Learn to Crunch Big Data with R

Get started using the open source R programming language to do statistical computing and graphics on large data sets

Become An Insider

Sign up now and get FREE access to hundreds of Insider articles, guides, reviews, interviews, blogs, and other premium content. Learn more.

A few years ago I was the CTO and co-founder of a startup in the medical practice management software space. One of the problems we were trying to solve was how medical office visit schedules can optimize everyone’s time. Too often, office visits are scheduled to optimize the physician’s time, and patients have to wait way too long in overcrowded waiting rooms in the company of people coughing contagious diseases out their lungs.

[ Related: 8 Analytics Trends to Watch in 2015 ]

One of my co-founders, a hospital medical director, had a multivariate linear model that could predict the required length for an office visit based on the reason for the visit, whether the patient needs a translator, the average historical visit lengths of both doctor and patient, and other possibly relevant factors. One of the subsystems I needed to build was a monthly regression task to update all of the coefficients in the model based on historical data.

After exploring many options, I chose to implement this piece in R, taking advantage of the wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques implemented in the R system.

To continue reading this article register now

Notice to our Readers
We're now using social media to take your comments and feedback. Learn more about this here.