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Pajama Challenges in a Medical Practice:...

Matherne, G. Paul,...

Case

Pajama Challenges in a Medical Practice: Is AI the Answer?

Matherne, G. Paul; Chinnaswamy, Trent; Li, Tianshu; Krishnan, Vineet

OM-1809 | Published May 1, 2024 | 9 pages Case

Collection: Darden School of Business

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Denise Roebell, doctor of nursing practice, manages the Garfield Community Physicians Group (GCPG). Across GCPG, the administrative burden of pre-visit planning and documenting care is contributing to burnout and loss of providers. Historically, scribes helped with this workload, but they are expensive. Roebell is working with a consultant, Janice Thompkins, to explore the potential for AI to help with administrative work. Two products stand out: Navina AI, which focuses on pre-visit planning and code identification, and DAX Copilot, which utilizes ambient listening to document doctor and patient interaction and generate notes into the electronic health record. GCPG physicians are excited but have concerns about onboarding time, impact on residents and medical students, data security, and the role of AI products. Thompkins is preparing a report to Roebell outlining pros and cons of each. Roebell has several decisions to make. First, should she use AI in the practice at this time, or should the GCPG physicians bide their time? Second, if they are going to use AI, which product would help the most with burnout? Which would help the bottom line the most? What is the best way to roll out this product for a smooth and effective transition? What should the success metrics be for an AI product in health care? At the University of Virginia Darden School of Business, this fictional case is taught in the second-year MBA courses “Minds and Machines: Flourishing in the Age of AI” and “Solutions and Innovations in Health Care.” It would also be useful in courses on innovations in the health care system and on technology and AI in business.

Develop an understanding of how to leverage AI thoughtfully in the health care setting. Get students to critically think about how and when to implement AI in health care. Explore how to design processes and guardrails to effectively leverage AI for societal good.