Predicting Healthcare Provider Engagement in SMS Campaigns

TL;DR

This paper analyzes factors influencing healthcare providers' engagement with SMS campaigns by examining millions of messages using various machine learning models to identify key drivers of response behavior.

Contribution

It introduces a comprehensive analysis of message features affecting provider engagement and compares multiple predictive models for this purpose.

Findings

Certain message features significantly increase click likelihood

Machine learning models outperform baseline methods

Insights inform more effective healthcare SMS strategies

Abstract

As digital communication grows in importance when connecting with healthcare providers, traditional behavioral and content message features are imbued with renewed significance. If one is to meaningfully connect with them, it is crucial to understand what drives them to engage and respond. In this study, the authors analyzed several million text messages sent through the Impiricus platform to learn which factors influenced whether or not a doctor clicked on a link in a message. Several key insights came to light through the use of logistic regression, random forest, and neural network models, the details of which the authors discuss in this paper.