Rene Casanova
Fernan A. Villa-Garzon
John W. Branch-Bedoya- Departamento de Ciencias de la Computación y la Decisión, Universidad Nacional de Colombia, Medellín, Colombia
Background: Health information systems (HIS) are critical for digital health transformation, yet fragmentation and poor interoperability adoption remains a major challenge.
Objectives: This study systematically reviews architectural patterns used in HIS and evaluates their alignment with ecosystem-level requirements.
Methods: Following PRISMA 2020 guidelines, a systematic literature review was conducted across Scopus, IEEE Xplore, PubMed, and Web of Science (2020–2025). Eligible studies described, evaluated, or proposed HIS solutions.
Results: From an initial set of 304 records, 89 met the inclusion criteria. Service-based and decentralized/distributed ledger architectures were predominant, with emerging models integrating edge computing and modular design. FHIR-based contracts are found as stabilizers of interfaces, enabling validation and reducing integration costs. However, gaps persist in cross-border care, sustainability, and artificial intelligence integration.
Conclusion: While microservices dominate current HIS architectures, achieving resilient, interoperable ecosystems requires greater architectural diversity and intersectoral collaboration.
1 Introduction
Digital health ecosystems rely on health information systems (HIS) as foundational components for delivering integrated and patient-centered care. These systems enable the capture, storage, and exchange of clinical and administrative data, which is essential for maintaining continuity of care, planning resources, and managing population health. However, their design and implementation must account for the inherent complexity of healthcare environments, which involve multiple stakeholders, heterogeneous processes, and dynamic regulatory frameworks.
Despite technological advances, healthcare ecosystems often function as collections of isolated and heterogeneous software solutions (1), lacking standardized integration frameworks. This fragmentation undermines interoperability and complicates efforts to establish sustainable, modular, and scalable digital health infrastructures. Over the past decade, software engineering has introduced multiple architectural strategies aiming at improving interoperability and resilience.
Among these, service-oriented and microservice architectures enhance modularity and deployability by decomposing systems into fine-grained, independently manageable components. Distributed and decentralized ledger-oriented architectures address data exchange trust, auditability, and security through shared and immutable transaction records. Layered architectures remain prevalent because of their capacity to separate concerns and facilitate maintainability. Edge-cloud computing architectures leverage distributed processing by bringing computation closer to data sources while ensuring elasticity and scalability through cloud integration. Process-driven and pipeline architectures support business intelligence and analytics workflows, improving transparency, adaptability, and workflow automation. Finally, machine learning and artificial intelligence integration architectures enable predictive modeling and adaptive decision support directly within clinical and administrative processes.
From an end-user perspective, the value of these architectural strategies lies in their ability to transform data into real-world actionable insights. Reliable architectures ensure that collected information remains accurate, complete, and timely, enabling consumers to make informed clinical and managerial decisions. Moreover, the quality of data generated and exchanged between systems directly influences the scope and reliability of artificial intelligence applications (2, 3), which depend on consistent, high-quality datasets for training and inference. By improving data integrity, well-designed architectures ultimately enhance the usability, trustworthiness, and impact of digital health tools throughout the continuum of healthcare provisioning.
Nevertheless, the adoption of best practices in real-world healthcare contexts remains inconsistent, and the extent to which these approaches support ecosystem-level principles such as openness, sustainability, and cross-border integration remains unclear. Furthermore, while reference models such as HL7 and open EHR have emerged as interoperability enablers, there is limited evidence on how these standards are combined with architectural strategies in complete production environments. A critical knowledge gap persists regarding which patterns dominate current HIS designs, which ones are emerging, and whether these architectures align with the requirements of complex digital health ecosystems, such as those conceptualized under Software Ecosystem (SECO) and System-of-Systems (SoS) paradigms.
To address this gap, we conducted a systematic literature review (SLR) following PRISMA 2020 guidelines, analyzing studies published between 2020 and 2025. The objective was to identify predominant architectural patterns in HIS, assess their application contexts, and evaluate their alignment with ecosystem-level requirements for digital health. This synthesis aims to inform researchers and practitioners about current trends and future directions for designing interoperable, resilient, and sustainable health information systems.
2 Methods
2.1 Protocol and registration
This study was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines (4) to ensure transparency and reproducibility. The review protocol was not registered in PROSPERO or similar registries due to its exploratory scope. A complete PRISMA 2020 checklist is provided in the Supplementary Material, and the flow diagram is depicted in Figure 2.
2.2 Information sources
Four major electronic databases were queried. The selected information sources are described in Table 1.
Table 1. Data sources included in this review.
The last search was conducted on 22 June 2025. The databases were selected for their broad coverage of healthcare and information technology literature. The exported files are available at the project repository.
2.3 Search strategy
Search strategies were designed to comprehensively capture studies related to health information systems and software architecture. Keyword combinations were constructed using Boolean operators and adapted to the syntax of each database. Searches were limited to the period January 2020 to June 2025 and targeted articles describing, evaluating, or proposing architectural models for health information systems.
The structure of the search strings combined controlled vocabulary (e.g., MeSH terms in PubMed) and free-text terms referring to “health information systems,” “architecture,” “software design,” and “interoperability.” Equivalent terms were harmonized across databases to ensure semantic consistency.
The following is the summary of search parameters:
• Databases queried:
○ Scopus
○ PubMed
○ IEEE Xplore
○ Web of Science
• Domains: Health information systems, software architecture, digital health ecosystems
• Time frame: 2020–2025
• Languages: English, Portuguese, and Spanish
• Last search update: 22 June 2025
The complete search strings and query syntax for each database are provided in the Supplementary Material.
2.4 Eligibility criteria
Elegibility criteria were defined to ensure methodological rigor and maintain alignment between the research objectives and the evidence included in this systematic review. Following PRISMA 2020 recommendations, criteria were applied in two sequential stages: (1) screening of titles and abstracts and (2) inclusion assessment for full-text articles.
2.4.1 Stage 1: Title and abstract screening
During the initial phase, studies were retained if they met all of the following criteria:
• Peer-reviewed source: Published in a peer-reviewed journal or conference proceeding between January 2020 and June 2025.
• Language: Written in English, Portuguese, or Spanish.
• Scope of contribution: Explicitly describe, evaluate, or propose a software architecture for HIS or a digital-health-related software solution.
• Originality: Not a review, book chapter, or secondary source that did not introduce a new architectural approach.
• Clarity: Contained sufficient methodological or conceptual detail to understand the proposed architectural contribution (studies that were too vague or purely conceptual without an architecture description were excluded).
2.4.2 Stage 2: Full-text inclusion assessment
Articles that passed the first stage were retrieved in full text and assessed against the following inclusion criteria:
• Domain specificity: The study explicitly addressed both healthcare and software architecture domains.
• Information sufficiency: The study provided adequate methodological, contextual, or design information to answer the research questions of this review.
• Relevance of focus: The study was not centered on a highly specific or niche topic outside the scope of the health information system architecture (e.g., isolated algorithmic or device-level implementations).
Studies that failed to meet one or more of these conditions were excluded, with reasons for exclusion depicted in the PRISMA flow diagram.
2.5 Data extraction
Data extraction focused on:
• Publication year
• Country of first author
• Type of creators:
○ Academy
○ Enterprise
○ Government
○ Collaborations
• Main application target (EHR, interoperability, IoT integration, etc.)
• Predominant architectural pattern
• Inclusion of digital ecosystem concepts
Data were coded using predefined categories based on similarities identified during preliminary screening. Extraction was carried out by two reviewers using a shared database to ensure consistency. Any disagreements were resolved through discussion.
2.6 Risk of bias assessment
To ensure methodological transparency and evaluate the credibility of the included evidence, a risk of bias assessment was conducted for all studies that met the full-text inclusion criteria.
Given the qualitative and conceptual nature of the research designs typically found in software architecture studies, the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research was selected as the most suitable instrument. This tool enables the appraisal of congruence between study objectives, methodological design, and reported findings.
Each article was independently assessed by two reviewers to examine the following key dimensions:
• Alignment between research objectives and study design
• Clarity of the context and representation of the phenomenon of interest
• Appropriateness of data collection and analysis methods
• Transparency in interpretation and logical support for conclusions
• Identification of potential researcher influence or interpretive bias
Discrepancies between reviewers were discussed until consensus was achieved. The individual item ratings were synthesized into a qualitative summary table (provided in the Supplementary Material), categorizing studies as having low, moderate, or high risk of bias.
The assessment informed the interpretation of findings in the Discussion section, particularly regarding the credibility, transferability, and dependability of architectural evidence across different digital health contexts.
2.7 Analysis plan
The analysis consisted of two strategies described in the following subsections and combined descriptive quantitative synthesis with interpretive narrative analysis to capture both the structural patterns and contextual nuances of health information system architectures.
2.7.1 Quantitative analysis
Descriptive analyses were used to summarize the main characteristics of the study. Data were extracted from the review database using SQL queries, resulting in structured dataframes for statistical summaries and advanced analyses using R (v4.3).
Frequency distributions and visualizations (bar charts, pie charts, and Sankey diagrams) were generated to illustrate the relationships among the following:
• Architectural patterns;
• Application targets (e.g., EHR, interoperability, IoT, and AI-assisted health); and
• Ecosystem-level dimensions such as modularity and governance, among others.
This quantitative layer established the structural overview of the field and guided the thematic coding process for the narrative synthesis.
2.7.2 Narrative synthesis
To deepen understanding beyond descriptive counts, a narrative synthesis was undertaken to interpret methodological, conceptual, and contextual dimensions reported. This process adhered to the methodological framework proposed by reference (5) for systematic narrative reviews and consisted of the following phases:
1. Data familiarization and thematic coding: Each full-text study was reviewed in detail for identifying the following core analytical categories: (1) pattern type, (2) interoperability mechanisms, (3) data governance, and (4) ecosystem principles.
2. Within and cross-study exploration: Convergence and divergence patterns were examined to identify recurring rationales behind architectural choices and contextual variations across healthcare domains and geographies, if present.
3. Conceptual mapping and integration: A conceptual matrix was constructed linking architecture types to implementation contexts, enabling comparison of technical strategies (e.g., FHIR adoption, blockchain frameworks) and ecosystem-level attributes (e.g., openness, sustainability, and intersectoral coordination).
4. Triangulation and validation: Quantitative trends from descriptive analyses were triangulated with qualitative themes to enhance credibility and dependability.
2.7.3 Outcome integration
The integrated synthesis enabled the identification of dominant architectural paradigms, emerging hybrid models, and systemic gaps (e.g., AI integration, sustainability, and cross-border interoperability). These insights directly informed the interpretation and implications discussed in the Results section, ensuring that both quantitative and qualitative pieces of evidence were cohesively aligned to the research questions.
3 Results
3.1 Study selection
The search across the four databases retrieved 304 records. After removing 36 duplicates, 268 unique records were screened by title and abstract. Following a full-text assessment of 214 articles, 125 were excluded for the following reasons:
• Reviews of book chapters without new proposals (n = 20)
• Non-healthcare- or non-architecture-specific (n = 36)
• Too vague or too specific, limiting generalization (n = 69)
Articles or conference papers not accepted by the evaluators are listed in Table 2, categorizing each of them in its own category. Furthermore, Figure 1 also shows the distribution of reasons for non-acceptance.
Table 2. Discarded records and reason for non-acceptance.
Figure 1. Reason for non-acceptance.
The whole process is depicted in the flow diagram in Figure 2.
Figure 2. PRISMA flow diagram of the search, screening, and selection process.
Ultimately, 89 studies were included for complete data extraction.
3.2 Characteristics of included studies
The included studies were published between 2020 and 2025, with the highest number in 2020 (n = 26), followed by 2024 (n = 25) and 2021 (n = 21) (Figure 3).
Figure 3. Number of published articles found per year.
Geographically, most of the contributions originated in Asia and Europe, with notable representation from India, China, and the member states of the European Union (Figure 4).
Figure 4. Publications by country of the main author. Map created using the rnaturalearth: World Map Data from Natural Earth package by Philippe Massicotte, Andy South and Koen Hufkens, licensed under MIT License.
3.3 Main targets
Analysis of application targets revealed eight clusters (Table 3; Figure 5). Table 3 presents the number of articles included in each group and their corresponding percentage weights.
Table 3. Main target cluster grouping included resources.
Figure 5. Distribution of the main target for reported solutions.
This distribution reflects a field strongly oriented to interoperability and integration, while security and AI remain critical but unevenly addressed.
3.4 Predominant architecture patterns
The classification of architectural patterns based on the taxonomy proposed by O’Reilly (217) and the extended categories shows a clear dominance of the following:
• Service-oriented patterns, primarily microservices (n = 39; 43.8%); and
• Distributed or decentralized ledger-oriented architectures, mainly blockchain-based (n = 30; 33.7%).
Figure 6 illustrates the distribution across architectural patterns.
Figure 6. Architectural patterns of the reported solutions.
Table 4 lists the number of articles included.
Table 4. Predominant architectural patterns reported in included resources and their main contrasting attributes.
3.5 Digital health ecosystem concept
More than half of the included studies (n = 48; 53.9%) explicitly referenced ecosystem principles, such as openness, modularity, and interoperability, frequently framed within SECO and SoS frameworks (Table 5). These works emphasize decentralized collaboration among heterogeneous systems, adaptive governance, and the evolution of health information infrastructures through federated and standards-based integration models.
Table 5. Distribution of resources according to their conception of health ecosystem.
Representative examples include blockchain-enhanced ecosystems for secure data sharing (156, 170), multistakeholder frameworks supporting pandemic response and population surveillance (138, 142), and architectures aligning standards with SECO principles (183, 197, 205).
In contrast, 41 (46.1%) focused primarily on technical implementation or infrastructure optimization (service orchestration improvement, data pipeline implementation, and diagnostic algorithms, among others) without situating their contributions within an ecosystemic or multiactor context. Although many demonstrated architectural innovation (130, 131, 204), they often lacked consideration of cross-organizational governance, sustainability, or policy interoperability. This imbalance highlights a persistent gap between technical performance and ecosystem maturity, suggesting that ecosystem awareness remains an evolving frontier in HIS research.
The relationships among targets, patterns, and ecosystem inclusion are shown in Figure 7.
Figure 7. Sankey diagram illustrating the flow of outcomes and their distribution across relevance categorization.
4 Discussion
4.1 Principal findings
This systematic review identified 89 studies addressing software architectures in HIS. The analysis revealed that microservice-based architectures dominate current implementations, followed by distributed or decentralized ledger-oriented solutions, mainly blockchain. In addition, more than half of the studies referenced ecosystem-level concepts such as interoperability and modularity, although a significant portion remained focused on isolated technical solutions. Emerging approaches, including edge-cloud integration and machine learning-based architectures, have been appeared in niche use cases.
4.2 Interpretation and implications
The dominance of microservice-based architectures reflects a strong orientation toward flexibility, scalability, and modularity, attributes critical for dynamic healthcare environments. These architectures enable incremental innovation and align well with interoperability standards such as HL7 FHIR, which act as a stabilizing interface across heterogeneous systems (159, 197). Evidence suggests that microservices not only support modularity but also reduce vendor lock-in and foster resilience through uncoupled components, allowing for the seamless integration of emerging technologies (210).
Equally significant is the growing application of distributed or decentralized ledger-oriented architectures, primarily blockchain, as a response to escalating concerns around data security, confidentiality, and traceability (205, 219). These approaches provide immutable transaction logs and enable granular access control, enhancing trust among stakeholders. However, practical constraints, such as energy consumption, latency, and scalability limitations, hinder their large-scale adoption in healthcare settings (69, 70). Consequently, hybrid strategies that integrate blockchain with service-oriented patterns or edge computing frameworks are likely to emerge as viable alternatives.
Beyond patterns, our analysis of application targets reveals an ecosystem under pressure to respond to multifaceted demands:
• Artificial intelligence-assisted health solutions are being explored for advanced diagnostic support and decision-making; however, data heterogeneity and governance gaps remain critical barriers (130, 131). The literature emphasizes the urgent need for reference architectures tailored for AI integration into interoperable HIS platforms (134).
• Data integration architectures seek to consolidate clinical, genomic, and administrative sources, yet progress is constrained by semantic inconsistencies and a lack of standardized ontologies (64, 65).
• IoT- and IoMT-enabled monitoring solutions promise real-time analytics for preventive care but introduce interoperability, processing load, and cybersecurity challenges that require standardized protocols and lightweight security layers (77, 220).
• Persistent attention to security frameworks illustrates a sector grappling with cyber vulnerabilities. While blockchain offers partial solutions, the evidence suggests that multilayered strategies incorporating encryption, multifactor authentication, and governance policies are more effective (75, 82).
The implications of these findings are clear: architectural design choices must go beyond technical optimization to incorporate governance, ethics, and sustainability, positioning HIS as enablers of resilient and adaptive digital health ecosystems.
4.3 Contrast with existing literature
Consistent with experiences reported in JAMIA, where systematic reviews have historically guided international adoption of HIS standards, our findings confirm the importance of architectural synthesis for evidence-based adoption.
Previous systematic reviews have consistently underscored the value of service-oriented architectures for fostering interoperability and reducing operational complexity (81, 197). Our findings corroborate these observations but extend them by identifying a trend toward hybridization, where microservices are complemented by event-driven mechanisms to support real-time responsiveness and edge computing layers to minimize latency in mission-critical applications (62, 67). These hybrid models align with the emerging discourse on decentralized digital ecosystems, where adaptability and scalability are paramount.
The integration of AI-driven services within HIS, although recognized in earlier studies (132, 133), remains an emergent capability rather than a standard feature. Our synthesis emphasizes that successful adoption hinges on addressing data governance frameworks and establishing normative clarity for clinical-grade AI deployments.
Similarly, while blockchain-based approaches have been celebrated for their auditability and security guarantees, earlier reviews rarely confront the operational constraints our analysis highlights, such as computational overhead and regulatory inertia (210, 219). These limitations reinforce the argument that fully decentralized models may be impractical for healthcare unless combined with other architectural patterns, such as layered or service-oriented architectures.
Finally, our review supports and expands on the literature’s consensus that interoperability challenges are less technical than organizational. Frameworks like HL7 FHIR and OpenEHR continue to dominate as enablers of semantic and structural consistency (74, 81); however, adoption remains uneven across different geographies. This suggests that future architectural strategies should embed policy-aware interoperability mechanisms alongside technical layers to bridge global disparities.
4.4 Strengths and limitations
This review systematically synthesized evidence from major academic databases, applying PRISMA guidelines to ensure rigor. However, it is subject to certain limitations:
• Publication bias: The predominance of academic contributions may underrepresent non-academic innovations (204, 221–223).
• Language restriction: Only studies in English, Spanish, or Portuguese were included.
• Scope of analysis: The review focused on architectural descriptions, without assessing the effectiveness of implementations in clinical settings.
4.5 Future research directions
Given the stated findings, future investigation should focus on developing reference architectures explicitly incorporating ecosystem governance, data ethics, and cross-border interoperability. Furthermore, it is important to refine blockchain alternatives, integrating them into hybrid models to overcome current scalability challenges.
Research should also focus on developing data management policies and governance frameworks to enable the safe integration of artificial intelligence applications into health information systems in production environments.
5 Conclusions
This review synthesized evidence from 89 studies and confirmed that microservices (159, 197) and distributed ledger approaches (205, 210, 219) dominate the architectural landscape of health information systems. Both patterns align with the need for scalability, interoperability, and trust, particularly when combined with standards such as HL7 FHIR and OpenEHR (74, 81). These findings confirm a strong trend toward hybrid architectures, where microservices and event-driven mechanisms integrate with blockchain, layered, or edge-computing solutions to deliver flexible and resilient infrastructure.
At the same time, our analysis revealed significant gaps in cross-border care, sustainability, and the integration of artificial intelligence. For cross-border healthcare, initiatives such as epSOS and X-Road illustrate partial progress in secure data exchange but still struggle with semantic interoperability and governance heterogeneity across national contexts (224). Despite FHIR’s convergence potential, uneven implementation and limited semantic alignment hinder continuity of care beyond local environments. Furthermore, although modular and cloud-native architectures enhance performance and scalability, long-term sustainability is rarely evaluated, particularly in low- and middle-income contexts where infrastructure, funding, and maintenance capacities are uneven (81). Studies highlight how microservices can support modular encapsulation and even integrate IoT-based environmental sensing into health big data platforms (225); however, these efforts remain isolated. A significant issue is the lack of governance models for managing the life cycle of the microservices and ensuring energy-efficient operation (226).
Regarding AI integration, despite significant advances in diagnostic and decision-support applications (130, 131), implementations are still fragmented, largely decoupled from HIS workflows, and constrained by data heterogeneity and the absence of reference models (132–134). Initiatives such as the World Health Organization’s Artificial Intelligence for Health framework underscore the need for transparent governance and continuous evaluation to ensure trustworthy adoption of AI within SoS and SECO paradigms (227, 228).
The implications for practice are clear: healthcare organizations should pursue multipattern strategies in which microservices support modularity, blockchain enhances security and traceability, and edge-cloud integration ensures real-time responsiveness (62, 67). Yet, architecture alone is insufficient. Adoption depends on synchronized standards, semantics, usability, and data governance mechanisms that foster scalability and trust across institutions (229). Event-driven and lazy-coupling strategies emerge as enablers of resilience and responsiveness, particularly when aligned with interoperable service and edge architectures (230, 231).
The experience of JAMIA demonstrates how systematic reviews of HIS architectures can consolidate best practices and inform policy translation. By integrating fragmented evidence, this review reinforces the call for reference architectures that explicitly embed governance, ethics, and sustainability principles. The combination of FHIR contracts with one or multiple architectural patterns has proven to stabilize interfaces and simplify the incorporation of new applications (232–234).
Finally, the future of HIS will not depend on a single dominant paradigm but on the intentional orchestration of diverse architectural patterns within open, sustainable, and trustworthy ecosystems.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.
Author contributions
RC: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – review & editing, Writing – original draft. FV-G: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing, Writing – original draft. JB-B: Conceptualization, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing, Writing – original draft.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fdgth.2025.1694839/full#supplementary-material
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Keywords: digital health ecosystems, software architecture, interoperability, microservices, blockchain, HL7 FHIR, systematic review
Citation: Casanova R, Villa-Garzon FA and Branch-Bedoya JW (2025) Architectural patterns for health information systems: a systematic review. Front. Digit. Health 7:1694839. doi: 10.3389/fdgth.2025.1694839
Received: 28 August 2025; Accepted: 27 October 2025;
Published: 17 November 2025.
Edited by:
Xia Jing, Clemson University, United StatesReviewed by:
Manisha Mantri, Center for Development of Advanced Computing (C-DAC), IndiaGilma Hernández Herrera, Universidad del Rosario, Colombia
C. Marcela Velez, University of Antioquia, Colombia
Copyright: © 2025 Casanova, Villa-Garzon and Branch-Bedoya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Rene Casanova, cmNhc2Fub3ZheUB1bmFsLmVkdS5jbw==