# Learning as a missing component of digital health, environment and climate change

**Authors:** Maeghan Orton, Gabrielle Samuel, Mats Blakstad, Peter Benjamin, Javier Elkin, Oscar Franco-Suarez, Felix Holl, Sarah J. Iribarren, Richard Holman Matanta, Kimberly A. Hill, Matt Hulse, Dimitrios Kalogeropoulos, Andrew Karlyn, Tanjir Rashid Soron, Anicia Santos, Temitayo Tella-Lah, Peter Drury

PMC · DOI: 10.1038/s41746-025-02080-5 · NPJ Digital Medicine · 2025-10-29

## TL;DR

Digital health is missing a focus on learning about climate change and environmental issues, which is essential for effective mitigation and adaptation strategies.

## Contribution

The paper introduces climate and environment learning as a novel and critical dimension in digital health that has been overlooked.

## Key findings

- Climate and environment learning is a systematic approach to using data analytics for mitigation and adaptation decisions.
- The WHO’s Digital Health Classification framework can help formalize this learning into practice.
- A shared language of metrics and evidence is needed to develop this learning into a research agenda.

## Abstract

Despite its rapid advancement, digital health has little considered issues of climate change or environmental degradation. As the digital health community begin to engage with this critical issue scholars have started mapping progression in the field, typically focusing on the relationship between digital health as it applies to climate and/or environmental mitigation or climate adaptation. In this Comment, we argue that climate and environment learning for mitigation and adaptation constitutes a critical yet overlooked dimension intersecting mitigation and adaptation strategies, warranting deliberate attention. This learning category is the systematic and transparent approach that applies structured and replicable methods to identify, appraise, and make use of evidence from data analytics across decision-making processes related to mitigation and adaptation, including for implementation, and informs the exchange of new best practices in a post-climate era. The WHO’s Digital Health Classification framework offers a good option for ultimately formalising learning into practice. As a foundational step, however, learning needs to be conceptualised and developed into its own research agenda, organised around a shared language of metrics and evidence. We call on actors in the digital health field to develop this concrete strategy and initiate this process.

## Full-text entities

- **Diseases:** infectious disease (MESH:D003141)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12569053/full.md

## Figures

1 figure with captions in the complete paper: https://tomesphere.com/paper/PMC12569053/full.md

## References

17 references — full list in the complete paper: https://tomesphere.com/paper/PMC12569053/full.md

---
Source: https://tomesphere.com/paper/PMC12569053