Scholarly Project Opportunities
Lange Youth Curriculum Restructuring
https://healthmatters.nyp.org/lang-youth/
Mentor: Dr. Mara Minguez, Pediatrics (mm2060@cumc.columbia.edu) with Maria Molina
(mam9452@nyp.org) – Lange Youth Medical Program
The Lang Youth Medical Program is a health-science enrichment, medical pipeline and college
preparation program at NYP in partnership with CUIMC. The Program serves middle school and high
school student from the Washington Heights and Inwood communities and offers them a hands-on
curriculum that interweaves academic, professional and personal development throughout a six-year,
academic and summer experience. Last year, we began a project to restructure our program’s existing
curriculum with the support of two graduate students from Teacher’s College. They did an excellent job
and were able to complete a large portion of the revised project-based learning modules. However,
there is still much work to be done to complete the remaining portions of our curriculum.
Next steps include the following:
- Complete curriculum outlines and lessons plans for: academic year (Grades 11 and 12) and for all summer programs (all grades)
- Review the entire curriculum to make sure all materials and formatting is streamlined and consistent across grades
- Develop and design a manual with the new curriculum that can be reproducible and shareable
- Create a template for an online portfolio for Lang Scholars to keep a record of their progress and
- learning throughout the six years
Our goal is to have the remaining portions of the curriculum completed by May 2019, do pilot testing in
July 2019 and then implement the revised curriculum for all grades in fall 2019.
Medical Education – Project in Surgical Training
Mentor: Michael D. Kluger, MD, MPH (mk2462@cumc.columbia.edu)
Assistant Professor of Surgery
Division of GI & Endocrine Surgery
Dr. Michael Kluger has worked with several medical students on scholarly project track in the past that
have resulted in first-author publications for the student. He has a medical education project that may
be well-suited for someone interested in choosing surgery specialties after residency. Please reach out
to him if you wish to discuss further.
Using Natural Language Processing to Assist in Feedback on Clinical Skills Education – Entrustable Professional Activities project at VP&S
Mentor: Dr. Beth Barron (bab2113@cumc.columbia.edu)
Associate Professor of Medicine at CUMC
Associate Director of CUMC/NYP Mary & Michael Jaharis Simulation Center
Columbia University Medical Center is one of ten schools piloting the Association of American Medical
Colleges core EPA’s for entering residency. EPAs provide the guideline for identifying gaps and transition
points in medical students learning while they transition into residency. Many schools use performance
evaluations and work-based assessments to score a student’s progress. Our concern is that the
narrative comments written by faculty are not utilized in such a framework – the need to measure and
quantify progress leads to dependence on evaluations that are able to be counted. Narrative comments
are not easily translatable to the countable and therefore can be neglected, however they can be a rich
source of data and would augment this process. CUMC is working to develop an automated way, using
natural language processing, to sort comments (into themes based on EPA’s and trustworthiness
components) as well as sort into positive vs negative. We hypothesize that faculty and students would
be better able to utilize this data if sorted in such a manner.
Some of the questions we are working to answer:
- Can the use of narrative comments from medical students end of class/rotation evaluations be used to better understand a student’s performance on entrustable professional activities (including trustworthiness)?
- How does sorting comments from medical student evaluations into themes based on EPA’s and professionalism affect/improve student’s and faculty’s understanding of student performance?
- Once the program is trained and reliable in its sorting it can be utilized for any number of projects. It would be very interesting to try to learn more about what types of comments are written about certain subgroups of students (examples include older vs younger, male vs female, difference in races, etc). It would also be interesting to see the reverse – do these types of biographical determinants determine the types of comments that a faculty member writes?
Students participating in this research program will participate in helping to develop and train this NLP
algorithm. It is necessary to confirm the computers sorting abilities to further train the program in order
for it to be able to function autonomously.
Scholarly Project Hand-Offs
[None available for this track at this time. Check frequently for updates.]