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Can healthcare big data reality live up to its promise?

Brian Eastwood | Aug. 16, 2013
If any industry's ready to reap the benefits of analyzing reams of data from disparate sources, it's healthcare. But just because there's a will doesn't necessarily mean there's an easy way.

Between electronic health record (EHR) systems, imaging systems, electronic prescribing software, healthcare claims, public health reports and the burgeoning market of wellness apps and mobile health devices, the healthcare industry is full of data that's just waiting to be dissected.

This data analysis holds much promise for an industry desperately seeking ways to cut costs, improve efficiency and provide better care. There are victories to be had, to be sure, but getting data from disparate, often proprietary systems is an onerous process that, for some institutions, borders on impossible.

Data Is data, no matter the source
Generally speaking, most healthcare organizations' data comes from clinical, financial or operational applications. On its own, each type of data has a specific use, which The Institute for Health Technology Transformation (iHT2) outlines in a report, Analytics: The Nervous System of IT-Enabled Healthcare. Clinical data improves care quality and eases population health management; financial data helps hospitals conduct cost analyses pertaining to the bottom line, and operational data examines facilities management and resource utilization.

Put it all together, and organizations can start to assess larger issues such as staffing needs, efficiency and care quality. That's why Laura Madsen, business intelligence (BI) evangelist and healthcare services lead at Lancet Software, sees no need to differentiate among the different types of data sources. "Data is data," she says. "At the end of the day, it's just bits and bytes...If we're good data professionals, we should be integrating clinical data and business data."

Government programs-and mandates-place added pressure on the healthcare industry while giving organizations reasons to take a good, hard look at analytics. The meaningful use program that offers financial incentives to use EHR systems, the accountable care organization (ACO) model of coordinated patient care, the concept of the patient-centered medical home and the increased emphasis on improving care quality all require a more sophisticated approach to healthcare data analytics.

Abundant unstructured health data makes analysis difficult
Of course, organizations can't analyze data without first collecting it. In healthcare, the iHT2 notes, several factors complicate this. As much as 80 percent of healthcare data is unstructured, whether it's in paper format or in free-form fields that need to be manually abstracted, and even the structured data-that which comes from the health information exchange (HIE) process, for example-is often inadequate for analysis. As a result, the report continues, providers end up using claims data from insurance companies to get a broad view of their own organizations.

When it comes to healthcare BI, size matters, says Madsen, who literally wrote the book on the topic. The country's largest providers, namely Intermountain Healthcare and Kaiser Permanente, have been doing it for a long time, but the gap is "huge" for smaller providers. Most of these organizations see the value of BI, Madsen continues, but they can't come up with a clear answer to the question, "What should we be doing?"


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