The influence of strategic foresight on quality of healthcare services in the presence of artificial intelligence solutions in Jordan
Background
Healthcare organizations are distinguished by intricate systems that undergo continual modifications and unpredictability. This greatly hinders the ability to estimate the exact consequences of any changes accurately. Therefore, scholars prove that strategic foresight enables leaders to anticipate future challenges and possibilities. The utilization of artificial intelligence (AI) in management is on the rise, mostly because of its ability to provide intelligent services, reduce medical errors, and improve operational efficiency.
Purpose
To examine the impact of strategic foresight on the quality of healthcare services provided by Jordanian nurses in the context of AI solutions in governmental hospitals.
Method
A cross-sectional descriptive correlational analysis was conducted. A convenience sampling approach was used in the four selected Jordanian governmental hospitals. The study’s target population consisted of nurses. Over three weeks between January and February 2024, 240 self-reported questionnaires were received using a five-point Likert scale, with a response rate of 88.9%. The completed surveys were suitable for analysis using AMOS SPSS v. 26 and SPSS.
Results
Simple linear regression and (Pearson’s r) test results showed that (R = .279, R square = 0.078) between strategic foresight and the quality of healthcare services. (R = .543, R square = 0.295) between strategic foresight and the adoption of AI-based solutions. And (R = .432, R square = 0.187) between adopting AI-based solutions and the quality of healthcare services. That reveals a statistically significant, positive correlation coefficient relationship between the variables. In the presence of the mediator, the direct relationship between strategic foresight and healthcare service quality was not statistically significant (b = 0.063, p = .398). The path analysis test indicates a linear relationship between the variables sequentially, and the AI-based solutions completely mediate the relationship between strategic foresight and the quality of healthcare services.
Conclusions
A positive and significant correlation between the variables suggests that a simulation-proposed model for a healthcare quality forecasting system, which the researcher built and included in the study recommendations, has to be designed. Therefore, AI-based forecasting systems should incorporate health service quality parameters to facilitate high efficiency and prompt patient demand fulfillment.
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