Posts – Data Science/AI/Machine Learning

Reducing Patient Costs through Machine Learning and Comparable Supply Categorization

  Written by Megan Bultema, Chief Data Scientist and Jackie Kokx, Data Scientist Until recently, clinical practitioners focused almost exclusively on providing individual patients with safe and accurate treatment. Physicians favoured methods that yielded the best outcome for patients solely on a case-by-case basis.   The needs of the medical sector, however, are changing. An […]

Artificial Intelligence in Healthcare is Driving Innovation in Data Analytics

Artificial intelligence applied to healthcare wasn’t on the radar ten years ago. Outside of academic circles, relatively few people were even aware of the potential of the technology. It seemed like a distant fantasy. Today, though, that’s all changed. Artificial intelligence is the new trend in innovation, and everyone in the sector is talking about […]

Combine your Powers of AI-enabled Data Analytics, Machine Learning and Expert Systems for Maximum Return

Written by Justin Schaper, Chief Technology Officer and Megan Bultema, Chief Data Scientist Artificial Intelligence or AI is revolutionizing healthcare data analytics and changing the way we predict, learn, and act based on insights gained through AI-powered data models. At Empiric Health, we have learned that solving healthcare problems requires multiple types of solutions and […]

Empiric Health: Using Analytics to Unlock Improvements in Surgery (BurstIQ)

Empiric Health’s partner, BurstIQ, highlights how using the BurstIQ Platform has enabled us to deploy an innovative AI-driven clinical analytics suite in order to identify unwarranted clinical variation, improve patient outcomes and increase the affordability of surgery. Empiric’s core methodology refines surgical cases into well-defined buckets, like Granny Smith apples to Granny Smith apples, and […]

Why Healthcare Data Fails to be Information

And How to Build Actionable Insights from Healthcare Data Written by Megan Bultema, Ph.D., Chief Data Scientist and Ben Doremus, B.S. in Electrical and Computer Engineering, Data Scientist DATA ≠ INFORMATION It is rare to find a modern healthcare system which does not purport to make “Evidence based decisions.” Even the most primitive surgical settings […]

Four Lessons for Working with Surgeons

Written by Wendy Gort, MBA, Data Scientist/Analyst   In my twelve years of experience as a healthcare data analyst, I’ve discovered that working with surgeons can be incredibly rewarding, yet equally challenging.  I’ve worked with many different types of clinicians, and while they all possess admirable qualities, I have grown to appreciate the unique relationship […]

Cohorts: Using More Meaningful Data to Identify Unwarranted Clinical Variation

Written by Megan Bultema, Ph.D., Chief Data Scientist Healthcare systems across the country rely on data to help solve one of the largest challenges they face — how to improve the value of the care they deliver to patients by reducing cost and improving quality. Data, however, is not a magic wand, and traditional ways of […]

Data Scientist Beware – Your Models Aren’t That Good – Here’s Why

Written by Damian Minglez, Chief Data Scientist, SwitchPoint Ventures and Empiric Data Science Team Many organizations think all Data Scientists are created equal (after all, they all have the same bag of tricks) and companies apply pressure to put models into production. Unfortunately, many Data Scientists are not challenged to justify their results and the organization […]