Interpretable AI is a powerful tool that can help healthcare providers quickly access medical data, review medical history, identify patterns, and recommend interventions. This technology can be used to detect unique symptoms and stratify the severity of risk for each patient, while focusing on the patient's well-being and the quality of care. Studies have shown that AI can lead to positive changes in patient safety outcomes and, in many cases, surpass traditional methods. For instance, AI has been able to reduce false alarms in several studies and also improve real-time security reporting systems (Table).AI has also been able to extract useful information from clinical reports.
For example, AI can be used to classify patients based on their ailments and severity, and identify common incidents such as the risk of falls, delays in delivery, hospital information technology errors, bleeding complications, and other risks to patient safety. AI can also help minimize the adverse effects of medications. In some cases, however, AI outcomes have been less than satisfactory. In these instances, the accuracy of the AI classification was lower than that of doctors or existing standards. Medical AI is becoming an increasingly valuable tool for treating patients. Brain-computer interfaces could help restore the ability to speak and move in patients who have lost these abilities.
This technology could also improve the quality of life of patients with ALS, strokes or spinal cord injuries. AI techniques such as machine learning (ML) can be used to provide clinical risk prediction to improve patient safety. Data-based machine learning algorithms have advantages over rule-based approaches to risk prediction since they allow multiple data sources to be considered simultaneously to identify predictors and outcomes. Healthcare organizations are increasingly implementing machine learning and other forms of AI to improve patient care and outcomes. However, for these technologies to have a substantial impact on safety and reduce associated costs, they must be accepted by regulatory bodies and the market.
Vendors must demonstrate that they are using artificial intelligence rather than just machine learning (ML).AI is a valuable tool that can be used to improve patient safety. It can lead to safety interventions at both the “clinical” and “diagnostic” levels. Advanced technology platforms that take advantage of forms of artificial intelligence (AI) are well positioned to support and increase the productivity of healthcare providers while improving patient outcomes by being able to identify those most at risk. The studies in this group focused mainly on extracting information from clinical reports such as safety reports (internal to the hospital), patient comments, EHR notes, and others which are typically derived from incident monitoring systems and patient safety organizations. The application of artificial intelligence (AI) has enormous potential as a tool for improving safety both inside and outside the hospital by providing solutions to predict damage, collecting a variety of data including new and already available data, and as part of quality improvement initiatives.