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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
This is the third 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. In previous posts, I described diagnosis and procedure codes. Here, I describe drug codes. Drug codes primarily record clinical/dosage information of … Continue reading Types of healthcare data – Drug codes
This post describes some of the commonly seen types of errors in healthcare data. Occasionally, memories flash across my mind, of the days I spent in maths lectures, of being amazed at the clean, efficient and elegant proofs that demonstrate the logical integrity of abstract maths theorems. Fast forward to today, I more often than … Continue reading Ugly side of healthcare data
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