The need to reorganize health research systems in pandemic crisis: A prospective study

The study addresses how pandemic crises, notably COVID-19, expose weaknesses in health research systems and the necessity of reorganizing them for effectiveness and resilience. Research activity—measured by publications, researcher numbers, and investments—is a key developmental indicator, and health research outcomes directly influence population health and safety. Many national research systems suffer from inconsistency, lack of coordinated policy, and resource constraints, underscoring the need for integrated systems. In Iran, the COVID-19 crisis spurred a reassessment of the health research system across medical universities, which share similar structures; thus, findings are generalizable nationally. The purpose of this study was to design an integrated pattern for health research systems during a pandemic using qualitative and quantitative methods.

Design: Descriptive and analytic study using expert input and systems analysis. Participants: Thirty purposively selected experts (heads and deputies of medical universities and faculties, and professors in health services management and health information management) from Iranian medical universities. Inclusion emphasized substantial executive experience, publication track record, research center membership, and doctoral-level specialization. Sampling continued to theoretical saturation (n=30). Data collection: An initial component list was derived from literature, then refined via semi-structured interviews. A process approach and qualitative content analysis were applied using Clarke and Braun’s six-step framework: familiarization, initial coding, theme search, subtheme formation, defining/naming main themes, and final list preparation. Components were identified at three levels: conceptual (level 1), structural (level 2), and basic (level 3). Structural components were named by function. For modeling, only conceptual and structural levels were retained; basic (level 3) components were reserved for operational planning. External validation was performed by two additional scientific experts not in the sample, and revisions were made accordingly. Data analysis: MICMAC (Matrix of Crossed Impacts Multiplications Applied to Classification) analysis determined component position, influence/dependence, and system stability. Experts scored pairwise influence in an n×n matrix on a 0–3 scale. Direct and indirect influence/dependence were computed. Multiple matrices (including the Matrix of Direct Influences) and corresponding graphs/maps were generated to classify components into input, linkage, independent, and output categories and to assess system stability.

• Seventeen structural components of the health research system were identified and categorized under conceptual groupings (enhancers, primary outputs, final outputs). Examples: Enhancers include leadership and management, human resources, information systems and databases, financing, and research process; primary outputs include knowledge management and science production, efficiency, quality, sustainability, innovation, satisfaction, decision making; final outputs include responsiveness to community needs, health promotion, culturalization, equity, effectiveness.
• MICMAC positioning: Inputs (second quadrant) were leadership and management, systems and databases, financing, human resources, and research process—largely influenced by external environment and capable of system-wide impact. Innovation, sustainability, and culturalization were independent components (third quadrant), with sustainability near the origin acting as a regulator. Knowledge management and science production was a linkage component (first quadrant) with high influence and dependence. Remaining components functioned as outputs of the system.
• System stability: Only one component was located in the first quadrant, indicating a stable system configuration.
• Direct vs indirect relationships: Strong relationships were observed among components in the direct mode; in indirect mode, a particularly strong relationship appeared between management and leadership and health promotion.
• Quantitative MICMAC indicators (selected): • Leadership and management showed the highest direct influence (1420) and high indirect influence (2612) with relatively low direct dependence (170). • Information systems and databases: direct influence 1306; direct dependence 397; indirect influence 2307; indirect dependence 206. • Human resources: direct influence 909; direct dependence 397; indirect influence 899; indirect dependence 317. • Financing: direct influence 909; direct dependence 170; indirect influence 1861; indirect dependence 101. • Knowledge management and science production (linkage): direct influence 909; direct dependence 795; indirect influence 771; indirect dependence 512. • Research process (regulator near origin): direct influence 795; direct dependence 454; indirect influence 1394; indirect dependence 281. • Outputs with high dependence: Health promotion (direct dependence 1022; indirect dependence 1578; direct influence 0), Effectiveness (direct dependence 909; indirect dependence 1367; direct influence 56), Equity (direct dependence 852; indirect dependence 1268; direct influence 56), Responsiveness to community needs (direct dependence 965; indirect dependence 1301; direct influence 113).

Pandemics exert broad short- and long-term pressures on health systems, including research functions. The MICMAC-informed model clarifies roles and interdependencies among research system components during crises. Inputs—management and leadership, financing, human resources, information systems and databases, and research process—are externally sensitive and, if under-resourced or defective, can trigger systemic crises. Components near the coordinate center (e.g., research process and sustainability) act as regulators that stabilize the system. Innovation, sustainability, and culturalization, while classified as independent, support long-term balance and resilience. Knowledge management and science production, as a linkage component with high mutual influence and dependence, serves as an intermediate goal that connects inputs to outputs and should be prioritized to amplify system performance. Classifying outputs into outputs, outcomes, and impacts supports policy-making and evaluation. Collectively, the findings address the study aim by proposing an integrated, process-based conceptual model that aligns resources and governance to maintain research effectiveness and responsiveness to community needs and health promotion during pandemics.

Combining a process model with qualitative content analysis and MICMAC quantitative mapping provides a pragmatic approach to designing integrated health research systems for pandemic conditions. Management and leadership, supported by adequate financing, human resources, and robust information systems/databases, are essential inputs to drive sustained research performance. Establishing strong knowledge management and science production capacities as linkage functions can translate inputs into valued outcomes (effectiveness, equity, responsiveness, health promotion). Building culturalization, promoting innovation, and ensuring sustainability help regulate and preserve system balance over time. Strengthening these components can enhance resilience and ensure research systems contribute effectively to pandemic response and long-term health gains. Future work should operationalize the basic (third-level) components, test the model prospectively across diverse settings, and evaluate impacts on policy uptake and health outcomes.