Survey Protocol: Implementing Workload Indicators of Staffing Need in Iranian Primary Healthcare Services Publisher Pubmed



Riaziisfahani S¹ ; Ahmadnezhad E¹ ; Ehsanichimeh E² ; Abdi Z³ ; Haghdoost B¹ ; Akbarisari A⁴ ; Sirous S^{5, 6} ; Haddadi M^{5, 6} ; Samadpour M⁷ ; Bayat M⁷ ; Kashkalani T⁷ ; Khalilnezhad R⁷
Source: Primary Health Care Research and Development Published:2025

Abstract

Aim: The primary objective of this study is to assess the workload situation within Iran’s primary healthcare (PHC) sector, with an emphasis on identifying workforce needs and ascertaining any existing shortages or surpluses. Background: Over the past four decades, the establishment of PHC in Iran has been a significant accomplishment for the country’s healthcare system. Iran places substantial importance on achieving universal health coverage through PHC, aligning with global health goals, and acknowledging the critical role of human resources in this context. This commitment has enabled widespread and inclusive access to PHC services for both urban and rural populations across the nation. The primary objective of this study is to assess the workload situation within Iran’s PHC sector, with an emphasis on identifying workforce needs and ascertaining any existing shortages or surpluses. Methods: In 2023, a retrospective cross-sectional survey in Iran’s PHC sector sampled 1,212 individuals from 557 units across seven districts. Units were selected based on predetermined criteria for proportional representation of eligible occupational groups. Data was collected using tailored electronic questionnaires, covering facility and individual characteristics, working time, activities, and support tasks. Shortages or surpluses were assessed using Workload Indicators of Staffing Need (WISN) ratios under various scenarios, utilizing data from 2022 registration systems. Adjusted time data-informed workload pressure calculations. Findings: Customizing the WISN protocol to each country’s context is crucial, involving stakeholders in study design, including sample selection and data collection methods. Contextual facility information aids analysis, necessitating standardized data collection approaches for diverse registration systems. © The Author(s), 2025. Published by Cambridge University Press.