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.
Tag Archives: Statistics
30
Jun
Jun
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.
Quality Assurance/Quality Improvement (QA/QI)
“Bad Blood” Shows how Theranos was an Abject Failure in Data Stewardship: Part 3 of 5
28
Jun
Jun
The book “Bad Blood” describes the fall of startup unicorn Theranos, but also provides insight into the company’s abject failure at data stewardship, which I talk about in this blog post.
Quality Assurance/Quality Improvement (QA/QI)
“Bad Blood” Demonstrates how a Lack of Product Description Leads to Data Science Misconduct: Part 2 of 5
25
Jun
Jun
This blog post talks about how lack of product description led to data-related misconduct at Theranos, because they could never nail down exactly what they were trying to do.
Quality Assurance/Quality Improvement (QA/QI)
Quality Improvement in Healthcare: What is the PDSA Model, and How Well Does it Work for QA/QI? Part 1 of 5
16
Jun
Jun
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
17
Dec
Dec
While other countries have found a way to control their community spread of COVID-19 while waiting for the vaccine program to be implemented, the United States has totally failed at this. An epicurve is a diagram of the timing of an outbreak, and in other countries, this curve has been flattened. But in the United […]
15
Dec
Dec
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 […]
Career Development
Two Takeaways from Danny Ma’s Machine Learning Panel: Understanding the Problem, and Understanding your Data
04
Dec
Dec
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.
26
Nov
Nov
Get to know three of my favorite SAS documentation pages: the one with sort order, the one that lists all the SAS formats, and the one that explains all the SAS operators and expressions!
02
Nov
Nov
If you receive payroll in the US, you can see that the data on the payroll stub is pretty complicated. This course in payroll is helpful for data scientists who find themselves analyzing US payroll data, because it explains the business rules and regulations behind the data.
