Industry Voices—Coronavirus data confusion points to hospitals' big future challenge

Photo of two men shaking hands in front of a hospital
A glaring lack of authoritative data amid the COVID-19 pandemic is a reminder of the big long-term challenge hospitals face in improving efficiency and making the value-based care model work. (cunaplus/Shutterstock)

More than five months since COVID-19 emerged in central China, we still lack reliable data on how many people have been infected and the rate of new infections, let alone an accurate accounting of the dead. Limited testing in most countries means we have little idea of how many people have the virus, and no precise picture of the fatality rate. 

That means governments are to varying degrees flying blind when trying to decide the appropriate policy responses. John P.A. Ioannidis, M.D., professor of epidemiology and population health at Stanford University School of Medicine, has called the pandemic a “once-in-a-century evidence fiasco.”

All this confusion stems from the same problem: the lack of a single source of reliable data about the virus and how it spreads, leaving the field open to a maelstrom of numbers and spin from governments, media and experts.

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While the pandemic has put a lot of short-term pressure on hospitals, this glaring lack of authoritative data is a reminder of the big long-term challenge they face in improving efficiency and making the value-based care model work.

Hospitals are in many ways a microcosm of the coronavirus confusion, because they rarely have a central source of agreed-upon data. In this information vacuum, nursing departments, billing departments and executives harness statistics in order to tell the story as they see it—just as different media outlets do with the coronavirus. 

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In the fee-for-service model, the inefficiencies this causes can almost be drowned out as hospitals chase the perverse goal of higher volumes. But as value-based care takes over in the coming years, hospitals simply won’t be able to succeed unless everyone gets on the same data page.

The highest cost treatments, which are the most valued under fee-for-service, actually produce the worst outcomes. Hip and knee replacements, for example, often end up with patients requiring long, expensive stays in nursing homes because they didn’t follow their post-op therapy. Under value-based care, that could be addressed by using data on patient engagement to help inform hospitals where proactive interventions are needed to avoid higher costs down the line.

Claim denial, which affects about one in every 10 claims and costs health systems up to 2% of net patient revenues, is another key problem that can be addressed through better use of data.

The blame for a denial will usually be pinned on mistakes by the billing department or business office. That only yields a superficial understanding, though. To understand the root causes of a denial—or any other problem—you need to go “upstream” by analyzing data that tells you where missteps in the process are originating. With denials, that could be in areas such as the documentation and coding of claims. 

When assessing where in the chain breakdowns and inefficiencies are occurring, it’s important to be aware that they could lay with people, processes or technology, or any combination of the three. Just because you’ve found one cause doesn’t mean you’ve solved the problem.

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Becoming a truly data-driven organization takes time but has a simple starting point that most never get past. It requires all the relevant stakeholders coming together, agreeing on the things that need to be measured and committing to be truly guided by the data. From that point on, there needs to be an ongoing, formalized process for leaders to embed this approach into healthcare professionals’ everyday thinking.

Part of this is a big—but worthwhile—challenge to bring clinically minded physicians and nurses on the journey to becoming critical thinkers on the business side. The other vital element is to have an advocate for the transition, a leader who promotes the use of the agreed-upon data and helps stakeholders understand how the data flow through to improvements in business results and healthcare quality.

Jon Ault is a principal with Eide Bailly’s Data Analytics practice.

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