Podcast: Data Science in Healthcare

Episode 3 of my new podcast series, The BaDFun Podcast, is now live. The title is “Embracing data science in healthcare, with Siddharth Mahapatra”. Here’s the blurb:

Siddharth Mahapatra podcast photo

“This episode features Siddharth Mahapatra, director of master data management for one of the largest healthcare systems in the US. Siddharth speaks about how healthcare providers are embracing data science, in directions like

  • capacity planning,
  • reducing waste by matching purchasing to predicted demand,
  • overcoming information hoarding, silos, and data quality issues,
  • making data accessible and trackable,
  • reconciling data sets that originate from different sources,
  • co-creating data solutions with end users to ensure adoption,
  • the importance of domain knowledge as well as data literacy in both managing and visualizing clinical data,
  • eliminating cultural biases in data capture and analysis and in patient experience,
  • and the responsibilities that accompany data ownership.”

Please check it out and let me know what you think. And subscribe if you want to hear more.

Understanding Decisions Made By Machine Learning

blackboard
Photo by Roman Mager on Unsplash

What’s so important about interpretability in machine learning?

It’s a poorly kept secret that we lack insight into how complex machine learning models like neural networks make decisions. We see the data that goes in, the math that goes in, and the results that come out. But in the middle, where we want to see a chain of reasoning like a human could give us to explain decisions, there’s only a black box. Neither data scientists nor these complex machine learning models can provide insight into “why” a model chose output A rather than output B.

What does it matter whether we have an understandable explanation for why a machine learning model delivers a specific result? For example, when diagnosing whether or not a patient has cancer, isn’t it enough that the model is accurate, according to rigorous testing? I’ll look deeper into the implications of interpretability in future blog posts. But Continue reading “Understanding Decisions Made By Machine Learning”

When Are We Impulsively Brilliant?

Don’t we all wish we could be impulsively brilliant at everything we try?

In this post—which I published to LinkedIn this time—I mash up the wisdom of Gary V. and Stripe CEO Claire Hughes Johnson to briefly examine how we can find a balance between impulsivity and commitment in our professional and personal brands.

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Photo credit: Scott Umstattd
 

Please join me in considering how we (and our teams) can be intentional without dissipating excitement or blunting momentum and speed of execution.

 

 

 

 

How to recruit social media advocates for your business

Word Gets AroundThe bigger a business is the more admirers they’ll naturally have. Even the smallest business is going to have some genuine admirers, if only friends and family.

In social media this admiration translates into what are commonly called “advocates”, people who talk online about the business they admire. They might be customers, they might be employees, they might be unrelated folks who simply have an interest in the subject matter—they might just plain like what you do. For you the key is that they talk about you because they want to.

Advocates can help you in a number of ways, including spreading the word about what you are doing and giving you a source of feedback about your brand.

Advocates versus Influencers

Advocates shouldn’t be confused with Continue reading “How to recruit social media advocates for your business”