Turn Data Science into Value: The Four Key Requirements
Delivering Value with Research-Based Data Science and Predictive Analytics
Judging by the buzz at HIMSS 2017, predictive analytics is reaching the peak of the hype cycle. Buzzwords used to describe it — like “cognitive computing,” “artificial intelligence (AI)” and “machine learning” — are often only brochure-level deep. But data science is science, after all, and must be validated through rigorous research and vetted by the academic community. The data science behind any predictive analytics model should go beyond marketecture, and be tested and proven through continuous research and critical peer review.
Predictive analytics solutions must offer research-driven data science, clinical actions and demonstrable results in order to deliver maximum value.
Predictive analytics solutions must offer research-driven data science, clinical actions and demonstrable results in order to deliver maximum value. But healthcare organizations struggle to understand how predictive analytics can be effectively used to drive these clinical and financial results.
To help healthcare organizations select an analytics partner, vendors need to show them what’s under the hood. Vendors should publish their work in leading medical journals to transparently detail the data science methods used and statistical results, and how their models improve health outcomes in real clinical settings. This type of rigor will not only curb skepticism, but also help ensure that predictive analytics is adopted in meaningful ways to optimize care and improve outcomes.
To do this and turn data science into value, a predictive analytics solution must meet four requirements.
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