Relationship between resident workload and self-perceived learning on inpatient medicine wards: a longitudinal study

Abstract

Background: Despite recent residency workload and hour limitations, little research on the relationship between workload and learning has been done. We sought to define residents' perceptions of the optimal patient workload for learning, and to determine how certain variables contribute to those perceptions. Our hypothesis was that the relationship between perceived workload and learning has a maximum point (forming a parabolic curve): that either too many or too few patients results in sub-optimal learning.

Methods: Residents on inpatient services at two academic teaching hospitals reported their team and individual patient censuses, and rated their perception of their learning; the patient acuity; case variety; and how challenged they felt. To estimate maximum learning scores, linear regression models with quadratic terms were fit on learning score.

Results: Resident self-perceived learning correlated with higher acuity and greater heterogeneity of case variety. The equation of census versus learning score, adjusted for perception of acuity and case mix scores, showed a parabolic curve in some cases but not in others.

Conclusion: These data suggest that perceived resident workload is complex, and impacted by additional variables including patient acuity and heterogeneity of case variety. Parabolic curves exist for interns with regard to overall census and for senior residents with regard to new admissions on long call days.

Figure 1

Theoretical model of resident learning. In a proposed theoretical model for resident learning, learning is optimal at a census or workload (Point A); where patient volume, acuity and variety (all contributing to workload) are adequate and appropriate for resident learning. Resident learning is less optimal when residents have either too few patients (inadequate workload, Point B) or too many patients (overwhelming workload, Point C).

Figure 2

Parabolic curves for learning vs. number of patients for R1's and R2/R3's. Parabolic curves generated from multivariate models using quadratic equations demonstrate the relationships between learning and census or new admissions, adjusted for acuity and case variety for R1s as compared to R2s/R3s.