ArticleHealth science reports2024
Predicting the number of IT staff needed in hospitals of Isfahan University of Medical Sciences based on modeling in 2023: A descriptive-analytical study.
Article in Health science reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Comparative Analysis of Multi-Criteria Decision-Making Methods and Workload Indicators of Staffing Need in Estimating the Number of Information Technology Experts Needed for District Health Networks: A Descriptive-Analytical Study.Health science reports · 2026Article
- Predicting the number of IT staff needed in hospitals of Isfahan University of Medical Sciences based on modeling in 2023: A descriptive-analytical study.Health science reports · 2024Article
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Authors and funding
6 authors.
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Abstract
Background and Aims: Considering the increasing use of information technology (IT) and the need of the implementation of related projects, the lack of IT specialists in the health system is one of the major challenges that require planning and foreseeing. This study was conducted with the aim of predicting the number of required IT personnel in hospitals of Isfahan University of Medical Sciences based on the modeling of identified and weighed influential factors in 2023. Method: First, Delphi method and multi-criteria decision-making (MCDM) using the Expository Posthaste Effective Resemblant Tool (ExPERT) were conducted to identify and weigh the components that affect IT staff's workload in hospitals. Then, the model for predicting the required number of IT personnel for the involved hospitals was developed. In all stages, the obtained information and results were checked and confirmed using experts' opinions in Focus Group Discussions. Results: Twenty-one hospitals (57%) out of 37 hospitals are facing a shortage of IT personnel. This varies from 0.5 to 1.6 personnel in different hospitals. Thirteen hospitals (35%) were reported to have adequate IT staffing and three hospitals (8%) had excess IT staffing. Conclusion: This study provided a predictive model for required IT staff in hospitals using MCDM through ExPERT which can be used in cases where the use of workload-based methods such as Workload Indicators of Staffing Need is complex or time-consuming.
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