Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care, both inside and outside of the hospital, by providing solutions to predict harms, collect a variety of data including both new and already-available data, and as part of quality improvement initiatives.
Adverse events related to unsafe care represent one of the top ten causes of death and disability worldwide, and a third to a half appear preventable. Investments in reducing harm can lead to substantial savings, and more importantly improve patient outcomes.
Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors.
A recent study published by the journal npj Digital Medicine, evaluates the potential of AI to improve patient safety in these eight domains of harm, analysing the different applications for the prediction, prevention or early detection of adverse events. The publication considers the results of 392 studies, providing numerous examples of how AI has been applied within each domain, using various types of sensing technologies: vital sign monitoring, wearable devices, pressure sensors and computer vision.
AI techniques, such as machine learning (ML), can be leveraged to provide clinical risk prediction to improve patient safety. Data-driven ML algorithms have advantages over rule-based approaches for risk prediction, as they allow simultaneous consideration of multiple data sources to identify predictors and outcomes. Healthcare organizations are increasingly implementing ML and other forms of AI to improve patient care and outcomes. However, substantial impacts to safety and reduction of associated costs related to safety issues will require further acceptance of these technologies across the larger ecosystem including regulatory agencies and the marketplace.
In conclusion, AI can provide decision support by identifying patients at high risk of hospital harm to guide prevention and early intervention strategies. Similarly, AI can be applied in outpatient, community, and home settings. When coupled with digital approaches, these technologies can improve communication between patients and healthcare providers to reduce the frequency of preventable harms. While existing data will be helpful, new data will be available through technologies like sensors which should improve predictions.
For AI to be effective, implementation of data-driven analytics will require organizations to develop, support, and iterate clinician, team, and system workflows for continued patient safety improvements.
Source: npj digital medicine