Course Director: Marianne Sharko MD, MS
3 credits
This course will provide an overview of implementation science and introduce issues surrounding ethics in the use of artificial intelligence (AI) in healthcare. It will explore the challenges in the safe and effective implementation of predictive models, large language models and generative AI in healthcare. It will identify ethical issues surrounding the use of AI in healthcare through the lens of the medical ethical principles of autonomy, beneficence, nonmaleficence and justice and will provide a framework for evaluating the ethics of AI generated tools from the perspective of multiple stakeholders, including patients, providers, health systems and payors. Students will examine predictive models created to assist in healthcare management, understand the challenges in their effective and appropriate implementation, and appreciate the potential for unintended consequences and safety risks. We will explore the need to develop clinical decision support tools that are guided by the principles of fairness, appropriateness, validity, effectiveness, and safety (FAVES). We will discuss the importance of informaticists and providers as advocates for seeking transparency in predictive algorithms, and utilizing measures of reliability, validity, and effectiveness in their outcomes. We will address the importance of advocating for equity in accessibility and the need to address bias in the development of AI-generated clinical decision tools.
We will introduce implementation science, frameworks and theories, including Diffusion of Innovation, RE-AIM and PRISM. We will include projects to provide practical experience in the process of implementation that will highlight research methods, measures, and potential barriers and facilitators.
We will invite experts in the field to provide guest lectures and to lead student workshops within their areas of expertise.
The learning style is mostly student-driven, using “flipped classroom”, participatory exercises, teamwork, workshops and presentations. After the midterm, students will work to define a project, analyze related literature, and give a presentation in the final week.