Background: Given the necessity of attracting, retaining, and developing human capital within the health system, the process of human resources analytics in this context is of paramount importance, and it plays a significant role in evidence-based decision-making and policy formulation. Therefore, the aim of the present study was to design a model for human resources analytics in the health system.
Methods: This research is practical in nature and utilized a qualitative approach based on grounded theory. In 2023, semi-structured interviews were conducted with 14 experts and managers from Kerman University of Medical Sciences, using purposive sampling until theoretical saturation was achieved. Throughout the process, interviews were continuously compared, and notes were taken during and after the interviews. The collected data from each interview were then analyzed using open, axial, and selective coding with the assistance of MaxQDA20 software. Through categorizing the open codes, axial categories were identified and in the final step, relationships among core categories were established through selective coding. Additionally, the validity and reliability of the findings were assessed and confirmed based on the four criteria of credibility, dependability, confirmability and transferability.
Results: In identifying the codes and categories related to human resources analytics, a total of 370 open codes, 61 subcategories, and 24 main categories were generated. These were ultimately examined across 6 main axes, including the central phenomenon, causal conditions, contextual conditions, intervening conditions, strategies and consequences.
Conclusion: It is recommended that in order to enhance and develop the human resources analytics process within the health system, attention be paid to the extracted model and its dimensions and components, particularly the identified causal conditions, contextual conditions, and strategies.
