This systematic review suggests that AI-based decision support systems, when properly implemented, can help improve patient safety by improving error detection, patient stratification and medication management. AI techniques, such as machine learning (ML), can be used to provide clinical risk prediction to enhance patient safety. Data-based ML 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, the substantial safety impacts and the reduction of associated costs related to safety issues will require greater acceptance of these technologies across the ecosystem, including regulatory bodies and the market.
Medical causality requires understanding medical and clinical vocabulary, having medical knowledge and being able to derive relationships and correlations. Each of them requires human cognitive abilities. The purpose of AI is to help the provider make the best decision that will ultimately benefit the patient. Often, the end user is not clear how the data predicts the results (which variables are most influential) and how the information is combined, making clinical decisions difficult. This “black box” problem means that the vendor cannot evaluate the accuracy of the methods that lead the system to its exit.
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. This first article in a two-part series on the implications of integrating artificial intelligence into routine clinical care examines its impact on patient safety. This systematic review explored how AI based on machine learning and natural language processing algorithms is used to address and report on patient safety outcomes. Fortunately, with current advances in AI, digital clones of medical and pharmaceutical professionals, digital photovoltaic case processors with AI, can solve these problems and scale operations. AI represents a valuable tool that could be used to improve the security of care. It can help detect errors more quickly, stratify patients more accurately, and manage medications more effectively.
AI can also provide clinical risk prediction to improve patient safety by allowing multiple data sources to be considered simultaneously. In addition, AI can be used as part of quality improvement initiatives to reduce costs associated with safety issues. In conclusion, AI has great potential for improving patient safety by providing solutions for predicting damage, collecting data from multiple sources, and helping healthcare organizations make better decisions. However, greater acceptance of these technologies across the ecosystem is needed in order for them to have a substantial impact on patient safety.