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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence–based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives.

Purpose Medical technologies and assets are one of the main drivers of increasing healthcare cost. The rising number and complexity of medical equipment have forced hospitals to set up and regulate medical equipment management programs to ensure critical devices are safe and reliable. The purpose of this paper is to gain insights into maintenance management-related activities for medical equipment. The paper proposes applying a tailored reliability-centered maintenance (RCM) approach for maintenance activities selection for medical equipment. Such approach will support assets management teams in enhancing operation, decrease risk and cost, and ultimately improve health of patients served by these equipment. Design/methodology/approach The traditional RCM approach will be used with a focus on criticality reduction. By criticality, the authors refer to the severity of failures and occurrence. The proposed method relies on the use of reliability growth analysis for opportunity identification followed by a thorough failure mode and effect analysis to investigate major failure modes and propose ways to reduce criticality. The effectiveness of the proposed method will be demonstrated using a case of one of the leading obstetric and gynecological hospitals in United Arab Emirates and in the Gulf Cooperation Council region. Findings The case examines the relationship between the current practice of planned preventive maintenance and the failure rates of the equipment during its life span. Although a rigorous preventive maintenance program is implemented in the hospital under study, some critical equipment show an increasing failure rates. The analysis highlights the inability of traditional time-driven preventive maintenance alone in preventing failures. Thus, a systematic RCM approach focused on criticality is more beneficial and more time and cost effective than traditional time-driven preventive maintenance practices. Practical implications The study highlights the need for utilizing RCM approach with criticality as the most important prioritization criterion in healthcare. A proper RCM implementation will decrease criticality and minimize the risk of failure, accidents and possible loss of life. In addition to that, it will increase the availability of equipment, and reduce cost and time. Originality/value This paper proposes a maintenance methodology that can help healthcare management to improve availability and decrease the risk of critical medical equipment failures. Current practices in healthcare facilities have difficulty identifying the optimal maintenance strategy. Literature focused on medical maintenance approach selection is rather limited, and this paper will help in this discussion. In addition to that, the Association for the Advancement of Medical Instrumentation supports the initiative of adopting RCM on a large scale in healthcare. Therefore, this paper address the gap in the literature for medical equipment maintenance and the work is in line with the recommendation of leading healthcare association. The paper also presents statistical review of the total number of received maintenance work orders during one full year in the hospital under study. The analysis supports the need for more research to examine current practice and propose more effective maintenance approaches.