Physician and Nurse Compensation Systems: A Systematic Review withPolicy Implications for the Iranian Healthcare Sector

Abstract

Context: Financial incentives represent a key policy lever shaping workforce motivation and behavior in health systems. Objectives: This systematic review examines physician and nurse compensation systems across countries to identify prevailing payment modelsand their policy implications, thereby providing evidence to inform provider payment system reforms in Iran. Data Sources: A systematic literature search was conducted across major English-language databases (Web of Knowledge, ProQuest, PubMed,Emerald, and Scopus) and Persian databases (SID, MagIran, Google Scholar) for the period 2000-January 2026. Search terms focused on physicianand nurse payment models. Study Selection: Study quality was appraised using the Critical Appraisal Skills Program (CASP) checklist for qualitative studies and theStrengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for quantitative research. Data Extraction: Extracted data were synthesized and organized in comparative tables by provider type (physician or nurse) and level of servicedelivery within the health system. Results: The search identified 2,205 studies, of which 29 articles and 4 policy reports from 17 countries met the inclusion criteria. Paymentsystems were classified into three levels: (1) Core stand-single payment models, (2) incentive-linked payment systems, (3) bundled paymentmodels, which will be elaborated on in the following sections. In addition, systems were classified as retrospective (rewarding past activity) orprospective (linked to predefined targets). Although fixed salary, capitation, and fee-for-service (FFS) remain the most common models, there is agrowing shift toward blended approaches that emphasize outcomes, accountability, and flexibility. Conclusions: No single provider payment model is universally optimal. In Iran, evidence supports gradual, context-sensitive reform throughhybrid payment arrangements aligned with service type, institutional capacity, and digital readiness, rather than premature large-scale adoptionof fully value-based or performance-based models.