Given my background in both technology and finance, I guide students by aligning technology with healthcare economics as they consider the problem for which they’re solving. It’s critical to understand how AI and technology can impact the patient or end-user as well as healthcare costs, efficiency, and revenue. Break down how predictive modeling, workflow automation, and data analytics can create value by optimizing resource allocation, reducing readmissions, or predicting patient outcomes. This framing helps students see the economic rationale behind tech investments in healthcare. I also encourage students to build technical fluency and financial literacy by recommending they build foundational knowledge in AI methods like machine learning, NLP, and data visualization as well as economic concepts around cost-benefit analysis, ROI calculations, cost control, and scalability, which are vital when evaluating potential AI projects. Understanding how these two areas overlap helps students approach problems with both a technical and economic lens.
As healthcare increasingly shifts to value-based models, I try to explain how AI-driven technologies can support this transition. For instance, predictive analytics can help identify high-risk patients for preventive care, which can align with value-based incentives. Discussing these models gives students insight into how AI can advance both patient outcomes and financial goals. It’s also crucial for students to understand the legal and ethical implications of AI in healthcare, especially with regard to privacy laws and the protection of patient data. I integrate into discussion the importance of transparent, accountable AI practices, and how technology can inadvertently lead to issues like algorithmic bias or discrimination. A helpful tool to learn from is the exploration of pragmatic case studies reviewing successes and failures of real-world cases. I suggest focusing on their technical, financial, and ethical implications as well as how these initiatives were funded, scaled, and how financial models were used to justify their costs.
Lastly, I strongly encourage entrepreneurial thinking. Given the transformative nature of AI in healthcare, I guide students to think like entrepreneurs. How can they create value through AI solutions in a healthcare environment? Help them explore venture capital, start-up opportunities, and business models in the healthcare tech space. Aligned with this, I recommend cross-functional networking so that students connect with medical, tech, and finance professionals to hear firsthand about the complexities of launching AI solutions in healthcare. Industry exposure can help students bridge their theoretical learning with practical, economically sound strategies for real-world implementation.