Are you aware of the ASPPH as a public health organization, but you just don’t know what it does, or how it fits into the bigger picture? I give a quick explainer of the ASPPH and its role in public health education.
Tag Archives: teaching approaches
I describe the three steps of my alternative model to the Plan-Do-Study-Act (PDSA) model for quality assurance/quality improvement (QA/QI) in healthcare.
Want an alternative to the Plan-Do-Study-Act (PDSA) model for quality assurance/quality improvement (QA/QI) in healthcare? I recommend approaching QA/QI a different way, by thinking about the various functions of the QA/QI department.
As a data science leader, what should you put in place so your organization doesn’t end up a data mess like startup Theranos? This blog posts provides guidance.
The Plan-Do-Study-Act model is promoted for quality assurance/quality improvement in healthcare. But does it have any peer-reviewed evidence base behind it? I examine that in this blog post.
What are the stages of the PDSA model, and how do they relate to the functions of a QA/QI department in healthcare? The answers are not straightforward. I examine these issues in this blog post.
Wondering what the Plan-Do-Study-Act (PDSA) Model is, and if you should adopt it for quality improvement in healthcare? Read my series of blog posts on the subject for my personal experience and recommendations
This is my first blog post in a series of five where I talk about data-related misconduct outlined in the book “Bad Blood”, and provide guidance on how to prevent it.
We experience artificial intelligence all the time on the internet in terms of friend suggestions on social media, internet ads that reflect what we have been searching for, and “smart” recommendations from online stores. But the reality is that even the people who build those formulas cannot usually explain why you were shown a certain […]
This lively panel discussed many topics around designing and implementing machine learning pipelines. Two main issues were identified. The first is that you really have to take some time to do exploratory research and define the problem. The second is that you need to also understand the business rules and context behind the data.