MEDICINE 401C
Internal Medicine Sub-Internship
Sonya Vipul Patel
Description from DukeHub
Course Goals: To provide an internal medicine inpatient care experience at the intern level. (2) How Goals Are Achieved: Students are assigned to an inpatient service at Duke University Hospital, Duke Regional Hospital or the Durham VA. These services include the general medicine services at all three hospitals, where internal medicine residents and attendings supervise the students; students may also rotate in the medical intensive care unit, in the cardiac intensive care unit, or on the cardiology service at Duke Hospital. The student functions as an intern on that service with the exception that orders must be countersigned by a resident or attending. Overnight duty consisting of night float responsibilities may be included over the course of the four-week schedule. The supervising resident or attending determines the number of patients assigned with anticipated increases over the four weeks. (3) Methods of Evaluation: Students are evaluated by their residents, fellows, and attendings. The evaluation form is made available to each student at the beginning of the rotation. Prerequisites: permission of instructor is required in order to add the course and permission is required in order to drop the course. In order to drop the course, students must provide at least 14 days advanced notice and permission of instructor is required. Failure to do so will result in a grade of Incomplete ('I') or a Withdrawal ('W') may be assigned. Please contact Marianne Drexler at (contact listed in DukeHub) for more information. Course is not available for visiting medical students. Credit: 5. Enrollment: max: varies by term. Sonya Patel-Nguyen, MD; John Roberts, MD and staff
Skills
AI-resilient: 2 of 5
- Yes: Physical, lab or field work · "sub-internship"
- Yes: Live work with people · "inpatient"
- No: Judgment someone is accountable for
- No: A craft you practice with your hands or body
- No: Small, in-person format
The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works
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