# Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx

arXiv: https://arxiv.org/abs/2408.08024 · HTML: https://mlsanity.com/paper/2408.08024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 67 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 24 of 24)

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- [Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services](https://mlsanity.com/paper/2408.07647/) (2024)

## Abstract (arXiv)

This paper introduces a reinforcement learning (RL) platform that enhances end-to-end user journeys in healthcare digital tools through personalization. We explore a case study with SwipeRx, the most popular all-in-one app for pharmacists in Southeast Asia, demonstrating how the platform can be used to personalize and adapt user experiences. Our RL framework is tested through a series of experiments with product recommendations tailored to each pharmacy based on real-time information on their purchasing history and in-app engagement, showing a significant increase in basket size. By integrating adaptive interventions into existing mobile health solutions and enriching user journeys, our platform offers a scalable solution to improve pharmaceutical supply chain management, health worker capacity building, and clinical decision and patient care, ultimately contributing to better healthcare outcomes.