Evaluation of the promotion criteria in an academic medical centre in Singapore

INTRODUCTION

Academic medical centres (AMCs) have the tripartite mission of performing research to advance healthcare delivery, educating future clinicians and providing healthcare services. This study investigates the criteria associated with being promoted in a Singaporean AMC.

METHODS

Using a dataset of 255 candidates for promotion at the studied AMC, we employ logistic regression to determine if these factors are associated with the likelihood of promotion. Further, we use interaction effects to test if the relationship between the H-index and likelihood of promotion differs across the academic levels of the candidates.

RESULTS

The logistic regression results based on the best of our three tested models suggest that the H-index is positively associated with promotion for those applying to become clinical associate professors (OR=1.43, p=0.01). Moreover, candidates who provide well-developed education portfolios (OR=3.61, p=0.02) and who have held service/leadership roles (OR=6.72, p<0.001) are more likely to be promoted.

CONCLUSIONS

This study affirms the correlation between promotion and the advancement criteria outlined by the AMC. This is important for transparency and trust between the AMC and its faculty in their applications for promotion and success in an academic career. Further, our study is one of the few empirical studies linking promotion criteria to promotion outcomes.

Calculator for predicting the probability of faculty promotion in an academic medical centre

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.