Do the planners or presenters have any relevant financial relationships to disclose?

<text variant="body1">No. The planners and presenters for this educational activity do not have relevant financial relationships with ineligible companies that produce, market, sell, resell, or distribute healthcare products used by or on patients. <text variant="body1">If a relevant financial relationship exists, the disclosure will identify the individual’s name and role, the company involved, and confirm that the relationship has been reviewed and mitigated.

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Evaluating AI in Nursing Practice

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

Evaluating AI in Nursing Practice

Ce cours fait partie de Spécialisation "AI in Nursing Practice: Foundations for Quality and Safety"

Instructeurs : Kais Gadhoumi

Inclus avec En savoir plus

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

3 heures à compléter

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

3 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Explain how AI models learn from historical healthcare data.
Interpret common AI outputs with appropriate confidence and skepticism.
Identify situations where AI tools may perform poorly.
Recognize how bias can emerge from historical data and healthcare system patterns.

Compétences que vous acquerrez

Catégorie : Nursing Practices
Catégorie : Nursing Process
Catégorie : Artificial Intelligence and Machine Learning (AI/ML)
Catégorie : Nursing
Catégorie : Nursing and Patient Care
Catégorie : Healthcare Ethics
Catégorie : Artificial Intelligence

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

septembre 2026

Évaluations

8 devoirs

Enseigné en Anglais

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Élaborez votre expertise du sujet

Ce cours fait partie de la Spécialisation "AI in Nursing Practice: Foundations for Quality and Safety"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
Développez des compétences professionnelles avec des projets pratiques
Obtenez un certificat professionnel partageable

Il y a 4 modules dans ce cours

This course helps nurses read AI outputs critically, weighing what shaped them, where they're likely to fall short, and what they can and can't tell you about the patient in front of you. Through the four-bucket framework and an AI-powered discussion activity, learners will practice tracing an AI output back to the training data and process that produced it, interpreting outputs with the right mix of confidence and skepticism, and recognizing bias as a predictable pattern that shows up when training data doesn't fully reflect the patients a tool serves.

In support of improving patient care, Duke University Health System Clinical Education and Professional Development is accredited by the American Nurses Credentialing Center (ANCC), the Accreditation Council for Pharmacy Education (ACPE), and the Accreditation Council for Continuing Medical Education (ACCME), to provide continuing education for the health care team. The designation was based upon the quality of the educational activity and its compliance with the standards and policies of the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC). Duke University Health System Department of Clinical Education and Professional Development designates this activity for up to 9 credit hours for nurses. Nurses should claim only credit commensurate with the extent of their participation in this activity. Upon completion of all three courses (Seeing AI in Nursing Practice, Evaluating AI in Nursing Practice, and Applying AI in Nursing Practice) in the AI in Nursing Practice: Foundations for Quality and Safety Specialization, you will be eligible to apply for continuing education credits. Please note that continuing education credit rosters will be collected quarterly.

Before you can evaluate an AI output, it helps to understand how the model behind it was developed. This module introduces how AI systems learn from data, including the roles of training, labels, and different learning approaches. You’ll consider how these choices shape what a model can recognize, what it may miss, and how its outputs should be interpreted.

Inclus

2 vidéos8 lectures2 devoirs

2 vidéosTotal 11 minutes

Evaluating AI in Nursing Practice3 minutes
How AI Learns from Data8 minutes

8 lecturesTotal 31 minutes

Welcome to Evaluating AI2 minutes
Map of Four Things AI Does in Your Workflow5 minutes
Meet Your Instructors2 minutes
Lesson Overview2 minutes
A Closer Look into How AI Learns5 minutes
Term Spotlight: Supervised, Self-Supervised Learning, and Large Language Models5 minutes
Term Spotlight: Unsupervised Learning and Semi-Supervised Learning5 minutes
Inside MEWS5 minutes

2 devoirsTotal 10 minutes

Recognizing Training, Labels, and Learning5 minutes
Recognizing the Four Learning Types5 minutes

This module follows the path from clinical documentation to the output that appears on your screen. Along the way, you’ll develop vocabulary to help you interpret AI outputs thoughtfully by understanding what they may tell you, what they may leave out, and when to approach them with additional questions.

Inclus

2 vidéos8 lectures3 devoirs

2 vidéosTotal 14 minutes

From Chart to Risk Score7 minutes
What to Expect From AI Outputs7 minutes

8 lecturesTotal 34 minutes

Lesson Overview2 minutes
Term Spotlight: Structured Data, Unstructured Data, and Natural Language Processing5 minutes
Term Spotlight: Features and Labels5 minutes
Inside LACE5 minutes
Lesson Overview2 minutes
A Closer Look at AI Outputs5 minutes
Term Spotlight: Sensitivity and Specificity5 minutes
Inside the Morse Fall Scale and Hendrich II5 minutes

3 devoirsTotal 15 minutes

Recognizing the Building Blocks of a Risk Score5 minutes
Recognizing The Four Types of AI Outputs5 minutes
Recognizing Sensitivity and Specificity5 minutes

Understanding how AI systems work is one part of evaluating their outputs. This module explores the conditions that can make AI tools less reliable and introduces bias as a predictable pattern that may affect how models perform across different patient populations.

Inclus

2 vidéos5 lectures2 devoirs

2 vidéosTotal 15 minutes

Where AI Struggles and Why9 minutes
Bias in AI Systems7 minutes

5 lecturesTotal 22 minutes

Lesson Overview2 minutes
Where AI Struggles In Your Workflow5 minutes
Inside the Braden Scale5 minutes
A Closer Look into Bias in AI Systems5 minutes
Inside the Epic Sepsis Model5 minutes

2 devoirsTotal 10 minutes

Recognizing Where AI Struggles5 minutes
Recognizing Bias in AI Systems5 minutes

This module closes the course and hands the learning back to you. You'll apply the evaluative vocabulary from this course to a real tool from your own workflow before completing the final assessment.

Inclus

4 lectures1 devoir

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeurs

Kais Gadhoumi

Duke University

3 Cours27 apprenants

Offert par

Duke University

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Foire Aux Questions

Duke University Health System Department of Clinical Education and Professional Development designates this activity for up to 9 credit hours for nurses. Nurses should claim only credit commensurate with the extent of their participation in this activity.

Upon completion of all three courses (Seeing AI in Nursing Practice, Evaluating AI in Nursing Practice, and Applying AI in Nursing Practice) in the AI in Nursing Practice: Foundations for Quality and Safety Specialization, you will be eligible to apply for continuing education credits. Please note that continuing education credit rosters will be collected quarterly.

No. The planners and presenters for this educational activity do not have relevant financial relationships with ineligible companies that produce, market, sell, resell, or distribute healthcare products used by or on patients.

If a relevant financial relationship exists, the disclosure will identify the individual’s name and role, the company involved, and confirm that the relationship has been reviewed and mitigated.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.