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… 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 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
Advances in computing power and in machine learning techniques are rapidly changing how humans utilize data. Aside from the core statistical issues, what are the questions that an analyst needs to consider when doing predictive modeling in practice? This post describes a few of these practical considerations. I like to use the 5 Ws to … Continue reading Practical considerations for Predictive Modeling
In this (technical) post, I illustrate how to calculate commonly used goodness of fit statistics. Goodness of fit statistics are essential components to doing statistical model building. They are how you tell whether your model is performing well ito explaining the patterns in your data. In practice, most of these are done automatically from the … Continue reading On being Sensitive and Specific – analytically speaking