Advancing Predictive Healthcare Analytics: The Synergistic Role of Machine Learning and Data Utilisation in Hospital Systems

Abstract

This research aims to explore how Machine Learning (ML), Predictive Analytics (PA), and Data Utilisation (DU) help hospitals in the healthcare sector of Houston, considering the mediating role of DU. Since there are around 180 hospitals in the area, the aim of this research is to determine how these technologies influence decision-making and flow of activities in hospitals. An explorative, quantitative approach is used for this research, and 120 operations managers in hospitals are included as participants. Quantitative data is gathered from the participants using structured Likert-scale questionnaires and analysed through Structural Equation Modelling (SEM) to determine the relationship between ML, PA, and DU and Mediating role of DU. Based on the results, the proposed ML method considerably increases PA by increasing predictive precision and dependability. Through centralised data management, DU facilitates this relationship by ensuring that data integration and real-time processing are made possible and improving the quality of data that goes into the process. These outcomes demonstrate what the application of such tools as advanced analytics can bring to the healthcare realm in terms of operations. Regarding the research recommendations, the research suggests increasing capital expenditure allocation to data infrastructure and offering relevant continuing education to operations managers to increase the effectiveness of analytics. Further research should establish how these technologies might be implemented in other healthcare institutions.

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