# Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services

arXiv: https://arxiv.org/abs/2408.07647 · HTML: https://mlsanity.com/paper/2408.07647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Revisiting Fundamentals of Experience Replay](https://mlsanity.com/paper/2007.06700/) (2020)
- [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://mlsanity.com/paper/1802.09127/) (2018)
- [Neural Contextual Bandits with Deep Representation and Shallow Exploration](https://mlsanity.com/paper/2012.01780/) (2020)
- [Power Constrained Bandits](https://mlsanity.com/paper/2004.06230/) (2020)
- [A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit](https://mlsanity.com/paper/1510.00757/) (2015)
- [Efficient Online Bayesian Inference for Neural Bandits](https://mlsanity.com/paper/2112.00195/) (2021)
- [Online Limited Memory Neural-Linear Bandits with Likelihood Matching](https://mlsanity.com/paper/2102.03799/) (2021)

## Built on by (most cited first, 2 of 2)

- [Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx](https://mlsanity.com/paper/2408.08024/) (2024)
- [From Restless to Contextual: A Thresholding Bandit Reformulation For Finite-horizon Improvement](https://mlsanity.com/paper/2502.05145/) (2025)

## Abstract (arXiv)

Pharmacies are critical in healthcare systems, particularly in low- and middle-income countries. Procuring pharmacists with the right behavioral interventions or nudges can enhance their skills, public health awareness, and pharmacy inventory management, ensuring access to essential medicines that ultimately benefit their patients. We introduce a reinforcement learning operational system to deliver personalized behavioral interventions through mobile health applications. We illustrate its potential by discussing a series of initial experiments run with SwipeRx, an all-in-one app for pharmacists, including B2B e-commerce, in Indonesia. The proposed method has broader applications extending beyond pharmacy operations to optimize healthcare delivery.