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Publicação
Optimization of Emergency Services at Hospital Beatriz Ângelo
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Resumo(s)
In order to enhance emergency department (ED) operations at ULS de Loures-Odivelas, this
thesis investigates the use of data analytics and predictive modeling. Predictive models for
important outcomes, such as hourly admission volume, hospitalization chance, most likely
diagnosis, and triage color, were developed in conjunction with exploratory data analysis of
past admissions. 44 healthcare professionals also participated in a survey to get qualitative
information about their present problems and openness to AI-based solutions.
The findings demonstrate that predictive models, especially those that predicted
hospitalization and admission volume, showed high accuracy and practical relevance,
providing useful instruments for resource allocation and proactive planning. The intricacy of
clinical circumstances and the requirement for better, structured data were highlighted by the
mediocre performance of models that predicted diagnosis and triage color. The results of the
survey showed that staff views and model outputs were in agreement, particularly with regard
to staffing shortages, seasonal spikes, and receptivity to data-driven solutions.
Piloting predictive dashboards for operational planning, improving triage procedures,
encouraging cross-sector collaboration, and including frontline staff in AI deployment are
some of the main suggestions made to guarantee efficacy and confidence. This thesis shows
that it is possible to use data science to support an emergency care system that is more
patient-centered, responsive, and efficient, even in the face of external validation and data
quality limits.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Emergency Department Predictive Modelling Hospital Operations Resource Allocation Healthcare Analytics SDG 3 - Good health and well-being