Why Advanced Analytics Integration is the Key to Medical Device Development

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April 10, 2019
Medical Ethics and the Impact of Technology
April 16, 2019

Why Advanced Analytics Integration is the Key to Medical Device Development

The FDA’s approval of an electrocardiogram (EKG) that enables atrial fibrillation detection right from a patient’s watch band is just one example of how the digitization of medical devices, a part of the Internet of Things movement, is leading product development and innovation in medicine. However, while medical devices built on a connected services platform include components for data storage, security, accessibility, and mobile applications, along with advanced analytics, successfully implementing artificial intelligence to drive actionable intelligence remains a challenge from an execution perspective.

According to Gartner, 85% of data science projects fail.  Successful integration of data science into medical device development requires a rethinking around the role of data science in product design and life-cycle management.

While data science is rightly defined as the process of using mathematical algorithms to automate, predict, control or describe an interaction in the physical world, it must be viewed as a product. This distinction is necessary because, like any medical product, data science begins with a need and ends with something that provides clear medical utility for healthcare providers and patients.

It is erroneous to restrict the realm of data science to just the designing of algorithms. While data scientists are good at fitting models, their true value comes from solving real-world problems with fitted data models

What it takes to develop a medical device algorithm

A successful algorithm development process in data science includes business leaders, product engineers, medical practitioners, and data scientists collaborating to discover, design and deliver. For instance, a typical data science integration with a medical device product would include many of the following activities:

    • Identifying the medical need
    • Identifying proper data variables
    • Developing the right analytic models
    • Designing analytic algorithm integrations
    • Performing testing and verification
    • Deploying beta versions
    • Monitoring real-time results
    • Maintaining and updating algorithms

Considering data science as a product or feature of a product provides organizations with a different paradigm for execution focused on a tangible outcome. Data scientists are trained to develop accurate models that solve a problem, but the challenge many companies face is operationalizing those models and monetizing their outputs. Furthermore, conceptualizing data science as a product will ensure companies focus on its implementation, rather than just its development.

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