Enhancing Feasibility and Integrity in Health Research: The S-FIPR Platform

Modern health and social science research faces well-documented challenges in reproducibility, transparency, and practical feasibility. A significant proportion of studies cannot be replicated, and incomplete reporting leads to wasted resources. Equally, many promising interventions fail to translate into practice due to unrecognized feasibility obstacles in planning and implementation. This article introduces S-FIPR (Software for Feasibility and Integrity of Research Projects), which is an integrated digital platform designed to improve research project rigor, transparency, and viability from inception to dissemination. We outline the background reproducibility and feasibility gaps that motivate the development of S-FIPR, then describe the platform’s concept, including its user roles, workflows, and core functional blocks. A central innovation is the Feasibility Index (F-Index), a composite score assessing proposals across multiple domains with full auditability and explainability. We detail S-FIPR’s approach to responsible AI governance, such as model cards and bias mitigation, ensuring AI features remain transparent and advisory. The platform’s alignment with international standards - CONSORT, PRISMA, STROBE, FAIR data principles, and GDPR - is mapped, underscoring compliance with best practices in reporting, data management, and privacy. An evaluation plan is presented, leveraging usability scales and technology acceptance frameworks (TAM/UTAUT) to assess user experience and key adoption metrics. We discuss ethical and legal considerations, including research ethics oversight and data protection. Finally, we address current limitations and outline a roadmap from a minimum viable product (MVP) through pilot studies to broader scale-up. By embedding evidence-based safeguards and guidance into the research lifecycle, S-FIPR aims to bolster the integrity and feasibility of health research projects in the digital era. Keywords: Research integrity, Feasibility, Reproducibility, Digital platform, F-Index, AI governance, CONSORT, FAIR data, Usability, Health research