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.

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