Educational Material

[Article] Patient safety protocols in the ICU: how technology can prevent adverse events

Patient safety in Intensive Care Units (ICUs) is a clinical imperative. The use of innovative technologies in safety protocols opens a promising path to preventing adverse events, improving the quality of care, and achieving better results.
Below, we explore some of the main strategies proposed in this regard:

1. Clinical decision support systems (CDS)
Computerized alerts integrated into the hospital system can identify high-risk drug combinations and suggest appropriate monitoring. Evidence shows that these systems significantly reduce the administration of dangerous combinations, demonstrating their real potential in mitigating clinical risks in the ICU.

2. Quality indicators and continuous assessment
Defining and implementing specific quality indicators for ICUs makes it possible to monitor and improve clinical practices. Trials indicate that data collection and management based on validated indicators increase responsiveness and encourage more agile interventions. Since 2013, entities such as the European Society of Intensive Care Medicine (ESICM) have recommended the use of computerized reporting and adverse event management systems in ICUs, with a focus on improving care and patient outcomes. Protocols that combine rapid stakeholder assessment with continuous, near-real-time recording expand the ability to make immediate interventions to improve the quality of care.

3. Artificial intelligence (AI) and advanced language models
AI, ranging from predictive algorithms to large language models (LLMs), is transforming intensive care medicine. While more robust evidence is still needed, initial data suggests that AI tools can optimize protocol adoption, support diagnostic decision-making, and improve test interpretation, with greater accuracy and a positive impact on patient safety.

4. International perspectives and Sustainability
In the global scenario, especially in countries like Brazil, where the complexity of care and the heterogeneity of ICU teams are notable, the strategic use of technologies for managing indicators in near real time, associated with the stratification of severity and risk (such as long stays and readmissions to the ICU) with Machine Learning models, can strengthen critical health systems. This combination promotes sustainability, resource efficiency, and greater resilience for hospitals and ICUs.

 

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