Solving the data interoperability problem
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n health care, there seemingly is no limit to the amount of data that needs to be collected, understood, analyzed and shared. Some of that data is refined, much of it — primarily medical notes — is not. And the problem of having data interoperate with other data from other systems has not really been solved yet, though Bob Stanley, senior director for special projects at data quality company Melissa, said some people think it’s not a problem. “I’ll propose that [data interoperability not being a problem] is maybe something accurate, but usually not,” Stanley said. Early in health care’s digital transformation, groups were trying to define standards that would be authoritative, but they weren’t recognizing the changes in the field, he noted. But new standards like Fast Health care Interoperability Resources (FHIR) for data interoperability are coming out and offer promise, but these efforts are ongoing, Stanley said. That ties back to a notion first shared a few years ago that dirty data is the industry’s dirty secret.
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Data quality, as well as data plumbing, harmonization and interoperability, Stanley said, “All of those are our sort of dirty secrets of informatics and data science, or even just trying to have a good customer outreach.” Stanley thinks data quality issues persist because “if you’re a project manager, or a new CIO, in one of these roles where in theory data quality would be really important, it’s just not something that gets you a feather in your cap. They aren’t coming in to the business and saying, ‘We’re going to need to spend this much money, time and effort’ for data quality. It’s just not exciting. It’s not the end game anyone is seeking.” But, he pointed out, “It’s like, you’ve got to do the dishes before you can cater the next party. Everybody just wants to party.” But there are a lot of challenges in the space, particularly with data coming from what they call real-world evidence, hospital systems, electronic medical record systems, and things like that, Stanley noted, citing a customer with whom a medication was refer-
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enced 190 different ways from within a single EMR system. Because of this, it’s important to look not only at the reference for what data the person is entering, but also looking at the context of where the person is entering the data. Stanley explained: “If it’s for this patient, I got this little string, and it’s suggesting this drug. I’m gonna say, ‘Yep, this is the drug I’m giving that person.’ But if the drug is contraindicated with other medications that patient is taking, then you can get a little popup that says, ‘Are you sure this is the drug you were thinking about? It shows a conflict with the other drug.’ “ The improvements being made in these systems are on the data entry side, which makes it easy for people downstream to do better work. And there are big ambitions for where this is all going. But, as Stanley pointed out, “there have been big ambitions for 20 years in this space. Ever since the Human Genome Project, we’ve really had high hopes for informatics and data-driven insights in health care and the life science space.” :
