ABSTRACT
BACKGROUND: With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients. OBJECTIVE: The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage. METHODS: An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tesanj General Hospital, Bosnia and Herzegovina. RESULTS: The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections. CONCLUSION: The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.
ABSTRACT
BACKGROUND: Introduction of fluids, medicaments and nutrients into the human body during hospitalization is fundamental for treatment and healing of patients. Fluids are introduced by means of infusion pumps while nutrients and medicaments are introduced by perfusion pumps. It is of vital importance for these devices to deliver exact amounts of the aforementioned substances as significant deviations can result in severe patient harm. Therefore it is important to effectively monitor their performance and prevent failures. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of infusion and perfusion pumps for post-market surveillance purposes. METHOD: The method was developed on the basis of metrology characteristics of the devices. In addition to the evaluation of essential safety and visual integrity of infusion and perfusion pumps, their performance in terms of delivered volumes was assessed and monitored. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of infusion and perfusion pumps as a method used during PMS contributes to significant improvement in devices' accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of infusion and perfusion pumps during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.
