A systematic review of 142 studies identifies six core operational dimensions and three cross-cutting themes — governance, continuity, and resilience — that together form a transferable framework for keeping blood-supply services functional during pandemics, disasters and conflicts, and that lay out concrete data and modeling needs for AI-driven forecasting, optimization, and decision-support.

Main Finding

A systematic review of 142 publications (2010–2025) identifies six operational dimensions and three cross-cutting themes that synthesize transferable lessons for maintaining safe, reliable blood donation services during diverse crises. The review produces an evidence-based, structured framework of operational practices that support coordinated governance, operational continuity, and organizational resilience under disruptive conditions.

Key Points

• Six overarching operational dimensions:
  • Collaboration, coordination and standards
  • Blood supply chain design and management
  • Resource management (staff, equipment, supplies)
  • Disaster preparedness and risk management
  • Communication (public-facing and internal)
  • Operations (day-to-day processes, logistics, donor management)
• Three emergent cross-crisis themes:
  • Coordinated governance and communication (clear roles, cross-agency coordination, standard protocols)
  • Operational continuity (redundancy, flexible workflows, supply-chain alternatives)
  • Organizational resilience (capacity to adapt, surge staffing, contingency planning)
• Methodological approach: systematic literature review (PRISMA), qualitative content analysis with MAXQDA, organized on an adapted ISBT COVID-19 Operational Recommendations Framework.
• Outcome: a structured, cross-crisis synthesis of operational practices that are transferable across types of disruptions (pandemics, disasters, conflicts, mass-casualty events).

Data & Methods

• Scope: peer-reviewed and grey literature from 2010 through 2025, searched across four bibliographic databases (databases not named in the summary).
• Selection and reporting: followed PRISMA guidelines to identify and screen studies; 142 publications were included in the final synthesis.
• Analysis: qualitative content analysis using MAXQDA; coded practices and lessons and mapped them onto an analytical framework adapted from ISBT COVID-19 operational recommendations.
• Synthesis: extraction and grouping of actions, enablers, and outcomes into the six operational dimensions and identification of cross-cutting themes.
• Limitations (inferred from methods): heterogeneity of study designs and crisis contexts across included studies, potential publication and reporting bias, and variation in geographic and health-system contexts that may affect generalizability.

Implications for AI Economics

This structured synthesis has several implications for AI-driven economic analysis and system design focused on health-critical supply systems:

• Demand forecasting and shock modeling

  • Need for AI models that forecast donor turnout and demand under non-stationary crisis scenarios (pandemics, disasters, conflicts).
  • Value in developing probabilistic and scenario-based models (e.g., ensemble forecasts, Bayesian methods) to capture uncertainty and tail risks.

• Supply-chain optimization and resource allocation

  • Opportunity for optimization and reinforcement-learning approaches to design robust supply-chain policies that trade off cost, safety, and resilience (e.g., multi-echelon inventory with perishability).
  • Algorithms should incorporate redundancy, lead-time variability, and inter-facility sharing constraints identified in the review.

• Coordination and mechanism design

  • AI can support coordination platforms (matching, routing, prioritization) across agencies and regions; mechanism-design insights required to align incentives for cross-jurisdictional sharing.
  • Simulation and agent-based models can evaluate policy interventions and governance structures before deployment.

• Operational continuity and adaptive control

  • Real-time decision-support systems using streaming data (inventory, donor registrations, staff availability) can enable adaptive scheduling and dynamic resource reallocation.
  • Reinforcement learning and control-theoretic approaches can optimize operational policies under shifting constraints, but must be robust and interpretable for safety-critical use.

• Communication, trust, and behavioral modeling

  • AI-driven personalization and segmentation for donor outreach can improve retention during crises; causal and behavioral models are needed to avoid perverse incentives.
  • Explainability and trustworthy-AI practices are essential to maintain public trust in automated communication and triage systems.

• Cost-benefit and policy evaluation

  • Economic evaluation of resilience investments (redundancy, surge capacity, interoperable data systems) can use AI-enabled counterfactual and simulation tools to estimate expected value under different crisis distributions.
  • Prioritization frameworks should account for equity and access, not just efficiency.

• Data and governance requirements

  • Effective AI solutions require standardized, interoperable data (donor records, inventory, logistics, epidemiological inputs) — aligning with the review’s emphasis on standards and coordination.
  • Privacy, ethical, and regulatory constraints must be integrated into model design; transparent governance is crucial for deployment in health systems.

Practical research directions - Build and validate probabilistic donor-supply-demand models using multi-source crisis data; compare scenario-based vs. adaptive policies. - Develop inventory and routing optimization frameworks that incorporate perishability, sharing agreements, and surge constraints observed in the review. - Create interpretable decision-support tools for coordination bodies that combine simulation, optimization, and prescriptive recommendations. - Evaluate the economic value of resilience investments through stochastic simulation and cost-effectiveness analysis under crisis scenarios.

Overall, the review’s structured operational framework provides a domain-informed foundation for AI-economic modeling of blood-supply systems — guiding data needs, modeling assumptions, and evaluation criteria for interventions intended to enhance resilience and continuity in crisis conditions.