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An analytic decision support tool for resident allocation.

Publication ,  Journal Article
Talay-Değirmenci, I; Holmes, CJ; Kuo, PC; Jennings, OB
Published in: J Surg Educ
2013

BACKGROUND: Moving residents through an academic residency program is complicated by a number of factors. Across all residency programs the percentage of residents that leave for any reason is between 3.4% and 3.8%.(1) There are a number of residents that participate in research. To avoid discrepancies in the number of residents at the all levels, programs must either limit the number of residents that go into the lab, the number that return to clinical duties, or the number of interns to hire. Traditionally this process consists of random selection and trial and error with names on magnetic strips moved around a board. With the matrix that we have developed this process is optimized and aided by a Microsoft Excel macro (Microsoft Corp, Redmond, Washington). METHODS: We suggest that a residency program would have the same number of residents at each residency stage of clinical practice, as well as a steady number of residents at each research stage. The program consists of 2 phases, in the first phase, an Excel sheet called the "Brain Sheet," there are simple formulas that we have prepared to determine the number of interns to recruit, residents in the research phase, and residents that advance to the next stage of training. The second phase of the program, the macro, then takes the list of current resident names along with the residency level they are in, and according to the formulas allocates them to the relevant stages for future years, creating a resident matrix. RESULTS: Our macro for resident allocation would maximize the time of residency program administrators by simplifying the movement of residents through the program. It would also provide a tool for planning the number of new interns to recruit and program expansion. CONCLUSIONS: The application of our macro illustrates that analytical techniques can be used to minimize the time spent and avoid the trial and error while planning resident movement in a program.

Duke Scholars

Published In

J Surg Educ

DOI

EISSN

1878-7452

Publication Date

2013

Volume

70

Issue

1

Start / End Page

31 / 35

Location

United States

Related Subject Headings

  • Surgery
  • Personnel Staffing and Scheduling
  • Internship and Residency
  • Humans
  • General Surgery
  • Efficiency
  • Decision Support Techniques
  • 3901 Curriculum and pedagogy
  • 3202 Clinical sciences
  • 1302 Curriculum and Pedagogy
 

Citation

APA
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ICMJE
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Talay-Değirmenci, I., Holmes, C. J., Kuo, P. C., & Jennings, O. B. (2013). An analytic decision support tool for resident allocation. J Surg Educ, 70(1), 31–35. https://doi.org/10.1016/j.jsurg.2012.07.003
Talay-Değirmenci, Işılay, Casey J. Holmes, Paul C. Kuo, and Otis B. Jennings. “An analytic decision support tool for resident allocation.J Surg Educ 70, no. 1 (2013): 31–35. https://doi.org/10.1016/j.jsurg.2012.07.003.
Talay-Değirmenci I, Holmes CJ, Kuo PC, Jennings OB. An analytic decision support tool for resident allocation. J Surg Educ. 2013;70(1):31–5.
Talay-Değirmenci, Işılay, et al. “An analytic decision support tool for resident allocation.J Surg Educ, vol. 70, no. 1, 2013, pp. 31–35. Pubmed, doi:10.1016/j.jsurg.2012.07.003.
Talay-Değirmenci I, Holmes CJ, Kuo PC, Jennings OB. An analytic decision support tool for resident allocation. J Surg Educ. 2013;70(1):31–35.
Journal cover image

Published In

J Surg Educ

DOI

EISSN

1878-7452

Publication Date

2013

Volume

70

Issue

1

Start / End Page

31 / 35

Location

United States

Related Subject Headings

  • Surgery
  • Personnel Staffing and Scheduling
  • Internship and Residency
  • Humans
  • General Surgery
  • Efficiency
  • Decision Support Techniques
  • 3901 Curriculum and pedagogy
  • 3202 Clinical sciences
  • 1302 Curriculum and Pedagogy