The Case for Healthcare Data Literacy: It’s Not About Big Data

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Healthcare Data LiteracyEditor’s Note: This article originally ran in The Health Care Blog on October 1, 2015. To read that version please click here.

After a recent talk, a client came up to me with a puzzled expression.

We made small talk. We talked about the weather. We talked about sports. Finally, he got to the point.

“When are you going to talk about Big Data?” he asked somewhat impatiently.

“I’m not,” I responded.

It transpired that he was expecting to hear about all of the miraculous things Big Data was going to do for his healthcare system. He had come expecting to hear my Big Data talk.

Apparently, this was something he had been looking forward to all week. He was to be disappointed.

As a matter of fact, I almost never talk about Big Data.

And for the most part, nobody at Health Catalyst does.

Which might seem a little strange for a company in the data and analytics business. You’d think we’d be singing the praises of Big Data from morning to night. But we aren’t. There’s a reason for that, which I think is important.

At Intermountain Healthcare I led a team that pioneered the use of analytics and process improvement to dramatically improve outcomes. Many of the concepts that are key to value-based care and the accountable care model originated—at least in part—in the work we did at Intermountain. I will forever be indebted to that experience.

So you’d think I’d be the first to be talking about Big Data.

But I’m not.

For one thing, I don’t find the concept of Big Data terribly helpful to the work I do. The data sets we’re dealing with typically don’t meet the definition of Big Data. In many cases the hospital and health system data warehouses we’re analyzing include millions and millions of records. But Silicon Valley is dealing with huge volumes, velocities, and varieties in data. Those data scientists are typically talking about extremely large data sets when they’re talking about Big Data.

So, strictly speaking, healthcare is not really dealing with Big Data. We’re just using average data. I’m convinced that the folks and vendors who hype big data the most are suffering from some sort of Freudian insufficiency complex.

The other problem is more fundamental: everybody is talking about Big Data.

Name a problem and we’re throwing Big Data at it. Cancer. Alzheimer’s disease. New drug discovery. ER wait times. Google recently announced an initiative to use Big Data to cheat death.

You may have noticed that people in our industry have a habit of picking up on the next new thing. Two years ago it was social networking. Last year it was the cloud. This year it’s Big Data. Next year, it’s going to be—well, we’ll just have to see what it’s going to be.

For a company in the analytics business, this period presents a strategic challenge. How do you talk about yourself when everybody else is talking about the same thing and your customers’ heads are starting to spin?

Luckily, the answer is quite straightforward: talk about reality.

That’s generally how you combat hype. You talk about solid facts. You talk about pragmatism. You give a structure to the chaos. Every organization is at a different point in the progression from Healthcare 1.0 (the old way of doing things) to Healthcare 2.0 (the point where an organization is consistently able to scientifically leverage technology to improve outcomes and cut costs).

We’ve developed a comprehensive model for organizations to help with the transition and we call it the Healthcare Analytics Adoption Model: a core set of foundational principles. This approach acknowledges the core challenges facing most organizations.

Healthcare Data Literacy in Action

It drives me crazy when I hear people talking about the things they’re doing with data that have little to no grounding in reality.

A recent story in the news made me stop and think about this and wonder if we’re reaching a teachable moment.

A news organization called ProPublica did something that sounded quite original and potentially groundbreaking. A team of reporters took CMS readmissions data and used it to develop an innovative sounding scorecard for surgeons. (You’ll find the original story and the follow up here).

My reaction was “Huh. They can tell all this from CMS readmission data? That’s extremely impressive.”

Without going into a whole lot of detail, the ProPublica researchers made it look simple. Surgeons had been scored using a simple system. If a patient was readmitted to the hospital within 30 days, that counted as a ding. If a patient died, that counted as a ding. The number had been risk-adjusted, although exactly what that meant wasn’t explained. And just to be sure, they had checked with “experts.”

This score was presented to consumers as a reliable indicator of surgeon quality and safety.

There was no mention of potential limitations, which are considerable: the impact of patient population, the role of the care team, the behavior of individual patients that contributed to their readmission. Worst of all, there was no mention of the potential problems involved when data used to bill the government for patient services is used as a proxy for patient safety.

In short, there was no context. An ordinary consumer looking at the numbers wouldn’t know what to think and would almost certainly conclude the wrong thing.

The truth is, it’s complicated.

It’s always more complicated.

A word about my background: before moving to lead the informatics team at Intermountain Healthcare I spent ten years working in the Air Force as a “command, control, communications, and intelligence officer” and with the National Security Agency. That experience shaped the way I look at information management.

My job involved the collection of massive amounts of data from every source of data that might contribute to better military and national security decision making, and then trying to actually make that data sensible and useful in situations where the wrong decision is measured at the international level.

To help us track the reliability of information, we would assign a very carefully calculated credibility score to the sources of data. Every intelligence report, every force status dashboard, every sensor warning system included some form of data quality, context, and credibility summary along with it, so that decision makers (the consumers of the data) could make the most informed decision possible.

Today, when I look at a data set like the ProPublica report my first question is: “what’s the source of this data, and is it a credible source of data for the decisions that it’s supposed to improve?” Those early years of experience as a military intelligence officer, along with an undergraduate philosophy class called “What is Truth?,” taught me to pause and challenge the data until it either crumbles or prevails.

I learned to look for credible numbers. To look for red flags indicating that information that told me one thing might not actually mean what I thought it did. And one of the most important things we learned in the Air Force when it came to data analysis and situational assessment: When in doubt, underreact.

The ProPublica incident suggests that those of us who produce and analyze healthcare data have a moral obligation to describe the context and degree of certainty that exists in the data we produce. We are obligated to ensure that the consumers of the data understand the decisions and conclusions that they should and shouldn’t make, based upon the data that they are consuming. The average American does not have a sophisticated understanding of data-driven analysis. We’re going to have to make data literacy a goal for ordinary Americans and perhaps even more important, for the politicians in Washington and the managers who are using data as a policy tool and as a driver for their business.

We’re going to need to give folks the tools to evaluate the credibility of the data they use and train them to ask tough questions about what numbers can and cannot do for our decisions.

Thirty years after entering this business I am more convinced than ever that technology and data combined can help drive the transformation of healthcare and American business. We are entering the Human Era of Data that will dramatically impact our evolution as a species.

So no, you won’t hear me talking much about Big Data.

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