Developing a cost-predictive model for hospital construction projects

Abstract

Hospital construction projects require the integration of innovative technologies, making them complex and challenging to estimate accurately. Without reliable guidelines, lists of considerations, or predictive models, decision-makers may choose an unsuitable contractor, leading to cost overruns and project delays. This research used quantitative and qualitative methods, including expert opinions and focus groups, to address this issue and identify critical factors affecting cost estimation in hospital construction. Using multi-regression analysis, historical data was analyzed to establish a cost-predictive model. The study identified 24 factors that determine the most critical criteria affecting the cost estimation of hospital construction. This model can evaluate the estimated direct cost provided to decision-makers in the early stages of the project with over 95% accuracy. The research results can assist professionals in the construction industry in better estimating and managing the costs of hospital construction projects, reducing risks, and improving project outcomes. Key findings indicate that specific components such as site and electrical works show average percentage errors of 8.02% and 5.11%, respectively, highlighting areas prone to cost estimation inaccuracies. This insight directs focus on improving accuracy in these critical sectors, thus further refining cost management strategies for hospital construction projects.

Keywords

• cost
• hospital
• experts
• variables
• regression model