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Τρίτη 18 Ιουλίου 2017

The Quality In-Training Initiative: Giving Residents Data to Learn Clinical Effectiveness

Publication date: Available online 17 July 2017
Source:Journal of Surgical Education
Author(s): Morgan M. Sellers, Matt Fordham, Craig W. Miller, Clifford Y. Ko, Rachel R. Kelz
BackgroundTraining programs are expected to provide clinical outcomes data to residents. Few systems have the necessary infrastructure. We evaluated initial adoption and use of the Quality In-Training Initiative (QITI) platform linking National Surgical Quality Improvement Program (NSQIP) data to trainees.Study DesignProportions of Accreditation Council for Graduate Medical Education general surgery residency programs with differing levels of NSQIP and QITI affiliation were calculated and program characteristics were compared. All NSQIP sites that captured QITI custom field data from July 2013 to June 2016 were included in case analysis. Differences in case collection were compared between participating (P) sites that actively participated in QITI and nonparticipating (NP) sites that did not. Resident participation by procedure type was examined.ResultsOf 268 accredited general surgery residency programs, 92% (n = 248) is affiliated with a NSQIP hospital and 61% of all clinical months is spent at NSQIP sites. For 42% of all programs (n = 114), the primary teaching hospital is affiliated with the QITI. In all, 74 P sites and 89 NP sites captured a total of 417,816 cases. The median number of cases captured per site was statistically higher for P sites (3063) compared with NP sites (2307, p < 0.001).A total of 68.3% of all cases captured had resident participation indicated by postgraduate year (n = 285,469). The most common procedures with resident participation were laparoscopic appendectomy (n = 17,082, 6.0%) and laparoscopic cholecystectomy (n = 15,502, 5.4%). Percentage coverage rates ranged from 17.3% to 91.8%.ConclusionMost general surgery rotations are at NSQIP sites. Identifying resident participation in captured NSQIP cases is feasible on a large scale. Captured cases reflect national case-mix. The platform has the potential to collect data on institutional and program-level variation in resident operative experience that may be used to improve training.



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