Assessing digital factors influencing AI-led asset information management in hospital facilities

Abstract

Purpose This study examines the digital factors influencing AI-led Asset Information Management (AIM) in hospital facilities. The study focuses on the extent to which digital factors related to data governance, operational efficiency, resource optimisation, and predictive capabilities are perceived to be present in healthcare facilities. Design/methodology/approach A quantitative research approach was adopted using a structured questionnaire administered to relevant professionals involved in hospital asset management. Data were analysed using descriptive statistics in SPSS, including mean scores, standard deviation, skewness, and kurtosis to evaluate the distribution and significance of responses. Reliability testing was conducted, yielding a high Cronbach's Alpha value of 0.95, confirming strong internal consistency of the measurement instrument. Findings A two-factor structure of digital factors was identified through an exploratory factor analysis. Although the statistical evidence points to a dominant one-factor structure, the two-factor solution was retained on theoretical grounds. Respondents rated foundational digital services, particularly access control and security protocols, mostly highly. One-way ANOVA comparing item-level mean scores across six groups of professionally experienced individuals showed no statistically significant differences. Suggesting that perceptions of these digital factors do not vary meaningfully by years of professional experience in this sample. Practical implications The study highlights the need for healthcare institutions to invest in digital infrastructure, data integration, and capacity development to leverage AI capabilities fully. Emphasis should be placed on transitioning from basic automation toward predictive and intelligent asset management systems. Originality/value This study provides empirical evidence on the layered adoption of AI in hospital asset management, demonstrating that current implementations are largely efficiency- and security-driven, while advanced predictive capabilities remain underdeveloped. It contributes to the growing body of knowledge by identifying a critical gap between AI potential and its practical realisation in healthcare infrastructure.

The authors' abstract, as published at the source. Frontiers in Digital Health, 2026 · DOI ↗