In this post, I show you the core SQL commands that will address a lot of the data management work you will do as a health data analyst. If you work with data in a large organization that has lots of it, you probably use SQL. Put another way, knowing SQL will open the door … Continue reading An intro to SQL – your key to databases
This is the fourth post in a series in which I describe the common types of healthcare data you will come across, namely, diagnoses, procedures, demographic, drug, laboratory result data, clinical notes and financial data. A quick recap previous posts: Diagnosis codes, such as ICD9 and ICD10s, record the REASON for a visit to a … Continue reading What’s blood got to do with it – LOINC!
As mentioned before, I'm not a fan of using advanced analytic techniques for the sake of intellectual pursuits. I'm a HUGE fan of asking good questions, framing analysis well, knowing data well, quickly arriving at actionable insights. A trained data scientist's toolbox has many statistical techniques, each of which has strengths and weaknesses. A health … Continue reading Decision Trees – intro
In a previous post, I described how to format typical physician productivity data to enable further analyses, finding useful insight. I dig deeper to find more actionable insights in this post. Refresher: we had office visit data for 3 doctors over 2 month period; we added RVUs to the office visit procedure codes; we added … Continue reading Confessions of health data -2
In this post, I describe the statistical concepts that I have found most relevant in health data analytics. First and foremost, I’m not a fan of using advanced statistical techniques for the sake of using them. In healthcare, the audience of your analysis is often non-statisticians (bio statistics research arena aside), so advanced statistical concepts … Continue reading Just the Statistics you need