Healthcare’s ‘valueless data’ problem

March 19, 2019
By Brian Robertson

Healthcare has a data problem.

The technological maturity of electronic health records (EHRs), data warehouses, and other sophisticated systems of record have enabled health systems and payers to absorb unthinkably massive quantities of data.

An annual research report by EMC and IDC predicts that the digital universe will contain 44 trillion gigabytes of data next year (roughly one byte of data for every star in the universe, with gigabytes to spare). Nearly a third of that data will be collected and stored by the healthcare industry, according to a Ponemon Institute study.

Alarmingly, this flood of data will never crest. Dr. John Halamka, chief information officer of Beth Israel Deaconess Medical Center, predicted that every patient will add 4 MB of data to his or her EHR storage every year – and this was before the steady ascension of wearables, apps and other consumer devices.

The problem is that about 80 percent of this healthcare data is unstructured. Because those “dark” data elements are difficult to identify and apply to business or clinical challenges, they have very little inherent value. It’s like standing at the edge of a vast, churning ocean and your task is locating and securing specific drops of water. Clearly, an impossible task for any human.

But not for artificial intelligence (AI).

The argument for AI
The analysis and optimization of administrative and financial transactions, health records, or other complex and repetitive tasks can quickly subsume even the most innovative enterprises. High-performance machines and algorithms can examine complex, continuously growing data elements far faster, and capture insights more comprehensively than traditional or homegrown analytics tools.

AI has carved established inroads in multiple industries, including healthcare. Much of that innovation has been put to work solving clinical challenges, but more health system leaders are considering its value on the financial and consumer experience segments of their enterprise.

There are several reasons for this. First, clinical applicability is heavily regulated and strenuously tested for consistency, reliability, and safety. True, administrative and financial tasks bear similar risks, but those risks are not nearly as amplified. As such, AI can be more readily implemented in this area, and the results are more immediate.

Second, AI is ideally suited to tasks that are both repetitive and complex, a common attribute on the non-clinical side of health systems. When an AI solution completes a task, the outcome is evaluated, and lessons learned are applied to make the next task more efficient. This mimics human learning, only at a speed and scale far beyond what is possible for even the smartest individuals.

Third, the consumerization of patient populations has placed enormous demands on healthcare infrastructure. Financial and administrative processes, such as billing, appointment scheduling, and communication preferences, to name a few, are particularly acute stress points.

Take self-pay as an example. Revenue cycle departments were built to primarily interact with commercial and government payers. Today, all health systems are adjusting to the patient-as-payer, where personalization and experience bears more weight than the business rules of an insurer, and touch points extend far beyond the revenue cycle department.

Thanks to high-deductible health plans (HDHPs) and other out-of-pocket obligations, 30+ percent of a large health system’s revenue could come from patient payments. Just as healthcare organizations are being asked to take on more risk by traditional payers, they are also assuming more financial risk of non-payment by patients. As a result, health systems are beginning to see diminishing or even negative cash flow and financial margins.

Moreover, patient obligations aren’t a claim sitting in an electronic queue – most often, it’s a bill sitting on the kitchen table, along with a car payment, mortgage, utilities and the kids’ tuition. Who is this consumer and where are they in their life? Do they need financial assistance? Do they have questions about their bill? Is the bill’s size forcing them to consider delaying or forgoing care the next time?

Bottom line, healthcare payment is rapidly transitioning from a primarily B2B transaction between providers and payers to one that now includes an ever-increasing consumer element. These consumers have heightened expectations for the business side of healthcare, thanks to their purchasing experiences in every other aspect of their lives.

AI offers the possibility to uncover insights from across the enterprise, enabling health system leaders to develop consistent and meaningful strategies that optimize the patient experience while also improving the health system’s bottom line. This is because machine learning distills complexity, finds patterns within billions of data points and gives organizations data-driven insight into the best opportunities for improving the quality and cost of healthcare.

Getting started with AI
Health systems need to transition quickly to value-based care while delivering consumer-centric experiences. A lever for accelerating change already exists within every healthcare organization – and that lever is the organization’s own data. However, analytics involves a different set of skills, a different organizational mindset and a different suite of technologies. The meaningful use of data requires more than light-lift SQL queries, dashboarding, or marginal enhancements to flowcharts.

That said, breaking ground on a net-new AI investment isn’t necessarily the heavy lift many fear it would be.

Like all healthcare technology implementations, a successful go-live begins with a solid strategy. For example, AI doesn’t need to solve all problems right out of the gate. Instead, consider a “crawl, walk, run” strategy that begins with automating specific tasks.

Iterate around your most essential pain points; AI is best done in an agile, experimental environment, rather than one that is broad and formless. Look at your available data, then pick a business process that has potential for optimization. There’s little risk in leveraging data around specific pain points. With each incremental iteration, you can move onto more ambitious initiatives. Next, create a culture and overall framework for rapid innovation. Set up a feedback loop that allows you to run experiments and gain results that provide value to end-users.

Acquiring the infrastructure, technology, and brain trust needed to uncover insights from incomprehensibly large and continuously growing data sets is the industry’s next great challenge. Millions of lives and billions of dollars of revenue and cost efficiency is at stake.

The promise of AI is no longer on the distant horizon, and it’s the farthest thing from a buzzword. It exists in the here and now. The unique complexities of the financial, administrative, and consumer experiences sides of healthcare are not only ideally suited to AI’s capabilities, but can deliver immediate and transformative results.

About the author: Brian Robertson is the CEO of VisiQuate.

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