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Acad Med. 2009 Nov;84(11):1610-6. doi: 10.1097/ACM.0b013e3181bb2364.

An integrated approach to evaluate faculty members' research performance.

Author information

1
Office of the Dean, Mount Sinai School of Medicine, New York, New York 10029, USA. rama.iyengar@mssm.edu

Abstract

Medical school administrations need a widely acceptable method of assessing research performance of faculty to make management decisions. The challenge is to identify metrics that allow for comparison across fields. Level of extramural funding, quality of publications, and peer recognition are the commonly used indicators of success. European institutions typically use the impact factors of the journals where their scientists publish, whereas U.S. institutions, including Mount Sinai School of Medicine (MSSM), mostly use grant funding as the major criterion of research productivity. At MSSM, the authors, representing the Dean's Office, collected data on the performance of research faculty in 2006 and 2007 and developed a method to compare the impact factors of publications by individual researchers across disciplines. This was then compared with each individual's research density (grant funding/square foot of research space) to determine whether these measures correlated and whether combining them yielded insights different from using either one independently. Results indicated a weak correlation between the two metrics in 2006 data and no significant correlation in 2007 data. Each faculty member was plotted on a four-quadrant model on the basis of the impact of his or her publications and research density. This dual-metric model allowed for the identification of the strongest and weakest performers and classification of those in between to develop appropriately tailored strategies for mentoring and development at the level of individual faculty. This integrated approach, based on objective numerical criteria, shows promise as a useful method for management of the research enterprise of medical schools.

PMID:
19858825
DOI:
10.1097/ACM.0b013e3181bb2364
[Indexed for MEDLINE]

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