C-CDAs: The Fuel For Medical Apps

Developing medical apps requires harnessing data from C-CMRs. Image via creative commons.

Developing medical apps requires harnessing data from C-CDAs. Image via creative commons.

When you look at Google’s and Apple’s app stores, you’ll see that they’re populated with thousands of applications that have revolutionized how we communicate and think about our phones. These apps are the reason we call our phones ‘smartphones.’ When you look at most electronic health records (EHRs), though, there’s no app ecosystem to help physicians diagnose, achieve patient adherence or improve care effectiveness and efficiency. EHRs don’t look ‘smart;’ they look like digital clipboards. Understandably many doctors aren’t pleased.

In a 2009 New England Journal of Medicine article, two key missing pieces in were identified that blocked the development of “smarter” EHRs: data liquidity and interoperability. For a usable app ecosystem to emerge, developers need to access relevant clinical data quickly, reliably and cheaply – and this opportunity may now be on the horizon.

What Are C-CDAs?

Before the federal government invested in the nationwide adoption of certified electronic health records (a project commonly known as Meaningful Use), the standards used to transmit medical data didn’t allow for quick, reliable and cheap access to data. This made it difficult to innovate. Unlike phones, medical data exchange was highly customized – systems differed across institutions. But the second stage of Meaningful Use, implemented this year, might be pushing us toward a solution.

Meaningful Use requires that electronic health record systems are capable of exporting data in a format known as the Consolidated Clinical Document Architecture (C-CDA). These C-CDA systems house common clinical data (allergies, medications, lab results, vital signs, procedures and social history) and were originally designed for point-to-point communication between systems, such as when a primary care physician refers a patient to a specialist. C-CDA extracts can be used for other purposes, too, though. Akin to how phone apps can use standard location, contact and user information for novel purposes, C-CDA documents can provide starting points for smart health apps.

CCDADev

The First Step

While it’s easy to draw comparisons to smartphones, electronic health record systems are incredibly different beasts. Most notably, EHRs have to deal with a larger set of terminologies than the average smartphone. Medical terminologies generally have many tens of thousands of codes and may be organized in different hierarchies. Additionally, the C-CDA requires use of major terminologies, but not all EHRs implement them the same way. I recently published a study on this issue in the Journal of American Informatics, focusing on semantic interoperability for C-CDA documents, one of the most important barriers for medical app creation.

In the study, we found that standardized terminology will be a necessary step before extracts from multiple EHRs can be easily used by medical apps. For example, you can say a patient has ‘diabetes’ in many different ways when you’re using  the C-CDA. It could be ‘diabetes mellitus’ (code 73211009) or a more specific ‘diabetes mellitus type 2’ (code 44054006) – but it could also be from another terminology, such ‘diabetes mellitus without mention of complication’ (ICD-9 code 250.0) or ‘type 2 diabetes mellitus without complications’ (ICD-10 code E11.9). The normalization of medical data is a fundamental first step to building the app ecosystem.

Next Steps

Once we work to normalize EHR data, what types of apps could we build? One open-source initiative has figured out how to measure quality of care. This project, known as pophealth, uses C-CDA extracts to calculate dozens of clinical metrics that have been endorsed by the National Quality Forum. It allows providers to ask questions about their patients, like ‘How many of my diabetic patients have evidence of poor glucose control?’ My company, Diameter Health also works with pioneer partners to develop other apps, like one designed to predict chronic kidney disease risk that we’ll publish on later this year.

Eventually, C-CDA documents will provide useful information for many different kinds of apps: clinical documentation, patient profiling, quality improvement, medication surveillance and predictive analytics, and we’re only beginning to scratch the surface of these possibilities. An important part of successful apps, however, will be aligning functionality with the needs of providers. Also, since medical apps access protected patient information, apps will need to consider privacy and deploy in close coordination with providers. Nonetheless, the goal will be to make apps easy to scale, quick to implement and relatively affordable in contrast to big data warehouses, which can cost millions of dollars.

While C-CDA documents aren’t the answer to all medical questions (they don’t encode complex image, genomic or waveform data, and other EHR formats are emerging, too), they’re helping us nudge closer to interoperability and data fluidity than we’ve ever been.

John D'Amore

John D'Amore

    John D'Amore has over a decade of experience providing informatics and strategic insight to healthcare organizations. He most recently founded Diameter Health, a health IT start-up to improve clinical, operational and financial outcomes through the intelligent use of data and predictive analytics. Previously, John was Vice President at Eclipsys (now Allscripts) overseeing enterprise performance management solutions. Before then, John worked at the largest health system in Texas overseeing clinical informatics, decision support and business intelligence. During his tenure, Memorial Hermann won accolades for its financial performance as well as the National Quality Forum Award for exceptional clinical care. John has published on best practices in population health and presented at national forums on how information technology can improve medical outcomes. He holds a biochemistry degree from Harvard University and a graduate degree in clinical informatics from the University of Texas, School of Biomedical Informatics.

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