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    What should my strategy be for population health analytics?

    November 24th, 2015

    Forward-looking healthcare organizations know that big data and analytics will drive significant change in how they care for patients. A select group of healthcare leaders joined me recently in Chicago for an interactive Allscripts Analytics summit on this topic.

    We asked them: What are your high-level strategic priorities for population health analytics? Here are responses from the Berkshire Health Systems Chief Medical Information Officer Mark Snowise, M.D. and Health First System Vice President of Decision Support Analytics Frank Wang:

    Allscripts Analytics Asks: Mark Snowise, MD


    Allscripts Analytics Asks: Frank Wang


    Today’s clinicians need tools that help stratify populations, identify gaps in care, benchmark outcomes and provide pathways to patient compliance. Only then can they take action to improve care. That means they need point-of-care, real-time analytics, cohort management, predictive models and quality metrics and dashboards for population health insights across care settings.

    Where to begin?

    The very first step is to understand the data – only then can clinicians determine the right questions to ask. Then aggregation of disparate data will become the foundation for true population health analytics.

    Next, we have to balance transparency with vulnerability; security is always a top-of-mind concern. Finally, we have to be able to use new tools – machine learning, for example, is ideal for uncovering the opportunities hidden in big data and pushing beyond the scale limitations of human thinking and analysis. Spark, Hadoop and cloud computing all give us the flexibility, scalability and speed to track real populations in real time.

    One the four pillars of the Allscripts Analytics strategy is to use our large Allscripts data lake – a key asset for our company – with more than 40 million de-identified patient records. With this information, our team is developing new predictive models to determine who is at risk for asthma, for example, and how to determine diabetes outcomes for select at-risk populations based on Hba1c trends over time.

    We are taking population health analytics to the next level in our December V3.0 release. A few examples of how we are accomplishing this are through the expansion of our value-based care functionality and with the integration of big data visualization tools. Since 2006 we’ve empowered our clients to understand their data and take action at the point of care to achieve their strategic goals across their patient populations.

    If you’d like to learn more about Allscripts Analytics solution for population health analytics, contact us.

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