Genomic Reference Laboratory Investment Feasibility Assessment

Build the evidence before building the laboratory

A genomic reference laboratory must operate as an integrated clinical service, not as a stand-alone equipment purchase. Its value proposition depends on how well referring providers can access the service, how samples move through the network, how laboratory and bioinformatics processes are controlled, and how results are returned within an appropriate clinical governance model. Saudi Arabia’s national genomics direction provides relevant context, but it does not validate a particular business case. The feasibility study is designed to separate strategic interest from demonstrable referral potential and operational readiness. The first workstream establishes the service proposition. It defines the intended testing portfolio, clinical use cases, sample types, reporting boundaries and referral channels. Provider interviews, historic referral records, procurement evidence and letters of interest are used to test which services could be relevant to prospective clients and how they are currently sourced. The study evaluates both direct diagnostic alternatives and send-out pathways, without presuming a market gap or a commercially viable volume. Technical diligence then turns the proposed portfolio into an end-to-end operating design. The team assesses laboratory workflow options, platform suitability, reagent continuity, sample accessioning, bioinformatics processing, variant review and reporting controls. Site and logistics analysis addresses the practical connection between collection points, transport conditions and the reference laboratory. The digital architecture is evaluated for laboratory information management, interoperability needs, access controls and traceability. Workforce design considers the roles required for technical operations, quality management, bioinformatics and specialist interpretation, with staffing assumptions tested against the selected service model. Quality, patient protection and data governance are decision gates rather than secondary compliance tasks. The engagement maps analytical validation, quality-control arrangements, proficiency testing, exception handling and clinical escalation pathways. It also defines the questions that must be resolved for genetic and health data: permitted purpose, consent and notices, access rights, retention, sharing, hosting and incident response. The applicable regulatory and licensing pathway is verified for each proposed test and delivery model; the study does not assume that a common pathway applies across all assays. The final investment case uses confirmed referral assumptions, supplier quotations, operating inputs and contract terms. It models alternative demand, technology and cost scenarios, identifies breakpoints and records the dependencies that management must close before launch. Deliverables include a feasibility report, service portfolio design, technical-commercial evaluation matrix, referral and operating model, data-governance requirements, risk register and phased implementation roadmap. These outputs enable a go, revise, partner or defer decision; they do not guarantee licensing, demand, funding or investment returns.

Scope of Work