Effective metrics to detect and prioritize cyber incidents in healthcare

Numerous researches have focused on metrics for detecting and prioritizing incidents or risks; however, most studies address siloed responses within a single team or department. There remains a notable gap in the literature, especially in the healthcare sector, regarding comprehensive incident and risk metrics. This gap extends beyond theoretical principles to practical applications. This paper introduces a set of metrics for detecting and prioritizing cyber incidents, risks, and attacks, with a focus on their application in healthcare. Key principles include a holistic approach to incident and risk management, and the use of both quantitative and qualitative metrics for evaluation. A case study on implementing cyber incident metrics in the Emerald Healthcare System is presented. The case study covers metrics related to incidents, planning, and strategy from risk recognition through incident resolution, as well as approaches to ensuring the readiness of an incident response plan, adopting a holistic approach to risk management, and improving healthcare cybersecurity through integrated technology design. Assessments based on artificial intelligence, machine learning, and deep learning have the potential to serve as powerful metrics. A holistic approach to risk handling helps avoid risk silos, while holistic risk management offers the best protection for hospital cyber infrastructure against malicious attacks. Protecting patient privacy is essential for any healthcare system. A robust and comprehensive approach to patient data privacy, encompassing staff training and strict access control to patient records, should be an integral part of any risk management strategy.