Objective: The academic medical centre (AMC), with over 2200 faculty members, annually manages approximately 300 appointments and promotions. Considering these large numbers, we explored whether machine learning could predict the probability of obtaining promotional approvals.
Methods: We examined variables related to academic promotion using predictive analytical methods. The data included candidates' publications, the H-index, educational contributions and leadership or service within and outside the AMC.
Results: Of the five methods employed, the random forest algorithm was identified as the 'best' model through our leave-one-out cross-validation model evaluation process.
Conclusions: To the best of our knowledge, this is the first study on the AMC. The developed model can be deployed as a 'calculator' to evaluate faculty performance and assist applicants in understanding their chances of promotion based on historical data. Furthermore, it can act as a guide for tenure and promotion committees in candidate review processes. This increases the transparency of the promotion process and aligns faculty aspirations with the AMC's mission and vision. It is possible for other researchers to adopt the algorithms from our analysis and apply them to their data.
Keywords: career development; data; medical leadership; mentoring.
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