Module 2.2 Artificial Intelligence, Data Analytics, and Emerging Technologies in Nursing

A sepsis prediction model deployed across hundreds of hospitals missed roughly two thirds of cases. A care management algorithm affecting millions of patients systematically deprioritised the sicker ones from one racial group. Neither failure was caused by bad technology. Both were caused by nobody senior asking the right question early enough.

This module equips senior nurse leaders to ask those questions. It covers what AI actually produces as distinct from what it is described as producing, how to interrogate the performance figures a vendor puts in front of you, why prediction without a deliverable intervention changes nothing, and where bias enters a model that looks entirely reasonable. It also sets out the Abu Dhabi and federal regulatory framework that any AI proposal must satisfy.

What you will cover

• Nursing informatics fundamentals, the DIKW hierarchy, and the four performance measures every leader should be able to challenge
• Clinical AI: where it genuinely works, the external validation case that changed vendor practice, and automation bias in nursing
• Predictive analytics for flow, risk stratification and workforce planning, and why thresholds are resource decisions
• Designing and reading nursing dashboards, distinguishing signal from noise, and using internal variation
• Telehealth, consumer wearables and connected medical devices: monitoring duty, consent and alarm governance
• Algorithmic bias and equity, the WHO ethical principles, and the DOH Abu Dhabi AI policy including graceful degradation