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    Experts Share How to Apply Big Data to Patient Care

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    The amount of patient healthcare data is increasing more and more rapidly. In fact, research firm IDC predicts that there will be an overall increase in health data of 48% annually. With so much big data, applying it to real-world examples is key.

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    Jonathan Weiner, codeveloper of the Johns Hopkins ACG System, and professor of health policy and management at the Johns Hopkins Bloomberg School of Public Health, says patient data, such as that from medical claims and pharmaceutical claims and EHRs, can combined with other information, like local demographics, to reveal patterns, trends, and associations.

    “The ‘garbage in, garbage out’ problem occurs when data comes in at irregular intervals or isn’t adequately linked to other relevant pieces of information,” says Weiner. “The challenge is to structure miscellaneous data to get a useful picture of individual or community health.”

    The ACG team, along with the Johns Hopkins Center for Population Health IT, works directly with organizations on a case-by-case basis to determine the most useful ways to link structured and unstructured medical, geographic and social data with insurance claims, medical administrative records, and other patient data for specific needs, according to Weiner.

    According to Weiner, who is also a professor of health informatics at the Johns Hopkins School of Medicine’s division of health sciences informatics, healthcare organizations must have IT tools that attempt to make sense of big data. “But this is an ongoing, evolving process,” he says. “Using technical and analytic tools is only half the equation. The other half is providing organizations with ongoing support and access to the latest concepts and innovation.”

    For example, in the U.K., providers are using the ACG System to combine primary and secondary care data with information from location and demography in ways that have led to innovative patient care strategies that reduce costs, improve outcomes and enhance patient experience, he says. 

    Here, Weiner, whose current research focuses on the application of EHRs and health IT for population-based applications such as performance measurement and predictive modeling/analytics, and Kumar Subramaniam, executive officer for Population Health Analytics at Johns Hopkins HealthCare Solutions, share their thoughts with Managed Healthcare Executive (MHE) on practical ways for healthcare executives to tap their data, as well as the specific challenges it poses for population health analytics. 

    MHE: Unfortunately, because of the lack of interconnectivity between systems, apparent in the EHR world as well as within MCOs; definitional differences; sloppy data accumulation; as well as a host of other issues, much of data to information in the industry is still GIGO (garbage in, garbage out). What’s your perspective on remedies for this?

    Weiner: The challenge that healthcare administrators face right now is how best to translate all the available patient and population health data into practical solutions that improve standards of care in the real-world care settings. The practice of slicing and dicing data occurs within an industry with incompatible and idiosyncratic data collection. As a result, the overall industry is data-rich but information-poor. The fact is that most IT vendors come with a significant investment burden, which is especially challenging for small/single providers who are being crowded out and at-risk of abandoning their practices.

    Our experience shows that a little data can often go a long way. Standardization is becoming more common and easier. For example, the NQF certified HealthPartners’ Total Cost of Care model provides a common means of standardizing costs across disparate data sources. 

    That said, the challenges related to analytics and big data are real and we plan to address at our international conference [in April 2018], which is open to all healthcare professionals for the first time this year.

    Next: Pop health progress

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