Cloud-Based Data Platforms for Pharmacy Research Collaboration

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Deng Yu

Independent Researcher

Shangcheng District, Hangzhou, China (CN) – 310002

Abstract— Pharmacy research increasingly depends on accessing, combining, and analyzing large, heterogeneous datasets—ranging from electronic health records and dispensing logs to pharmacovigilance reports, wearables, and social determinants of health. Traditional, siloed infrastructures struggle to support this scale and complexity, slowing evidence generation and multi-center collaboration. Cloud-based data platforms offer elastic storage and compute, shared workspaces, and governed access to harmonized data assets, enabling faster, more reproducible studies while enforcing privacy and security. This manuscript proposes a comprehensive framework for cloud-enabled collaboration in pharmacy research. We synthesize literature on data models, governance, and privacy-preserving analytics (e.g., de-identification, federated learning, differential privacy); compare data lake, warehouse, and lakehouse paradigms; and discuss interoperability standards (FHIR, OMOP, CDISC) and vocabularies (SNOMED CT, RxNorm, ATC, MedDRA). We then outline a reference architecture and operating model spanning ingestion, curation, cataloging, lineage, access control, quality assurance, secure analytics, and audit.

A multi-center study protocol illustrates how the platform supports medication safety, pharmacoepidemiology, pharmacogenomics, and outcomes research with ethical safeguards and traceable workflows. Finally, we present results from a pilot-style evaluation using synthetic and limited de-identified data to benchmark performance, usability, data quality, and governance outcomes. The findings indicate marked improvements in query latency, time-to-collaboration, documentation completeness, and cross-site consistency, alongside high researcher satisfaction. We conclude with practical guidance on implementation sequencing, change management, and risk mitigation. Cloud-based data platforms, when aligned with FAIR principles and rigorous governance, can substantially accelerate trustworthy pharmacy research and translate to safer, more effective medication use.

Keywords

cloud data platforms; pharmacy research; interoperability; data governance; de-identification; federated learning; OMOP; FHIR; reproducibility; pharmacovigilance

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