The ERAS Application Can Predict ACGME

Competency-Based Surgical Resident Performance

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

INTRODUCTIONINTRODUCTION

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

PRIOR STUDIESPRIOR 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

PURPOSEPURPOSE

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

METHODSMETHODS

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

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

METHODSMETHODS 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

RESULTSRESULTS 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

PC MK PBL IC P SBP

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/gyn0.31

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

Psych0.25

p=0.05

Honors

Surgery0.29

p=0.02

Total Honors 0.09

p=0.002

0.09

p=0.01

TS Overall

Female 0.23

p=0.02

AOA 0.23

P=0.06

Honors

Ob/gyn0.22

p=0.03

Honors Peds 0.22

p=0.05

Total Honors 0.06

p=0.04

0.06

p=0.04

RESULTS

MULTIVARIABLE ANALYSISMULTIVARIABLE 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)

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)

DISCUSSIONDISCUSSION

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

CONCLUSIONCONCLUSION 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

THANK YOUTHANK YOU

Insert UCLA Logo here:)