Designing a national framework for preventing fraud in Iran's health system: a sequential mixed-methods study using qualitative analysis and Delphi consensus.

Abstract

To design and validate a comprehensive, expert-informed national framework to prevent and control fraud and misconduct in Iran's health system. Fraud in this context refers to practices such as false claims, overbilling, unnecessary prescriptions, informal payments and beneficiary manipulation, which undermine efficiency and trust. A sequential mixed-methods study, including an exploratory qualitative phase followed by a two-round Delphi consensus process. National-level study across multiple sectors of Iran's health system, including insurance, governance and health policy institutions. In the qualitative phase, 12 experts (senior managers, auditors and policymakers) were interviewed, achieving data saturation. In the Delphi phase, 31 experts participated in Round 1 and 27 completed Round 2. All had ≥5 years of relevant experience in health management, fraud detection or policy-making. Framework analysis of interviews identified six strategic categories: cultural, educational, legal, technological, insurance-related and structural interventions. These were validated and prioritised through the Delphi process. High consensus (≥75%) was achieved for most items, with unanimous agreement on legal clarity, enforceable sanctions and electronic identity verification. The final framework comprised three overarching domains-capacity building, strengthening oversight systems and modernisation of operational infrastructure-operationalised through six strategic pillars. This study proposes a validated and context-specific anti-fraud framework tailored to Iran's health system. By combining qualitative exploration with structured consensus, the framework offers practical strategies for enhancing transparency, accountability and resilience. It may also serve as a model for other low- and middle-income countries facing similar governance challenges. Not applicable.