Incentives, as some economists would suggest, drive the world. Whether someone is doing things to fill their stomach, pocket, ego, heart, there are underlying motivations that initiate and then sustain someone's actions. I've found that a clear understanding of these incentives is critically important in life. More specifically, if I wish to analyze healthcare systems and … Continue reading What’s in it for me anyway? – incentives in healthcare
Category: healthdata 101
A health data whisperer’s tricks
Some of you may remember the 1998 movie The Horse Whisperer. In it, Robert Redford’s character, who had a remarkable talent to understand horses, was able to help a horse and its owner overcome fears and recover from a tragic accident. It’s a lovely slow paced movie, well worth enjoying a popcorn over 🙂 In … Continue reading A health data whisperer’s tricks
Health Insurance Analytics Metrics
Health insurance is primarily in the business of receiving premium from policyholders and paying for their medical expenditure. To do that, the health insurance companies need to ensure that the premium charged is sufficient to pay for these medical and administrative costs. Analysts new to health insurance might find these concepts difficult to grasp. Some … Continue reading Health Insurance Analytics Metrics
EHRs, what’s all the fuss about?
EHRs… ahhh, love them, hate them. If you do any work in the medical space, you will need to interact with them. In this post, I talk about what they are, what they do, their strengths and weaknesses. What are they? EHRs (electronic health records) are digital records of patient’s clinical records. EHR platforms are software platforms … Continue reading EHRs, what’s all the fuss about?
Feature engineering for health analytics
Feature engineering is an important step in analytics. Some may say this is THE most important step. I typically spend between 50-70% of the time of an analytic exercise on feature engineering. What is it: In the predictive modeling context, features are basically elements of the data. E.g. age of patients, diagnoses and procedures patients have had … Continue reading Feature engineering for health analytics