approach from Bind Benefits is hoping to combine big data analytics and machine learning with a consumer-driven benefit design that places price transparency at the center of the process of seeking care.

READ MORE: 93% of Payers, Providers Say Predictive Analytics are the Future

“One of the problems with health insurance is that some services are conducive to planning and shopping, and some are not.  Yet beneficiaries typically pay a premium and have a deductible that encompasses both categories of care,” explained Trevor Fast, Plan Design Lead at Bind.

“We find that a lot of the waste in healthcare is concentrated around those plannable services, because some of them are actually very low value in terms of price versus outcomes.  We need to equip beneficiaries with the data they need to identify the best prices for plannable services while still providing comprehensive coverage for the ongoing essentials, such as chronic disease management, preventive care, and emergency care.”

Bind bills itself as providing “on demand” health insurance, although it stresses for legal reasons that its programs are employer-sponsored, self-insured health plans, making it a third-party manager and not an insurance company itself.

Its plan is designed around dividing services into “core” benefits – ACA-compliant preventive, emergency, and pharmacy coverage with no deductible – and “add-ins,” which are available a la carte to beneficiaries at any point during the year.

“Add-ins cover a wide range of services, but they all share the characteristic that the member has time to plan for them and that there are multiple providers or settings that offer comparable care,” Fast said.

A patient approaching the need for a knee replacement, for example, could purchase add-in coverage for the procedure immediately before making the decision to go ahead with the surgery instead of having to budget and pay for a traditional plan that includes comprehensive joint replacement coverage for months or years before the need arises.

“The net result of this benefit design is employers can save ten to fifteen percent while preserving a rich core benefit that meets of what most people need during the plan year,” said Fast. “For those who need to access care outside of Core, they can buy additional coverage at any time.”

“We have found that between 93 and 95 percent of members find all their care within Core in a given year,” he continued.  “About 80 percent of the year’s costs fall within Core.  The remaining 20 percent is spent in the add-in category.  Because those are plannable services, we see a huge opportunity to bend the cost curve there by giving patients clear pricing so they can make more effective choices.”

Data plays a starring role in enabling broad price transparency, he added.  Bind works closely with UnitedHealthcare and its data analytics resources to access and analyze quality measures, episodic costs, and other financial data.

“For the first time, the state of data science and analytics is allowing us to make sense of the vast wealth of provider, clinical, and treatment data that’s out there,” said Fast. 

“The insurance company is the only place where all that data comes together.  The payer is the only one that can look out on all of the different providers and tell the patient about the pricing of every one of them.  We can deliver that data to consumers in a clear, accessible way through the supercomputers most Americans have in their pockets.”

A smartphone app is the main point of interaction for beneficiaries.  The app provides access to data on a patient’s costs for Core services, such as treatment for an ear infection at a retail clinic versus the emergency room, as well as the paycheck deductions and out-of-pocket costs involved in selected add-in services.

Patients can search for specific providers and facilities, but can also view a regional map that shows all providers offering a particular service and Bind’s associated charges for care.

“This is not a cost estimator,” Fast stressed.  “We don’t provide price ranges – those are not helpful if you don’t know which end of the spectrum you’re going to end up on.  If you want to enable smarter, informed decisions, you need to provide a clear economic signal at the outset, and it has to be reliable.”

Consumers are often surprised that the prices for identical services, such as an MRI, can vary by hundreds of dollars depending on the setting of care, Fast said.  Simply sharing that data through the app can significantly influence decision-making.

Bind also tries to get ahead high costs by using machine learning to guide patients along lower-cost care pathways that may also promote more conservative treatment options.

“We use machine learning to contextualize where an individual is in the course of addressing their condition,” Fast explained.  “Most conditions covered under our add-ins have clear steps along the progression to a major event, like an orthopedic surgery.” 

“We want to make sure that the individual is aware that they may be approaching a care boundary, and we want them to understand the options that will be available to them as they move closer to it.”

An “early listening system” based on eligibility check information and claims history helps pinpoint what services an individual is likely to be considering in the near future and allows them to plan even further ahead for the expenses involved.

“If a member has a visit to an orthopedic surgeon, we’ll be able to see the eligibility check coming from that provider, and we can take a look at their past diagnoses or claims codes to see why they’re probably seeing that specialist,” Fast said.  “We can proactively reach out to the member with a notification and ask them if they’re considering an arthroscopy, for example.”

“If so, we can present them with their cost options for getting an add-in for that procedure, but we can also surface their Core options to them if they haven’t tried everything that typically leads up to an arthroscopy.”

Presenting options for physical therapy, chiropractic care, or a cortisone injection – all of which are covered under the Core plan – may help the member save money and potentially solve their problem with a less invasive therapy.

“We’re not necessarily making a recommendation,” Fast pointed out.  “We leave that up to the providers and patients, of course.  But if we have data that shows three million other people in a similar situation chose to go here next and achieved this outcome at that cost, we think that could be valuable for a member planning his or her care.”

Using machine learning to process eligibility check data in addition to claims data could also help to ensure that patients stay in control of the referral process, as well.

“Many providers refer to specific hospitals or facilities for very good reasons.  But unfortunately, some will steer their patients to certain partners for their own financial interests, even if it ends up costing the patient $20,000 more,” Fast said.

“Patients don’t always know they have other options.  If we can suggest that an outpatient surgical center might be more cost effective than the hospital, yet both have similar risk-adjusted quality and outcomes scores, the patient can make an informed choice.”

More detailed quality data is on the way, and Bind is also working on analytics that can identify provider referral patterns, which can allow the company to attach more customized copays to certain treatment choices while alerting patients of their options. 

“If Doctor Jones tends to send her patients straight to surgery, but Doctor Singh is more likely to recommend physical therapy first, and one achieves better long-term outcomes than the other, patients should be able to use that information to budget and plan on what’s best for them,” said Fast. 

“The further upstream we are, the smarter our financial design can be.  Consumers now expect that services learn from their histories.  Our entertainment streaming services can do it.  Our credit card companies can do it.  Why shouldn’t health insurance be able to deliver on that, too?”


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