Task-technology fit model: Modelling and assessing the nurses’ satisfaction with health information system using AI prediction models

ABSTRACT

Health information system (HIS) plays a significant and supportive role in improving the quality and efficiency of healthcare organizations and their services. The purpose of this paper is to predict the effectiveness of the health information system in terms of nurses’ satisfaction using an artificial neural model. To do so, the study focuses on predicting the nurses’ satisfaction with health information systems in the Gaza strip based on the task technology-fit model (TTF). A total of 164 nurses participated in the questionnaire survey from three hospitals in the Gaza strip to build the prediction dataset of the five task technology-fit factors including task characteristics, technology characteristics, attitude and task-technology fit, and nurses’ satisfaction. After comparing the performance of the neural and regression models, it was found that the neural network is better than the regression model in predicting the nurses’ satisfaction as the results indicated that the performance metrics including accuracy, precision, and recall of the neural model are 92.68%, 95.24%, and 90.90%; respectively. The findings will help the decision-makers and hospital managers to enhance the health information systems and nurses’ satisfaction.

KEYWORDS:

• Artificial neural network (ANN)
• artificial intelligence (AI)
• health information system (HIS)
• prediction model
• Rapidminer

Disclosure statement

No potential conflict of interest was reported by the author.

Ethics approval

This study was approved by the top management of the hospitals in the Gaza strip.

Additional information

Notes on contributors

Kamal Mohammed Alhendawi

Kamal Alhendawi is currently assistant professor in information and knowledge management, published more than 20 indexed research in leading publishers' journals such as Cognition, Technology & Work, and International Journal of Healthcare Management. Working as supervisor and external examiner for master degree students in intelligent, technology, business, health management.