Predicting Healthcare Provider Engagement in SMS Campaigns
November 20, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Daanish Aleem Qureshi, Rafay Chaudhary, Kok Seng Tan, Or Maoz, Scott Burian, Michael Gelber, Phillip Hoon Kang, Alan George Labouseur
arXiv ID
2511.17658
Category
physics.soc-ph
Cross-listed
cs.AI,
cs.CY,
cs.LG,
stat.ML
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
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.
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