# Effective Monitoring of Online Decision-Making Algorithms in Digital Intervention Implementation

arXiv: https://arxiv.org/abs/2409.10526 · HTML: https://mlsanity.com/paper/2409.10526/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Power Constrained Bandits](https://mlsanity.com/paper/2004.06230/) (2020)
- [Action Centered Contextual Bandits](https://mlsanity.com/paper/1711.03596/) (2017)
- [Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines](https://mlsanity.com/paper/2206.03944/) (2022)
- [Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care](https://mlsanity.com/paper/2208.07406/) (2022)
- [Statistical Inference After Adaptive Sampling for Longitudinal Data](https://mlsanity.com/paper/2202.07098/) (2022)
- [reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use](https://mlsanity.com/paper/2402.17739/) (2024)
- [Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials](https://mlsanity.com/paper/2402.17003/) (2024)

## Built on by (most cited first, 1 of 1)

- [Reproducible workflow for online AI in digital health](https://mlsanity.com/paper/2509.13499/) (2025)

## Abstract (arXiv)

Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, the delivery of treatment based on accruing data. The objective of this paper is to provide guidelines for enabling effective monitoring of online decision-making algorithms with the goal of (1) safeguarding individuals and (2) ensuring data quality. We elucidate guidelines and discuss our experience in monitoring online decision-making algorithms in two digital intervention clinical trials (Oralytics and MiWaves). Our guidelines include (1) developing fallback methods, pre-specified procedures executed when an issue occurs, and (2) identifying potential issues categorizing them by severity (red, yellow, and green). Across both trials, the monitoring systems detected real-time issues such as out-of-memory issues, database timeout, and failed communication with an external source. Fallback methods prevented participants from not receiving any treatment during the trial and also prevented the use of incorrect data in statistical analyses. These trials provide case studies for how health scientists can build monitoring systems for their digital intervention. Without these algorithm monitoring systems, critical issues would have gone undetected and unresolved. Instead, these monitoring systems safeguarded participants and ensured the quality of the resulting data for updating the intervention and facilitating scientific discovery. These monitoring guidelines and findings give digital intervention teams the confidence to include online decision-making algorithms in digital interventions.