1. The ERAS Application Can Predict ACGME Competency-Based Surgical Resident Performance Amy M. Tolan MD *, Amy H. Kaji MD PhD *, Chi Quach †, O.Joe Hines MD †, and Christian de Virgilio MD * * Harbor-UCLA Medical Center, Torrance, CA † David Geffen-UCLA School of Medicine, Los Angeles, CA Amy M. Tolan MD *, Amy H. Kaji MD PhD *, Chi Quach †, O.Joe Hines MD †, and Christian de Virgilio MD * * Harbor-UCLA Medical Center, Torrance, CA † David Geffen-UCLA School of Medicine, Los Angeles, CA

2. INTRODUCTION Resident selection: daunting task Factors used in decision-making process: Grades USMLE scores AOA Letters of recommendation Faculty interviews Resident selection: daunting task Factors used in decision-making process: Grades USMLE scores AOA Letters of recommendation Faculty interviews

3. PRIOR STUDIES AOA predictive of future success (J Am Coll Surg 2006) USMLE predictive of higher In-training exam scores and board pass rates (J Surg Educ 2007) Grades/Honors in 3rd-yr clerkships AOA predictive of future success (J Am Coll Surg 2006) USMLE predictive of higher In-training exam scores and board pass rates (J Surg Educ 2007) Grades/Honors in 3rd-yr clerkships

4. PURPOSE To determine whether information collected in ERAS application would predict strong performance on ACGME competency-based evaluations To determine whether information collected in ERAS application would predict strong performance on ACGME competency-based evaluations

5. METHODS Age Gender AOA Research Number of publications Extended volunteerism Number of non-English languages Age Gender AOA Research Number of publications Extended volunteerism Number of non-English languages Leadership experience Teaching experience Advanced degrees (PhD, MPH) USMLE step 1 score Honors in core 3rd yr clinical clerkships Medical school rank  Predictor variables:  Retrospective correlative analysis  2 institutions: Harbor-UCLA, UCLA

6. METHODS Outcome variables : Scores on the 6 ACGME core competencies (1-9 scale @ Harbor, and 1-5 scale @ UCLA) Technical skills Overall competency = average score of all 6 competencies + technical skills Outcome variables : Scores on the 6 ACGME core competencies (1-9 scale @ Harbor, and 1-5 scale @ UCLA) Technical skills Overall competency = average score of all 6 competencies + technical skills

7. RESULTS 77 residents (37: Harbor UCLA, 40: UCLA) 30 Female, 47 Male Not predictive: Research Number of publications Additional languages spoken Leadership experience Teaching experience Extended volunteerism Medical school rank Honors during the third year Medicine clerkship 77 residents (37: Harbor UCLA, 40: UCLA) 30 Female, 47 Male Not predictive: Research Number of publications Additional languages spoken Leadership experience Teaching experience Extended volunteerism Medical school rank Honors during the third year Medicine clerkship

8. PCMKPBLICPSBP Older Age 0.04 p=0.03 0.04 p=0.03 Female 0.28 p=0.01 0.32 p=0.002 0.36 p=0.002 AOA 0.28 p=0.02 0.32 p=0.02 0.27 p=0.03 USMLE 0.001 p=0.004 PhD 0.21 p=0.02 Honors FP 0.26 p=0.05 Honors Ob/gyn 0.31 p=0.004 0.32 p=0.01 0.33 p=0.004 0.26 p=0.02 0.34 p=0.005 Honors Peds 0.29 p=0.01 0.25 p=0.05 0.28 p=0.04 0.26 p=0.05 Honors Psych 0.25 p=0.05 Honors Surgery 0.29 p=0.02 Total Honors 0.09 p=0.002 0.09 p=0.01

9. TSOverall Female 0.23 p=0.02 AOA 0.23 P=0.06 Honors Ob/gyn 0.22 p=0.03 Honors Peds 0.22 p=0.05 Total Honors 0.06 p=0.04 0.06 p=0.04 RESULTS

10. MULTIVARIABLE ANALYSIS Medical Knowledge USMLE ( 0.076, p=0.02) Practice-Based Learning Honors Ob/gyn ( 0.3, p=0.04) Interpersonal Communication Female gender ( 0.24,p=0.04) Medical Knowledge USMLE ( 0.076, p=0.02) Practice-Based Learning Honors Ob/gyn ( 0.3, p=0.04) Interpersonal Communication Female gender ( 0.24,p=0.04) Professionalism Older age ( 0.03,p=0.04) Honors Ob/gyn ( 0.22, p=0.04) System-Based Practice Honors Ob/gyn ( 0.34, p=0.005) Technical Skills Total number of honors ( 0.06 p=0.04)

11. DISCUSSION Limitations: Only 2 institutions Did not include assessments of faculty interview, letters of recommendation Limitations: Only 2 institutions Did not include assessments of faculty interview, letters of recommendation

12. CONCLUSION ERAS application is useful for predicting subsequent competency based performance in surgical residents Honors in Ob/Gyn: PBL, P, SBP Female gender: IC Older age: P Total number of honors: TS USMLE: MK (benefit small) Honors in Surgery not predictive ERAS application is useful for predicting subsequent competency based performance in surgical residents Honors in Ob/Gyn: PBL, P, SBP Female gender: IC Older age: P Total number of honors: TS USMLE: MK (benefit small) Honors in Surgery not predictive

13. THANK YOU Insert UCLA Logo here:)