Detecting Clinical Errors in Electronic Health Records Using Machine Learning

Electronic Health Records are a part of modern healthcare systems. Healthcare organizations use healthcare platforms to do things. They. Monitor healthcare data. They also analyze this data. Talk about healthcare.  Healthcare organizations use healthcare platforms to do things. They. Monitor healthcare data. They also analyze this data. Talk about healthcare. The problem is, healthcare datasets often have errors. These errors include information. There are also prescriptions. Sometimes there is duplicate or missing information. The goal was to improve Electronic Health Records.This survey paper looked at techniques like Machine Learning, Deep Learning finding anomalies and Natural Language Processing. These techniques are used to find mistakes in Electronic Health Records. The healthcare monitoring systems we looked at showed that using Machine Learning for healthcare analytics makes a difference. It helps find anomalies makes healthcare information more consistent. Improves the efficiency of healthcare monitoring. It also makes healthcare more transparent. Helps with decision making. Using Machine Learning in healthcare systems also makes patients safer helps healthcare organizations work better and improves the way healthcare is monitored. In the future healthcare systems will use technologies, like Explainable Artificial Intelligence, cloud-based healthcare analytics, federated learning, blockchain to secure healthcare information IoT to monitor healthcare and intelligent Deep Learning frameworks. These technologies will make healthcare monitoring systems smarter and improve patient safety. Electronic Health Records and Machine Learning will keep playing a role in this.

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