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Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue., Comment: This is an original manuscript of an article published by Taylor & Francis in Health Systems & Reform on 22 Oct 2024, available online: https://www.tandfonline.com/doi/10.1080/23288604.2024.2387138

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., Comment: Presented at the Third Workshop on End-to-End Customer Journey Optimization at KDD 2024 (KDD CJ Workshop '24), August 26, Barcelona, Spain

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., Comment: Presented at The First Workshop on AI Behavioral Science (AIBS'24) at KDD 2024, August 25, Barcelona, Spain

By providing evidence-based clinical decision support, digital tools and electronic health records can revolutionize patient management, especially in resource-poor settings where fewer health workers are available and often need more training. When these tools are integrated with AI, they can offer personalized support and adaptive interventions, effectively connecting community health workers (CHWs) and healthcare facilities. The CHARM (Community Health Access & Resource Management) app is an AI-native mobile app for CHWs. Developed through a joint partnership of Causal Foundry (CF) and mothers2mothers (m2m), CHARM empowers CHWs, mainly local women, by streamlining case management, enhancing learning, and improving communication. This paper details CHARM's development, integration, and upcoming reinforcement learning-based adaptive interventions, all aimed at enhancing health worker engagement, efficiency, and patient outcomes, thereby enhancing CHWs' capabilities and community health., Comment: Presented at the 7th epiDAMIK ACM SIGKDD International Workshop on Epidemiology meets Data Mining and Knowledge Discovery, August 26, 2024, Barcelona, Spain

Artificial Intelligence and digital health have the potential to transform global health. However, having access to representative data to test and validate algorithms in realistic production environments is essential. We introduce HealthSyn, an open-source synthetic data generator of user behavior for testing reinforcement learning algorithms in the context of mobile health interventions. The generator utilizes Markov processes to generate diverse user actions, with individual user behavioral patterns that can change in reaction to personalized interventions (i.e., reminders, recommendations, and incentives). These actions are translated into actual logs using an ML-purposed data schema specific to the mobile health application functionality included with HealthKit, and open-source SDK. The logs can be fed to pipelines to obtain user metrics. The generated data, which is based on real-world behaviors and simulation techniques, can be used to develop, test, and evaluate, both ML algorithms in research and end-to-end operational RL-based intervention delivery frameworks.

As video games attract more and more players, the major challenge for game studios is to retain them. We present a deep behavioral analysis of churn (game abandonment) and what we called "purchase churn" (the transition from paying to non-paying user). A series of churning behavior profiles are identified, which allows a classification of churners in terms of whether they eventually return to the game (false churners)--or start purchasing again (false purchase churners)--and their subsequent behavior. The impact of excluding some or all of these churners from the training sample is then explored in several churn and purchase churn prediction models. Our results suggest that discarding certain combinations of "zombies" (players whose activity is extremely sporadic) and false churners has a significant positive impact in all models considered., Comment: Churn Prediction, Ensemble Methods, Survival Analysis, On- line Games, User Behavior