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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

Q

Quality management system (QMS)

A quality management system (QMS) is the formalised set of policies, processes, procedures, records and responsibilities through which an organisation plans, controls and continually improves the quality of its products, services and compliance activities. In life sciences the QMS is not optional: ICH GCP section 5.0 requires sponsors to implement a system to manage quality throughout all stages of a clinical trial; the MDR and IVDR require manufacturers to operate a QMS, in practice certified to ISO 13485; good manufacturing and pharmacovigilance practices (GMP, GVP) require pharmaceutical quality systems; and Art. 17 of the EU AI Act requires providers of high-risk AI systems to put in place a QMS covering regulatory compliance, design and development, data management, risk management, post-market monitoring and incident reporting.

A QMS typically rests on a quality manual and policy, a hierarchy of standard operating procedures and work instructions, document and record control, training and competence management, supplier qualification and oversight, change control, deviation and CAPA (corrective and preventive action) management, internal audits and management review. ISO 9001 provides the generic model; ISO 13485 adapts it to medical devices; ISO/IEC 27001 applies the same architecture to information security and ISO/IEC 42001 to AI, so that an integrated management system can hold all of them.

Data protection obligations are best implemented through the QMS rather than alongside it. The accountability principle of Art. 5(2) GDPR requires demonstrable compliance, which is exactly what controlled procedures, training records, vendor qualification, change control and audits provide. Typical QMS documents with a data protection dimension are the DPIA procedure, the breach management SOP, the data subject request SOP, the records retention schedule, the computerised system validation procedure and the supplier assessment procedure with its DPA template. iliomad, itself ISO 9001 and ISO 27001 certified, builds these into clients' existing systems through its quality assurance services.