Opening the black box: the patient mix of GP trainees.

BACKGROUND

The variety of health problems (patient mix) that medical trainees encounter is presumed to be sufficient to master the required competencies.

AIM

To describe the patient mix of GP trainees, to study differences in patient mix between first-year and third-year GP trainees, and to investigate differences in exposure to sex-specific diseases between male and female trainees.

DESIGN AND SETTING

Prospective cohort study in Dutch primary care.

METHOD

During a 6-month period, aggregated data about International Classification of Primary Care diagnosis codes, and data on the sex and age of all contacts were collected from the electronic patient record (EPR) system.

RESULTS

Seventy-three trainees participated in this study. The mean coding percentage was 86% and the mean number of face-to-face consultations per trimester was 450.0 in the first year and 485.4 in the third year, indicating greater variance in the number of patient contacts among third-year trainees. Diseases seen most frequently were: musculoskeletal (mean per trimester = 89.2 in the first year/91.0 in the third year), respiratory (98.2/92.7) and skin diseases (89.5/96.0). Least often seen were diseases of the blood and blood-forming organs (5.3/7.2), male genital disorders (6.1/7.1), and social problems (4.3/4.2). The mean number of chronic diseases seen per trimester was 48.0 for first-year trainees and 62.4 for third-year trainees. Female trainees saw an average of 39.8 female conditions per trimester--twice as many as male trainees (mean = 21.3).

CONCLUSION

Considerable variation exists trainees in the number of patient contacts. Differences in patient mix between first- and third-year trainees seem at least partly related to year-specific learning objectives. The use of an EPR-derived educational instrument provides insight into the trainees' patient mix at both the group and the individual level. This offers opportunities for GP trainers, trainees, and curriculum designers to optimise learning when exposure may be low.