Insight
Reducing implicit bias in predictive healthcare analytics
Machine learning and predictive analytics are a valuable tool in healthcare. Here’s how to avoid implicit bias in your outcomes and analytics.
Resources Predictive Analytics
Predictive Analytics content from Arcadia | Page 2
Explore our predictive analytics content and discover actionable insights for proactive healthcare decisions.
Content types:
Topics:
Insight
5 questions to ask before you use predictive algorithms in healthcare
Predictive analytics offer huge potential value to healthcare organizations — but only if you pick the right problems to solve.
Video
Get ahead of the 3 megatrends impacting healthcare in 2030
Accelerated innovation, public trust in the system, new payment models, and big data. Here’s NYT bestselling author Shawn DuBravac’s take on how these trends will impact that future of healthcare.
Press Release
Arcadia statement on predictive analytics and bias
We’re sharing the following statement with our customers who may have questions about our predictive algorithms in light of recent media coverage. For general questions, please contact hello@arcadia.io. Dear colleagues,...
Insight
Can data-driven care management help address provider burnout?
Within all the rightly harsh press about “physician burnout” there is a bright spot called predictive analytics. I will get to that in a moment. Let me set the stage with a few words about physician burnout. Physician...
Webinar
How to use predictive analytics for care management intervention
How do you find the most ‘actionable’ patients for care management — the ones who will most benefit from intervention? Dr. Rich Parker first saw the value of care management as a practicing physician: it enabled him to...
Data Gallery
A population under a microscope
Finding the demographic factors (including social determinants of health) that indicate potential for complex care management.
Data Gallery
The evolution of diabetes: 12 years of disease state transitions
EHR and claims data for 10 million patients show the evolution of diabetes, a glimpse at the most at-risk individuals and a way to prevent disease.