The healthcare sector is a typical example where sharing personal data between organizations is essential, and access to these data is intrinsically distributed. Healthcare professionals in many organizations need to analyze patient data to perform their tasks. However, these data are typically stored in silos in distinct locations and different formats, making it difficult to share. Thus, the complexity of the medical system prevents the patient’s entire medical history from being easily accessed when needed. In this way, much information is lost or exhaustively repeated, making the diagnosis and treatment of the patient difficult and harming the patient’s journey.

According to research from the Johns Hopkins American Hospital, medical errors rank as the third leading cause of death in the United States, often stemming from systemic issues like poorly coordinated care ^[1]. Overcoming the challenge of coordinating patient care can be achieved through secure and accurate sharing of patients’ data, granting healthcare teams access to comprehensive health histories, facilitating early diagnosis, and improving treatment efficacy. Achieving these benefits is made possible through standardized electronic medical records (EMRs) stored in computerized healthcare environments, containing vital personal information like diagnoses and treatments, distributed among hospitals and clinics where the patient received treatment. EMRs streamline patient data monitoring and access, enabling seamless care integration between medical teams and health facilities, thus providing patients with various levels of care with pertinent medical information. While sharing these data benefits the patient, leading to more accurate diagnoses and appropriate treatments, it poses a significant challenge concerning privacy and security, given the highly sensitive nature of the information stored in EMRs. Often, patient data are shared without explicit consent among untrusted entities such as healthcare professionals, pharmacies, patient families, and other physicians ^[2]. Although efforts are made to share patient data through secure institutional medical systems, non-institutionalized and insecure means of communication are sometimes used for simplicity and immediacy. During the COVID-19 pandemic, there has been a notable emphasis on streamlining consultations and enhancing information exchange among patients, healthcare providers, and health organizations. Consequently, patient records have gained increased importance in public health ^[3], as they offer valuable data on diagnoses and prescribed medications, enabling identifying individuals belonging to COVID-19 risk groups, among other applications. The broader availability of patient data in electronic formats has significant implications for decision making and continuity of care in both the public and private sectors, fostering seamless data exchange between these realms. Timely data regarding disease outbreaks is crucial in effectively coordinating national-level public health policies and prevention strategies.

The significance and relevance of data availability have been steadily increasing, with numerous establishments implementing this accessibility. In 2019, for instance, there was a notable rise in patient information in electronic format. Key improvements compared to 2018 included patient registration data (89% compared to 79%), the primary reasons for patient consultations (64% compared to 50%), and admission, transfer, and discharge records (56% compared to 43%) [5]^[4]. Notably, electronic systems in public establishments have seen remarkable growth in functionalities in recent years, particularly concerning the listing of all laboratory test results (from 17% in 2016 to 41% in 2019), patients using specific medications (from 18% in 2016 to 40% in 2019), and having medical prescriptions (from 29% to 51%) [5]^[4]. These improvements indicate an evolution in the level and complexity of adopted electronic systems, leading to reduced fragmentation in care provision, thus enhancing quality efficiency and minimizing gaps in care [6]^[5]. However, as data digitization practices advance and sensitive data generation increases significantly, the systems must address many challenges. EMR systems predominantly rely on centralized client–server architectures, where a central authority holds full access to the entire system. However, this architecture brings forth particular challenges concerning privacy and security. System vulnerabilities can lead to failures and create opportunities for cyber attackers to breach patient data [7]^[6]. Managing these systems becomes a delicate task, requiring preserving privacy while ensuring data accessibility for authorized entities. Moreover, records are frequently stored in fragmented formats within local databases, hindering patients from accessing a comprehensive, consolidated electronic medical record [8]^[7]. Data format standardization is fundamental for achieving interoperability within the healthcare sector, entailing a unified language for exchanging and interpreting medical data and enabling diverse systems to communicate seamlessly. However, attaining such standardization presents notable challenges due to the escalating number of healthcare applications, EMRs, and medical devices, which have led to a rapid proliferation of varied data formats. This fragmentation poses substantial hurdles for healthcare professionals, researchers, and policymakers aiming to harness the power of data to enhance patient care, advance research endeavors, and facilitate evidence-based decision making. Blockchain technology is emerging as a promising avenue for standardizing and achieving interoperability in EMRs. It aims to facilitate the verification and registration of EMRs through a consensus among peers participating in a peer-to-peer network. This approach ensures reliable execution of data access policies, thereby upholding data integrity, accountability, and non-repudiation [9]^[8]. Blockchain technology becomes particularly appealing for applications requiring input from multiple stakeholders, where trust is challenging to establish using conventional technologies.

2. Standards for Health Data Systems

Standards governing health data systems encompass a comprehensive set of norms, specifications, and guidelines designed to parameterize the collection, storage, processing, and sharing of clinical and administrative information within healthcare systems. Alongside standards for health systems, specific organizations contribute to standardizing communication methods between systems and structural norms for storing and representing clinical data, resulting in a diverse array of medical system standards worldwide. Several global initiatives have pioneered these efforts to establish standards and guidelines that transcend borders and sectors. The Observational Medical Outcomes Partnership (OMOP) (available at https://www.ohdsi.org/data-standardization/ (accessed on 24 September 2023)) initiative focuses on standardizing observational health data. By creating a common framework for representing population health data, OMOP enables more consistent and comparative analyses, providing valuable insights into medical outcomes. Another influential global initiative is Integrating the Healthcare Enterprise (IHE) (available at https://www.ihe.net/ (accessed on 24 September 2023)), which aims to promote interoperability among healthcare information systems. By defining integration profiles based on established standards, IHE facilitates harmonizing diverse systems, enhancing collaboration and data exchange among healthcare entities.

2.1. Standards for Electronic Medical Record Systems

Standards for electronic medical records systems are centrally focused on promoting interoperability between different health systems and applications, allowing the sharing and exchanging of health information securely, efficiently, and accurately. Such standards support the formulation of reference models aligned with laws and regulations and dedicated to developing new health applications. The Open Electronic Health Record (openEHR) is an organization dedicated to developing and maintaining software system specifications and standards for EMRs. While it proposes health system models, it does not create its applications. Instead, its primary contributions consist of two reference architectures designed to integrate health software solutions. openEHR specifies various system components alongside the architectures, encompassing communication, storage, integration, and data representation (available at https://openehr.org/developers (accessed on 24 September 2023)). One distinctive feature of the openEHR specifications is the adoption of a role-based approach, delegating healthcare professionals to define procedures and the initial level of data representation in the model, referred to as “archetypes”, which adapt to specific contexts. openEHR first specifies a general model organized into components. Each component and its specificities are detailed in the standard definitions. The two reference architectures specified by openEHR are particularizations of this general model. Figure 21 shows the organization of the specifications into functional blocks of the general model proposed by openEHR (available at https://specifications.openehr.org/releases/BASE/latest/architecture_overview.html (accessed on 24 September 2023)). These blocks are organized as follows: