Distributed Solutions for a Reliable Data-Driven Transformation of Healthcare Management and Research

Modern healthcare management and clinical practice strongly rely on data and scientific evidence. The healthcare sector is a problem-driven, data-intensive domain where the ability to access, edit and trust data emerging from its activities are critical for the operations of the sector as a whole (1). As a consequence, digital technologies, tools, and services became a core component of both Healthcare Management and scientific Research (HMR), as confirmed by the WHO guideline and recommendations on digital interventions for health system strengthening (2). Particularly, HMR is increasingly adopting digital tools that allow data to be shared between geographically distant subjects (3–7). Data interoperability, security, privacy and ease of sharing are core components of high quality HMR. A panoply of solutions is already available to create, collect and share different types of data (6–9) originating from different sources (clinical records, current or ad hoc data streams, directly from patients or published studies). Current data management software in HMR are primarily built on two technological infrastructures: Cloud-Based (CB) or Distributed Ledger Systems (DLTs). Emerging DLTs technologies offer alternative solutions for the management and sharing of data in HMR and can help increase trust in data's integrity and the resulting evidence. As pointed out by many authors, applications of DLTs in HMR are growing rapidly (1, 10) and they will affect the healthcare domain, specifically patient-centered care (e.g., data ownership and personal health records), and healthcare and research management (e.g., regulatory compliance, providing a decentralized framework for sharing Electronic Health Records (EHR), clinical trials). The aim of this paper is to shed light on CB and DLT solutions, emphasizing the potential role of innovative digital solutions based on DLTs in creating a data-driven transformation of HMR, and to describe relevant examples and practical uses of DLT-based solutions for patients, healthcare management, and research activities.