Six Mistakes You Can Avoid in Healthcare Data Science

Learn the six most common mistakes made in healthcare data science. Download the white paper for detailed insights into some of the the most common errors healthcare data scientists make, why they make them, and the ways to avoid them including:

  • Not Predicting Impactable Risk
  • Not Anticipating Deployment
  • Data Leakage
  • Inadvertently Introducing Bias and more

Download the White Paper

First Name

Last Name



Company Name

Company Size


Here is your file to download.
Oops! Something went wrong while submitting the form.

Six Mistakes You Can Avoid in Healthcare Data Science

Healthcare data scientists must confront a host of challenges that do not exist in other industries. The fact that many data scientists come to healthcare from non-healthcare backgrounds means they will not be familiar with the subtle-yet-vital details waiting for them. Using all-purpose tools makes avoiding them effortful even for data scientists that are skilled in the profession’s best practices.

Interested in learning more about how data scientists can overcome and avoid healthcare-specific challenges? Check out these related resources:

Back to all resources

See What ClosedLoop Can Do For You.

See How ClosedLoop Can Improve Patient Outcomes & Lower the Total Cost of Care

Get in touch today to see the ClosedLoop platform in action.

Talk To An Expert