Why Advanced Analytics Integration is the Key to Medical Device Development

Burke Blog Feature Image
April 10, 2019
Medical Ethics and the Impact of Technology
April 16, 2019

Why Advanced Analytics Integration is the Key to Medical Device Development

Advanced analytics

Advanced analytics must be a part of the process and not just an afterthought. Designing intelligence (even AI) into a connected medical device first depends on whether the data is being used to make a real-time decision or report on the outcome of a series of events.

Most companies don’t realize the different layers of advanced analytics that create actionable intelligence. They may include:

  • Simple rule- and complex rule-based analytics
  • Asynchronous event rules
  • Complex event processing, and
  • Unsupervised learning models

By understanding these layers, companies can move quickly into developing mature analytics that have an impact from day one.

As a company matures its analytics system from descriptive and diagnostic to predictive and prescriptive, it should also evolve to include strategic opportunities to provide business value, including automating decisions that can be delegated to a smart decision-support system.

Successful integration

Successful integration involves viewing advanced analytics as an architecture and not as a single solution to be implemented. The best way to make sure that you are successful in analytic development is to follow a continual process of discovery, design, and delivery.

For instance, data science architecture may begin with a business question, requiring you to determine if you have the right data and can actually leverage that data in the existing IT system. If you don’t answer this basic question, you will have challenges fully vetting the analytic opportunities available to you.

Developing products inside of analytics

Successful integration hinges on clearly identifying the data science process control. To design and support connected equipment analytics, data science should include clear steps such as,

  • analytics incubation
  • analytics validation
  • analytics enablement
  • analytic consumption
  • analytic maintenance.

While the first two steps are where a data scientist will play a vital role, the subsequent three steps are what will ultimately lead to successful implementation and require strong organizational cross-functional support.

Leave a Reply

Your email address will not be published. Required fields are marked *