Diagnostic network design and optimization

Designing optimal diagnostic systems is complex and highly context-dependent, relying on multiple data inputs and assumptions.

Important variations in epidemiology, geography and health systems – often within the same country – must also be accounted for, as well as budgetary considerations. Diagnostic network optimization takes a geospatial network analytics approach, used in many manufacturing industries, to improve patient access to services in the most cost-efficient way.

Using data to inform instrument placement, sample transportation, referral mechanisms, staffing, and geographical prioritization, diagnostic access can be increased for those most in need, while also ensuring cost efficiency and feasibility. Data can also inform integration of diagnostic tools and services (such as testing for COVID-19 using molecular diagnostics already in place for TB), to provide patient-centred services and resource allocation across heath sectors and programmes.

Together with other approaches to support countries to identify gaps and develop tailored interventions, such as patient pathway analysis and structured assessments, diagnostic network optimization can help target the right mix of diagnostic solutions to where they are needed most, at costs sustainable for health systems, and is able to rapidly adapt to emerging threats.