Objectives: The need for shared definitions and evidence based quality-indicators is widely perceived among Emergency Medical Services (EMS). In the region Friuli Venezia Giulia (FVG), Italy, both an EMS and a data collection system were established several years ago, but a comprehensive assessment of their quality had never been attempted and is the purpose of this study.
Design: Analysis of data regarding EMS emergency activities in the period January - September 2009. The quality indicators proposed by the national project entitled «Progetto Mattoni» were used.
Setting: 79 915 records were included, corresponding to 68 340 calls, 78 158 missions, 50 168 patients.
Results: The database has poor accuracy and accessibility. Only 20/64 indicators could be applied. Some of their definitions limit the applicability and/or reproducibility. The distribution of resources among the 4 operative centres of the region is uneven, as well as their performances in terms of call-to arrival interval. The standard recommended by the national guidelines for urban areas (8 minutes) is respected in only 56%of cases.The rescue intervals are shorter in operative centers with more resources per capita.
Conclusions: There is considerable scope for improvement in both the data collection system and EMS.The indicators themselves should be partly revised.
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Annu Rev Biomed Eng
January 2025
1Center for Engineering for Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA;
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3Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
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Department of Medical Oncology, Vall d'Hebron University Hospital, Barcelona, Spain.
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Division of Surgery & Interventional Science, Faculty of Medical Sciences, University College London, London, United Kingdom.
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Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
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