A novel approach to predictive analytics could make the difference.

What if you could diagnose patients sooner, start treatment earlier, and prevent symptoms from worsening?

The secrets to developing more effective treatments and enabling better outcomes likely reside in the volumes of data captured from sources such as patient registries, administrative claims databases, patient and provider surveys, and electronic medical records. This is what the life sciences industry terms real-world data: “…data used for decision making that are not collected in conventional controlled randomized trials (RCTs).”[1]

Many companies, though, struggle to derive clear and useful insight from what is effectively a massive “chopped salad” of information that resides in disparate locations and formats.

In our experience, two things can accelerate analysis of complex real-world data and development of more effective treatment approaches:

  1. Analytic tools that have the necessary power to identify the complex variable interactions that are predictive of diagnosis and treatment efficacy
  2. A discovery-driven research approach that uses data analytics to reveal answers to challenging questions—as opposed to traditional hypothesis-based approaches that test pre-defined theories

This article describes, at a high level, how this is possible.

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