Towards an Integrated Framework for Health Surveillance Systems: A Systematic Literature Review of Design Components and Implementation Challenges

ABSTRACT

Background and Aims

Health surveillance systems play a critical role in early detection, timely response, and evidence‐based policymaking. Despite significant technological progress, challenges such as data fragmentation, limited interoperability, and inconsistent governance continue to undermine system effectiveness. This study aims to advance the understanding of surveillance design by developing an integrated framework that consolidates technical, organizational, and ethical dimensions.

Methods

A systematic literature review (SLR) was conducted following PRISMA 2020 guidelines, covering studies indexed in Scopus and Web of Science up to April 2025. From 191 initially retrieved records, 36 studies met the inclusion criteria and were thematically analyzed.

Results

Through thematic analysis, ten key design components were identified. These findings offer actionable insights for policymakers, healthcare administrators, and system developers. Based on these findings, an integrated, evidence‐based framework is proposed to support effective system implementation. These key components are: leadership and management, stakeholder engagement, education and awareness, workforce competence, finance and insurance, data privacy and security, patient comfort, risk assessment, infrastructure readiness, and cultural/ethical considerations.

Conclusion

The synthesis revealed recurring implementation challenges, including fragmented data systems, inadequate stakeholder participation, and limited financial or technical capacity. Based on the findings, this study proposes an evidence‐based integrated framework to guide the design and implementation of adaptive, efficient, and ethically grounded surveillance systems. The proposed framework provides actionable insights for policymakers, healthcare administrators, and digital health architects seeking to strengthen surveillance infrastructures across diverse health settings and promote sustainable, data‐driven decision‐making.

1. Introduction

Rapid population growth and the increasing burden of chronic diseases have intensified the pressure on healthcare systems, exposing persistent workforce shortages and inefficiencies in health governance [1]. At the same time, the digital transformation of healthcare has led to significant advancements in accessibility, continuity of care, and clinical decision support [2]. Within this evolving landscape, health surveillance systems have become vital components for early detection, timely response, and informed policymaking.

Health surveillance is classically defined as the continuous and systematic collection, analysis, and interpretation of health data for use in planning, implementation, and evaluation of public health practice [3]. Effective surveillance systems integrate both preventive and reactive mechanisms, aiming not only to mitigate risks through trend recognition but also to restore population health in times of crisis [4]. These systems require robust infrastructures, reliable data sources, and agile analytical frameworks capable of operating in near real‐time [5].

Strategic disease prevention and health resource optimization remain central goals of healthcare systems [6]. Global health expenditures have escalated, accounting for 10.89% of GDP in 2021 [7], emphasizing the urgency of strengthening surveillance systems [8].

Health surveillance systems have evolved since the 1970s from rudimentary reporting mechanisms to sophisticated, multi‐layered platforms capable of real‐time decision support [9, 10]. As outlined by the World Health Organization, core components include event detection, data integration, case investigation, outbreak analysis, and strategic guidance [11]. AI‐powered tools now augment these elements by converting large, heterogeneous datasets into actionable intelligence, thereby enhancing predictive capacity, clinical accuracy, and policy relevance.

Recent technological advances—particularly in artificial intelligence (AI) and data science—have transformed the potential of health surveillance by enabling scalable, automated, and context‐aware analytics. These technologies enhance adaptability to emerging health threats such as pandemics, antimicrobial resistance, and health inequities [12, 13]. However, despite a growing body of literature focused on disease‐specific surveillance, there remains a critical lack of system‐level understanding regarding the design architecture, operational components, and implementation barriers of health surveillance systems in diverse clinical and organizational settings.

Multiple surveillance models have emerged to address specific public health needs, including passive [14], active [15], sentinel [16], syndromic [17], One Health [17], and event‐based approaches [18]. Each model serves distinct functions within national and international public health infrastructures, contributing to disease tracking, outbreak control, and resource optimization [19, 20]. Furthermore, beyond diagnostics, surveillance systems improve care delivery efficiency and reduce operational costs—especially when integrated with AI‐driven platforms for workflow automation and real‐time monitoring [21, 22].

In light of these developments, this study addresses a clear research gap: the absence of a comprehensive, evidence‐based framework that delineates the essential design components, technical and organizational challenges, and opportunities for intelligent integration in health surveillance systems. The objective of this study is to systematically extract, synthesize, and structure key insights from the literature in order to develop an integrated framework that supports the planning, implementation, and scaling of adaptable, efficient, and ethically grounded health surveillance systems. This review further aims to guide stakeholders‐including policymakers, system designers, and health IT leaders‐ in strengthening surveillance capacity within diverse and dynamic health environments.

Therefore, this review aims to synthesize existing knowledge to inform the development of an architectural framework for health surveillance systems. Such a framework provides a structured view that aligns governance, data, and technological layers, thereby facilitating coherent design and scalable implementation.

Based on the identified research gap and study objectives, the present review was guided by the following research questions:

RQ1: What are the pivotal components that influence the design and implementation of health surveillance systems?

RQ2: What are the barriers and challenges to effective implementation?

Addressing these questions provides a structured foundation for developing an integrated framework that supports the planning and scaling of adaptable and ethically grounded surveillance infrastructures.

2. Materials and Methods

This study adopted a Systematic Literature Review (SLR) approach in accordance with the PRISMA 2020 guidelines to ensure transparency and methodological rigor. The review focused on identifying key components and design challenges in the implementation of health surveillance systems [23].

A comprehensive search was conducted across two major citation databases—Scopus and Web of Science (WOS)—selected for their extensive coverage and citation quality. The final search was executed on 7 April 2025 using a combination of keywords such as framework, model, surveillance, design, implementation, and barrier, excluding terms related to unrelated domains (e.g., video surveillance). Boolean operators were applied to refine results.

To explore these dimensions, we conducted a systematic literature review (SLR), synthesizing existing research on surveillance system design and implementation. Unlike isolated studies, systematic reviews consolidate fragmented knowledge and offer structured, comprehensive overviews [24, 25]. Systematic reviews are generally considered the gold standard for synthesizing evidence because they apply predefined protocols, minimize bias, and provide structured and comprehensive assessments of existing knowledge. However, due to the absence of protocol registration (e.g., PROSPERO), the heterogeneity of study designs, and the limited comparability of empirical findings across the included studies, conducting a full systematic review was not feasible. Therefore, this study adopts a systematic literature review (SLR) approach, which enables a transparent, structured, and methodologically appropriate synthesis of the available evidence. Their importance has increased significantly, with over 1,237 journals publishing systematic reviews by 2023 [26]. These reviews follow a reproducible, multi‐stage methodology that ensures objectivity in addressing research questions [27].

This study used the Web of Science (WOS) and Scopus databases for their wide coverage and reliability [27]. PubMed and ProQuest were initially excluded due to their primary focus on clinical and biomedical studies, which did not align with this study's focus on surveillance architecture. A preliminary PubMed search yielded over 5,000 articles, mainly clinical case reports, which were not relevant to our focus. However, we have since reviewed our inclusion criteria to incorporate PubMed‐indexed articles pertinent to AI‐driven surveillance models where applicable.

Inclusion criteria were: (1) full‐text, peer‐reviewed articles published in English by February 2025, (2) studies explicitly focused on surveillance system design or implementation, and (3) availability in the selected databases. Studies unrelated to surveillance system design, as well as non‐academic publications, highly technical engineering reports, and duplicates, were excluded.

The initial title and abstract screening was performed manually by two independent reviewers using a structured Excel spreadsheet. All retrieved records from Scopus and Web of Science were imported into the spreadsheet, which was designed to record bibliographic information, relevance assessments, reviewer notes, and inclusion/exclusion decisions. Each reviewer independently evaluated the titles and abstracts according to the predefined criteria, and discrepancies were resolved through consensus discussions. This approach ensured transparency, reproducibility, and systematic organization of eligible studies prior to the data extraction stage.

Thematic analysis was performed following the six‐phase approach described by Braun and Clarke in 2006 [28]. This process involved familiarization with the extracted data, generation of initial open codes, grouping of similar codes into potential themes, reviewing and refining themes, defining and naming them, and producing the final thematic map. An inductive approach was applied, allowing key components and challenges to emerge directly from the data rather than from a predefined theoretical framework. This ensured that the synthesis accurately reflected the empirical evidence gathered from the reviewed studies.

The initial search yielded 191 articles, of which 36 met all eligibility criteria after two‐stage screening. These studies underwent quality assessment based on five pre‐defined QA criteria, and were rated as high, medium, or low quality. Only studies scoring above “low” were included in the final analysis. Data extraction was conducted using a standardized Excel spreadsheet, capturing bibliographic information, study objectives, system components, design challenges, and context‐specific considerations. A thematic analysis method was applied to identify recurring themes, which were then grouped into ten major components influencing the design and implementation of surveillance systems. This led to two primary research questions:

RQ1: What are the pivotal components that influence the design and implementation of health surveillance systems?

RQ2: What are the barriers and challenges to effective implementation?

Inclusion and exclusion criteria were systematically applied to ensure alignment with the study's objectives, as detailed in Table 1 [29].

Table 1.

Study inclusion/exclusion criteria.

Inclusion criteria

Exclusion criteria

Studies published up to 7 April 2025 Studies that explicitly focus on surveillance systems as a primary objective Peer‐reviewed, full‐text journal articles Published in the English language • Available in the selected electronic databases (Scopus, Web of Science)

Studies not related to the design, development, or implementation of health surveillance systems Studies focusing on video or camera‐based surveillance, Internet of Things (IoT) applications, or other non‐health surveillance domains Studies with a purely technical or engineering focus, medical textbooks, non‐scholarly discussions, or grey literature (e.g., reports, conference summaries) Articles not published in English Duplicate records identified during the screening process Studies with insufficient methodological quality based on QA assessment

To gather pertinent resources for this review, both automated and manual searches were employed [29]. The automated search, which was the initial step, relied mainly on keywords or search terms and was executed electronically using various databases [30]. As a result, two prominent citation‐based databases, Scopus and Web of Science, along with other resources, were identified. PubMed and similar databases were initially screened as part of a pilot search. However, the vast majority of retrieved articles—over 5,000—were clinical studies or unrelated to system‐level design. Despite multiple refinements, the search yielded low relevance, and thus PubMed was excluded from the final scope in favor of more domain‐specific sources. The final search included articles published up to April 7, 2025.

The search strategy was designed iteratively and applied to selected academic databases. Table 2 provides the final search terms, Boolean operators, and database‐specific queries. The search focused on identifying literature discussing the design and implementation of health surveillance frameworks, excluding unrelated results such as video surveillance.

Table 2.

Search procedure.

Search date	7 April 2025
Search terms	Scopus/other resources	(title (framework or model) and title (surveillance) and title (design or implementation or guideline or develop or principle or barrier and not title‐abs‐key (video))
	Web of science	(((ti= (framework or model)) and ti = (surveillance)) and ti= (design or implementation or guideline or develop or principle or barrier)) not ti = (video)
Language	English
Document type	Article, review article

As outlined in Table 2, relevant search terms such as ‘surveillance system design’ were used across selected databases, utilizing Boolean operators (AND/OR) to maximize retrieval. No date restrictions were applied. To ensure thoroughness, both backward and forward citation tracking were conducted—examining references from identified articles and identifying newer works citing those sources [30].

The selection of studies was conducted in accordance with PRISMA 2020 guidelines. All retrieved records were first screened by title and abstract, and then by full‐text review to determine eligibility. Two independent reviewers (Reviewer A and Reviewer B) conducted the screening process in parallel. Discrepancies were discussed and resolved by consensus, and where disagreement persisted, a third reviewer was consulted.

Data extraction was also performed in duplicate and independently by the same two reviewers using a predefined, piloted Excel‐based data extraction form. Extracted data included bibliographic details, study objectives, system components, design challenges, context, and outcomes. The extraction form was refined during a pilot phase using a random sample of three studies to ensure clarity and completeness. Any inconsistencies in data entry were resolved through consensus discussions.

The search yielded 191 potentially relevant articles. Based on inclusion/exclusion criteria (Table 1), a two‐stage screening process was applied. Titles and abstracts were reviewed first, with non‐English and duplicate articles (n = 39) excluded. Additionally, 99 articles unrelated to surveillance system design were removed, leaving 36 for full‐text assessment.

These remaining studies underwent quality evaluation using five predefined QA criteria to ensure methodological rigor and the validity of their findings [31]. Figure 1 illustrates the full selection and filtering process.

Figure 1.

PRISMA 2020 Flow Diagram for Systematic Reviews Which Included Searches of Databases, Registers, and Other Sources.

1. Does the research topic address surveillance system design, implementation, and development?

2. Is the research context clearly defined?

3. Does the research adequately describe the methodology?

4. Is the data collection procedure sufficiently explained?

5. Is the approach used for data analysis appropriately detailed in the research?

To determine the quality level, three rankings—‘high,’ ‘medium,’ and ‘low’—were applied to each QA criterion [31]. A study received a score of 1 if it fully satisfied a quality criterion. Similarly, a rating of 0.5 was assigned if a study partially met a criterion, and a score of 0 indicated non‐compliance with a criterion. In this study, the highest possible rating was 5 (i.e., 5 × 1) across the five QA criteria, while the lowest possible rating was 0 (i.e., 5 × 0). The overall quality of the included studies was evaluated using the Quality Assessment Criteria (QAC), and the distribution of scores is summarized in Table 3. Also based on this coding scheme, a study was categorized as follows:

Table 3.

Quality assessment criteria (QAC).

Low	Medium	High	Total
3	8	25	36

Note: High quality: > 4; Medium quality: < 4 and > 2; Low quality: < 2 (e.g., 0.5).

Overall, 25 studies were classified as high quality (69%), while 8 studies (22%) fell into the medium‐quality category. Three studies of low quality were excluded from the review, resulting in a final set of 36 studies for the systematic literature review (SLR).

A thematic analysis was employed to categorize the extracted elements. Full‐text reviews of the included studies allowed the research team to identify relevant text segments, which were inductively coded using open coding. Through iterative comparison and refinement, recurring codes were grouped into ten major components based on conceptual similarities.

This data‐driven approach, grounded in empirical evidence rather than predefined theory, ensured that the classification accurately reflected the key factors influencing surveillance system design and implementation. Each component was then clearly defined with representative sub‐elements assigned accordingly.

3. Results

Following the systematic review process, a total of 191 records were initially identified. After applying the inclusion and exclusion criteria, 36 articles were selected for final analysis. The quality assessment classified 25 studies as high‐quality and eight as medium‐quality; three low‐quality studies were excluded.

3.1. Distribution of Studies

The selected studies covered a wide geographical range. The majority originated from the United States (n = 11), followed by the United Kingdom (n = 3), Iran (n = 3), and several international collaborations. Region‐specific contributions are detailed in Table 4. In terms of publication trends, the majority of studies (n = 21) were published between 2021 and 2024, demonstrating a significant recent focus on surveillance systems (Table 5).

Table 4.

Studies published by regions.

Country name	Article count	Country name	Article count
Australia	2	Netherlands	1
Brazil	1	Poland	1
Canada	1	Spain	1
Caribbean	1	Taiwan	1
France	1	Thailand	1
International	4	UK	3
Iran	3	USA	11
Japan	1	International	2
Jordan	1

Table 5.

Chronological distribution of the studies.

Chronological distribution	Article count
1997–2005	4
2006–2015	3
2016–2020	8
2021–2024	21

Table 5 delineates the dispersion of all the studies under consideration. This data suggests a growing interest and increasing scholarly activity in this field over recent years.

The study identifies 10 main components and several subcomponents for designing and implementing a surveillance system, as presented in Table 6. This section directly addresses the first research question (RQ1).

Table 6.

Components and sub‐components in the design and implementation of surveillance systems.

Row number	Main component	Sub component
1	Leadership & management	Gaining insights into the workings of the surveillance system [32] Aligning with the goals and considering the needs of the end‐users [32, 33, 34, 35, 36] Using surveillance as a means to secure quality data for informed health policy and planning [37, 38] Clarifying roles and detailing responsibilities [19] The necessity for cooperation between authorities and its relevance to decision‐makers at all levels [39, 40, 41, 42] Integration of surveillance efforts into national health strategies[42, 43] The role of those in leadership and management in the process [33, 41, 42, 44] Comprehending the perspectives of patients, healthcare providers, and medical staff [45] Assessing patient risk levels for focused attention [19, 45] The critical role of advocates and supporters in the system [46] Facilitating better decision‐making within the health system's hierarchy [38] The structure of reporting at multiple levels [38, 47] Forming a commitment charter, outlining rights and responsibilities within the surveillance program [32] Enhancing the sustainability of the surveillance [32] The application of surveillance in strategic health planning and strategy development [37] Addressing the necessities of the end‐users [39] The necessity for dedicated resources, including time and funding, within program budgets [32, 41] Ethical leadership and transparent governance [41] Governance models for sustainable and scalable health monitoring [42]
2	Identifying and engaging key stakeholders	All individuals, parties, and organizations related to care and services should collaborate and make efforts to establish the framework for effective surveillance systems [32, 48] Knowing the audience and their information needs and profile of the actors involved and stakeholders' motivations and influences driving collaboration [19, 20, 32, 36, 49] Participatory design, design approach to engage patient, caregiver, and community stakeholders in solving a community‐defined problem [33, 49, 50] Provide stakeholders a high order visualization and a shared understanding of the organizational components that comprise the operational workflow and how they unite to support desired objectives [39] Building trust among marginalized or hesitant groups [41] Use of Advisory Boards and Workgroups [43]
3	Education, awareness and information[patients & staff]	Patient education and disease familiarization [51] Establishing common training and shared understanding [32] Ensuring patient awareness of screening and treatment processes [33] Promoting networking for patient and medical staff education [32] Immediate symptom management Recommendations for patients [33] Centralized access to reliable information for patients [33] Creating a knowledge‐sharing platform and resource hub [52, 53] Specialized conferences, workshops, and scientific committees [32] Continuously educate health workers about changes in clinical guidelines[49] Use monitoring tools (such as checklists) to assess staff acceptance of guidelines [49] Tailored educational strategies [43]
4	Enhancing qualifications for medical staff, nurses, and specialists	Continuing education [46, 54] Establishment of resident training clinics [54] Development and dissemination of clinical guidelines from professional societies [46] Integration of continuing education programs with hierarchical and regional health system services [55]
5	Finance & insurance	Budgeting and financing the cost of staff, retraining staff, equipment, consumables, other operational costs [36, 41, 56] Cost‐effectiveness analysis for cost‐effectiveness of the broader surveillance scheme, stakeholder satisfaction, and impact on long‐term system sustainability [36, 49] Attention to health insurance [57] Ensure there is adequate finance resources [41, 53] Have a program ‘charity care’ to cover costs for patients that don't have any insurance [56]
6	Privacy, security, and consent in data reporting, monitoring, and intervention	Determination of data ownership [32] Determination the reporting format from facilities [48] Effective usage of national statistics data [48] Data fidelity and timeliness [58] Data Completeness, Data Quality registry must fulfill the standard of completeness and timeliness of registration [38, 48] Data security, Privacy & trust issue [33, 41, 49, 59] Organizing, interpreting and sharing information outputs to reduce the analytical and decision‐making burden on end users [58] The appropriate communication and reporting of results [36, 41] Knowledge of the hazard both informs the choice of surveillance stream, and heavily impacts the downstream practical decisions around how the system will function [36] Ability to convey information in a readily interpretable manner [58] Access to data of historical systems Validation [57, 60] Data management support in all hospitals multiple systems for registration of routine care data [60] Evaluating output‐based methods [36] Use of registry data for research purposes [48] Complementarity between online and clinical data [59] Use of Standardized data collection instruments and Data distribution methods [20, 47] Considering Time Intervals for Reports (Monthly, quarterly, every six months, and annual reports) [20]
7	Patient comfort and satisfaction	Access to and quality of care [47] Quality of life for survivors [47] Concentration of equipment at the time of visit [56] Understanding patients [45] Reducing the patient's waiting time for a visit [35, 56] Patient Comfort [49]
8	Risk definition & assessment	Risk assessment/Risk management [32, 61] Definition of and decision on a model for identification of the main risks [34]
9	Infrastructure	Considerations of technology, space, and workflow [41, 42, 62] Continued availability of information technology and electrical infrastructure [41, 42, 58] Timely dissemination of results [46] Frequent surveillance to prevent disease progression [63] Comprehensive and systematic, evidence‐based approaches [38, 48, 53] Telemedicine and remote care for underserved communities [50] Effective registry follow‐up for registered cases [48] Vigilance in tracking unusual or problematic disease events [48] Remote patient monitoring [50] Clinical decision support using information technology [38] Complementary web‐based monitoring methods [64] Continuous monitoring of the implementation of instructions using digital tools [49] Digital literacy and capacity‐building for system use [41] Infrastructure readiness for scaling digital surveillance systems [42] Data Interoperability and Integration [42] Standardized surveillance indicators [44] Evaluation mechanisms for continuous improvement and scale‐up [42]
10	Cultural and ethical considerations	Cultural sensitivity in system design and communication [41] Ethical concerns regarding data use and patient autonomy [41] Perceived stigma or fear related to surveillance participation [41]

3.2. Key Components of Surveillance Systems

To effectively implement a surveillance system in healthcare settings, it is essential to articulate its operational framework, clearly defining its goals and anticipated impact. This clarity fosters stakeholder engagement, encouraging feedback that enhances system feasibility. Once a consensus is reached, rigorous testing, validation, and accreditation protocols should be established to maintain system integrity. Emphasizing rapid implementation strategies can further accelerate deployment. Key insights synthesized from the literature include:

• In the proposed framework, primary and tertiary prevention aspects—typically associated with direct clinical or therapeutic interventions—were not included, while secondary prevention components such as screening and early diagnosis were addressed [47].

• Establishing implementation teams and holding regular meetings allows stakeholders to exchange experiences and refine strategies [33].

• Integration with standard care protocols ensures operational continuity in clinical environments [54].

• Distributing educational materials, like handouts, supports stakeholder understanding and system engagement [33].

• Context‐specific adaptations—such as aligning educational interventions with clinical workflows—are critical for overcoming implementation barriers [33].

• Telemedicine integration requires streamlined operations, staff training, coordinated care, and financial sustainability to ensure effectiveness [46].

Data used in this study is available from the corresponding author upon reasonable request. To minimize bias, data was gathered from authoritative sources, and analyses were conducted collaboratively to ensure accuracy. All methods, procedures, and findings were thoroughly documented under the supervision of experts in systematic reviews. Cited references support the key claims made throughout the study.

These experts were affiliated with the Department of Information Technology Management, Tarbiat Modares University (Tehran, Iran), and provided methodological supervision throughout the review and data synthesis process.

The principal constraint of this study stemmed from the exclusion of the PubMed citation database. This decision was necessitated by the inability to segregate medical reports from pivotal elements in the architectural framework of surveillance systems. The search criteria yielded an excess of 5,000 articles, rendering screening impracticable. Consequently, the incorporation of this citation database was deprioritized in the research methodology.

Although these components were derived inductively, they are largely consistent with conceptual dimensions identified in prior studies on health surveillance frameworks and system implementation (e.g. [32, 33, 39, 41, 42, 48, 49, 58, 59]). This alignment reinforces the validity of the extracted components and supports the robustness of the synthesized framework.

Although some sub‐components under the Leadership & Management category, such as communication, coordination, and collaboration—may appear broad, they were consistently identified in the reviewed studies as essential managerial and leadership mechanisms. These functions enable ethical governance, promote accountability, and ensure the alignment of surveillance activities across institutions and sectors. Thus, within the proposed framework, leadership and management are viewed as cross‐cutting enablers that translate strategic vision into coordinated action.

In this framework, Infrastructure is conceptualized as an all‐encompassing foundation that supports both the physical and informational layers of the surveillance system. The reviewed studies often described infrastructure in a comprehensive sense—covering data flow, storage, interoperability, and technical readiness. Accordingly, the sub‐components presented here capture the integrated nature of infrastructure as the essential backbone enabling reliable data exchange, connectivity, and system scalability.

3.3. Summary of the Proposed Framework

The proposed framework integrates ten key components and their related sub‐components into a coherent structure that reflects the multi‐dimensional nature of health surveillance systems. At the strategic layer, governance, leadership, and stakeholder engagement ensure alignment with national health priorities and ethical oversight. The informational layer encompasses data standards, interoperability, and privacy, which enable secure and efficient data exchange. The technological layer supports system architecture, infrastructure readiness, and analytical capabilities, providing scalability and resilience. At the operational layer, process management, capacity building, and evaluation mechanisms facilitate the ongoing implementation and improvement of surveillance activities.

Collectively, these interconnected layers create a flexible and adaptive architecture capable of supporting evidence‐based decision‐making and cross‐sectoral coordination in public health surveillance. The framework thus bridges design principles with real‐world implementation requirements, serving as a roadmap for policymakers, system developers, and healthcare institutions.

The different dimensions in the design and implementation of the surveillance system are also shown in Figure 2.

Figure 2.

The different dimensions in the design and implementation of the surveillance system.

3.4. Challenges in Implementation

The outcomes related to the identification of challenges and barriers in implementing surveillance systems are presented in Table 7. Additionally, the study proposes solutions based on the research findings.

Table 7.

Challenges/barriers in implementation of surveillance systems.

Row number	Challenges/barrier	Suggested solution
1	Incorporating the methodology of patient enrollment within the system's procedural framework [33]	Integration with existing processes [33]
2	The absence of a cohesive framework for addressing challenges associated with the aggregation and interpretation of heterogeneous data modalities, including auditory, textual, visual, and standardized photographic content, within the healthcare domain [65]	Contemplating the extensive array of data typologies, and proffering suitable methodologies for their analysis and exposition [19]
3	Limited patient engagement and insufficient provision of educational resources impede effective participation in surveillance activities [33]	Disseminating a comprehensive pedagogical information compendium to patients upon the initiation of therapeutic interventions and establishing an instructional conduit [33]
4	Inadequate human resource capacity and insufficient workforce development hinder effective system implementation and sustainability [33, 55]	Establish and operationalize extant communicative conduits and methodologies for the dissemination of pedagogical resources [33]
5	The healthcare community is confronted with the arduous task of providing high‐caliber services amidst the limitations of resources and the presence of conflicting priorities, a situation that has been [54]	The allocation of funds and financial management pertaining to personnel expenses, continuous professional development of staff, procurement of equipment, consumable materials, and other operational expenditures are essential components of organizational stewardship [36]
6	Low stakeholder satisfaction caused by limited involvement, unclear communication, and lack of perceived benefits from the surveillance system[36]	Establish clear governance structures and strengthen cross‐sector coordination mechanisms to ensure integrated system management [39, 40]
7	Senior organizational echelons often face information overload due to the sheer volume of data and the unstructured nature of various data sources [19, 60]	Acquisition of suitable software and hardware, Employment of contemporary technological solutions, Dissemination of dependable and current data, and the imperative for a skilled workforce are critical components for organizational success [66]
8	The lack of capacity to analyze and distill actionable insights from data impedes the ability to make decisions in a timely manner [65]	Integration of data within online analytical processing systems, data warehouses, and other related systems is crucial for enhancing analytical capabilities [19]
9	Although the number of theoretical approaches has grown in recent years, the use of models and frameworks to evaluate the implementation of guidelines still seems to be a challenge [49]	It can be suggested that existing models be localized according to the local, social, and cultural conditions of each country or region [49]
10	The lack of familiarity of implementers and stakeholders with theoretical frameworks [49]	Holding training courses and practical workshops can help in better use of models [49]
11	Governance and Coordination (Without defined responsibilities and collaboration protocols, data sharing becomes fragmented) [42]	Need for clear governance structures and cross‐sector coordination, especially in multi‐agency settings [42]
12	Misalignment between system goals and the practical needs of end‐users reduces adoption and system usability [42]	Align system objectives with user requirements and engage end‐users during the design and implementation phases [32, 33, 34, 35, 36]
13	Limited Workforce Capacity (Need for trained personnel in bioinformatics, epidemiology, and data management. Skills gaps can bottleneck analysis and interpretation of complex data sets) [42]	Use training sessions, informational tools, and reminders to educate employees and increase adoption [43]
14	Lack of standardized performance indicators limits monitoring and evaluation of surveillance outcomes [44]	Develop a standardized set of indicators to track key outcomes across the continuum of care [44]

4. Discussion

This study synthesized ten interrelated components forming an integrated architectural framework for health surveillance systems. These results are consistent with prior studies emphasizing that successful surveillance efforts require not only robust technical infrastructure, but also supportive governance, stakeholder engagement, and ethical design [3, 33, 39, 49]. The discussion below summarizes the main themes identified through the thematic analysis and their implications for the design and implementation of such systems.

4.1. Governance and Leadership

Governance and leadership represent the strategic foundation of surveillance systems. Consistent with [39, 49], effective governance requires ethical oversight, transparency, and clear accountability mechanisms. Strong leadership enables coordination across institutions and ensures that strategic goals are aligned with operational implementation [32].

4.2. Stakeholder Engagement

Multi‐sectoral engagement emerged as a critical success factor [33, 48]. Lack of communication and limited involvement of end‐users reduce stakeholder satisfaction and system adoption. As emphasized in [36], participatory decision‐making enhances responsiveness and long‐term sustainability.

4.3. Data Standards and Interoperability

Interoperability remains a persistent challenge, especially across heterogeneous data sources [41]. Adopting standardized data structures and exchange protocols ensures consistency and comparability of surveillance data, enabling evidence‐based policy development [55].

4.4. Privacy and Security

Concerns regarding data privacy and security continue to constrain system integration [49]. Studies such as [41, 59] highlight the need for governance frameworks that balance confidentiality with accessibility to ensure public trust and data reliability.

4.5. Infrastructure and Technology

Infrastructure underpins all other components, encompassing data flow, storage, and connectivity. Limited infrastructure readiness and uneven technological capacity are major barriers reported in multiple studies [33, 42, 58]. Strengthening infrastructure ensures scalability and resilience.

4.6. Analytics and Decision Support

Analytical capabilities, including AI‐based and real‐time analytics, transform data into actionable insights [32, 41]. As supported by [55], integrated analytics enhance early detection and situational awareness in public health surveillance.

4.7. Capacity Building and Human Resources

Workforce limitations and lack of training hinder system efficiency [33, 55]. Investing in human capital and cross‐disciplinary capacity building ensures sustainability and adaptability in evolving surveillance environments.

4.8. Monitoring and Continuous Improvement

Establishing performance indicators and evaluation loops promotes adaptive learning and accountability [44]. Continuous assessment supports iterative enhancement of surveillance effectiveness [49].

4.9. Integrative Perspective

Collectively, these components reinforce the need for an architectural approach that aligns governance, data, technology, and operations. The resulting framework provides a coherent foundation for designing scalable, ethical, and data‐driven health surveillance systems.

The thematic synthesis indicates that integrating governance, technology, and data dimensions within an architectural framework enhances coherence and scalability of surveillance systems. The proposed framework extends beyond prior architecture‐focused models by incorporating human, ethical, financial, and technical dimensions, ensuring adaptability and broader applicability. Overall, this integrative approach bridges theory and practice, providing a structured foundation for future research and policy development in evidence‐based health surveillance.

5. Conclusions

This systematic literature review synthesized findings from 36 peer‐reviewed studies to identify and structure the essential components and design challenges associated with implementing health surveillance systems. The review culminated in a comprehensive framework comprising 10 core components and 101 subcomponents, spanning governance, stakeholder engagement, data governance, technical infrastructure, workforce readiness, and ethical‐cultural considerations.

The scientific value‐added of this study lies in its integration of technical, organizational, and ethical dimensions into a single, generalizable model—an advancement over prior fragmented or disease‐specific approaches. Unlike previous reviews that focused narrowly on surveillance technologies or epidemiological outcomes, this framework offers a multifaceted, implementation‐ready structure that bridges the gap between conceptual models and real‐world operational needs.

Moreover, the study presents a synthesis of 14 recurring challenges—such as data fragmentation, interoperability issues, and stakeholder disengagement—and matches them with literature‐derived, actionable strategies. This positions the framework as a practical tool not only for academic discourse but also for policy formulation, health system planning, and system design in various contexts, especially resource‐constrained environments.

The findings of this review converge toward an integrated architectural framework that organizes the ten identified components of health surveillance systems into interrelated architectural layers. At the strategic layer, governance, leadership, and stakeholder engagement provide ethical direction and policy alignment. The informational layer ensures data integrity through interoperability standards, privacy protection, and secure data management. The technological and infrastructural layer supports system functionality, analytics, and network readiness, forming the operational backbone of surveillance systems. Finally, the organizational and process layer addresses capacity development, performance evaluation, and adaptive improvement mechanisms.

Together, these layers form a cohesive architecture that bridges strategy and implementation, enabling a scalable, transparent, and evidence‐based health surveillance ecosystem. This architectural perspective not only synthesizes fragmented insights from the literature but also provides a practical roadmap for policymakers, system designers, and health institutions seeking to modernize surveillance infrastructures. In terms of applicability, this framework can support:

• The design of surveillance systems for both communicable and non‐communicable diseases;

• The adaptation of AI‐driven tools in national public health infrastructures;

• The development of training and governance protocols in cross‐sectoral health collaborations;

• And the evaluation of pilot or national programs seeking scalability and sustainability.

Future research should focus on validating the proposed framework through empirical studies in diverse health settings. In particular, evaluating the role of artificial intelligence integration, user‐centered design, and inter‐agency collaboration will be crucial to refining the framework and enhancing its adaptability. Additionally, expanding the framework's application to areas like mental health, chronic disease monitoring, or disaster preparedness could further demonstrate its cross‐sectoral relevance.

5.1. Study Limitations

This study has several limitations. First, although a comprehensive database search was conducted, the exclusion of PubMed may have led to the omission of relevant biomedical studies. This decision was based on the overwhelming number of irrelevant clinical articles that lacked system‐level design focus. Second, the review was limited to English‐language publications, potentially excluding valuable non‐English contributions. Finally, the thematic synthesis, while rigorous, is inherently interpretive and may be influenced by the reviewers' analytical lens.

Future studies should empirically validate the proposed framework in real‐world implementations, particularly in low‐ and middle‐income countries where infrastructure and governance dynamics differ significantly. There is also a need to explore the integration of emerging AI technologies in surveillance systems from both technical and ethical standpoints.

Author Contributions

Maryam Mollabagher contributed to data curation, investigation, resources, software, visualization and writing original draft. Alireza Hassanzadeh was involved in conceptualization, project administration, formal analysis, methodology, supervision and writing (review & editing). Mohammad Mehdi Sepehri participated in conceptualization, formal analysis, methodology, and validation. Abbas Habibelahi contributed to data curation, formal analysis, investigation and validation. Abolghasem Sarabadani was responsible for conceptualization, resources, visualization, and writing original draft.

Ethics Statement

This research is an extract from a doctoral dissertation in the field of Information Technology Management, Smart Business, Tarbiat Modares University, with the ethics code IR. MODARES. REC.1402.111.

Conflicts of Interest

The authors declare no conflicts of interest.

Mollabagher M., Hassanzadeh A., Sepehri M. M., Habibelahi A., and Sarabadani A., “Towards an Integrated Framework for Health Surveillance Systems: A Systematic Literature Review of Design Components and Implementation Challenges,” Health Science Reports 9 (2026): 1‐15, 10.1002/hsr2.71652.

Data Availability Statement

All needed data supporting the findings of this study are provided within the article. Also The data that support the findings of this study are available on request from the corresponding author For any additional information or clarifications, interested readers may contact the corresponding author via email.