Optimising medical equipment utilisation and serviceability: A data-driven approach through insights from five...

Underutilisation, inadequate maintenance, and poor communication often lead to inefficiencies in effectively utilising medical equipment. This study uses a data-driven approach to explore the factors influencing equipment utilisation and serviceability in healthcare settings, offering insights to enhance operational efficiency.

The study was conducted across five specialised healthcare institutions. A comprehensive list of medical equipments was created, and data was collected using a yes/no checklist to assess various factors affecting equipment utilisation and serviceability. The Utilisation Coefficient (UC) was calculated for each piece of equipment. Descriptive statistics, correlation analysis, and decision tree model were used to evaluate the relationships between equipment serviceability and factors such as maintenance practices, procurement policies, and communication.

The study found that the UC was the most significant factor affecting equipment serviceability, contributing over 76% to decision-making in the model. Maintenance practices, such as regular calibration and preventive maintenance, positively correlated with serviceability. In contrast, factors like equipment age and miscommunication between staff and engineers negatively impacted serviceability. With its high accuracy of 98.79%, the decision tree model demonstrated its reliability in predicting serviceability, instilling confidence in the study's methodology.

This study highlights critical factors influencing medical equipment utilisation and serviceability, providing actionable insights for healthcare management. Hospitals can improve operational efficiency, reduce costs, and enhance patient care by focusing on regular maintenance, better communication, and strategic procurement.