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4 key elements of an advanced analytics plan

By Andrew Rebhan

March 22, 2017

    Analytics is a familiar topic for IT leaders, but it's evolving rapidly, opening up new opportunities in the health care industry. Electronic health records, biomedical devices, patient supplied data, and new diagnostic technologies such as genomics have dramatically expanded the pool of data that health systems can analyze.

    To provide value, raw data need to be turned into actionable information. Recent progress in advanced analytical techniques, including machine learning, text analytics, big data, and predictive and prescriptive modeling have made it easier to turn raw data into high-value information. These techniques, coupled with the influx of new data, can provide powerful insight for improving clinical and financial results, as well as patient satisfaction.

    Organizations should have a strategic plan to guide the implementation of their analytics program. Progressing through the different levels of analytics maturity (e.g., descriptive, predictive, prescriptive) delivers increasing competitive advantage, but also demands greater IT sophistication and organizational commitment. Health systems need to build up several capabilities when preparing for an advanced analytics program, including a robust architecture, governance structure, self-service business intelligence (BI) capabilities, and data sources.

    Here are four steps on how to plan and execute your analytics strategy:

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