Population health, predictive analytics, clinical decision support, and AI (artificial intelligence) currently lead health care’s hype list of innovative big-data strategies. However, with the passage of the Affordable Care Act, MACRA, and the Centers for Medicare and Medicaid’s implementation of value-based purchasing arrangements in Medicare, operational performance improvement has become imperative for health care organizations.
To support value-based purchasing, performance improvement initiatives require that data be aggregated, harmonized, and analyzed from many perspectives – operational efficiency, cost, clinical practice variation, quality, and patient satisfaction. However, the effective implementation of rapid and continuous operational performance improvement has been elusive due in large part to the complexity of curating disparate data in order to make it fit for purpose. This data curation is necessary in order to understand the cause, effect, and impact various metrics have across domains. The ability to understand the impact of particular measures is key. Dr. Edwards Deming, the leading thinker on quality management, famously said, “Measurement without the opportunity to improve is harassment,” and certainly physicians, already burned out from EMR and regulatory fatigue, don’t need projects without clear benefits.
Performance analytics, which provides access to key operational metrics and analytic capabilities, is a big-data approach to addressing these imperatives. Turning that approach into an improvement strategy requires developing a methodology for using data…