The data collected (or potentially collected) by medical devices present a plethora of opportunities to be leveraged to demonstrate outcomes or create new data-enabled products. However, with these opportunities comes the challenge of integrating data science into existing product development practices.
In this guide, we demystify the domain of data science and articulate what’s actually involved, dive into the two main approaches to applying data science that medical device vendors can use to leverage their data and, lastly, review the most common pitfalls experienced by teams that lead to failed data science initiatives as well as how to avoid them.
You’ll walk away with:
A breakdown of the four components of a data science project
Two frameworks for leveraging medical device data
Five pitfalls we commonly see that cause projects to miss expectations
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