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Related Experiment Video

Updated: Mar 19, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Value of supervised learning events in predicting doctors in difficulty.

Mumtaz Patel¹, Steven Agius², Jack Wilkinson³

¹Department of Renal Medicine, Manchester Royal Infirmary, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK.

Medical Education

|June 14, 2016

Summary

This summary is machine-generated.

Supervised Learning Events (SLEs) can predict doctors in difficulty, particularly the Team Assessment of Behaviour (TAB) and Educational Supervisor Report (ESR). However, improving feedback quality is crucial for effective identification and management of trainees needing support.

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Last Updated: Mar 19, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Medical Education
Doctor Training
Performance Assessment

Background:

Supervised Learning Events (SLEs) replaced traditional assessments for UK foundation-year doctors in 2012, emphasizing trainee reflection and assessor feedback.
The effectiveness of SLEs in predicting doctors in difficulty (DiD) remains under-investigated.
This study sought to understand how and why SLEs succeed or fail in identifying DiD.

Purpose of the Study:

To identify principles guiding the effective use of SLEs in identifying doctors in difficulty.
To evaluate the predictive value of different SLE components for DiD.

Main Methods:

Retrospective case-control study of electronic portfolios from North West Foundation School trainees.
Free-text supervisor comments from SLEs were assessed for four domains of Good Medical Practice and scored for level of concern.
Quantitative analysis of cumulative SLE scores and qualitative thematic analysis of feedback were performed.

Main Results:

The prevalence of DiD was 6.5%.
Team Assessment of Behaviour (TAB) and Educational Supervisor Report (ESR) were strongly predictive of DiD status.
Qualitative analysis revealed inadequate report completion and a lack of constructive feedback, indicating underutilization of SLEs.

Conclusions:

TAB and ESR are key predictors of doctors in difficulty.
Current SLE implementation is suboptimal due to issues with report completion and feedback quality.
Improving the quality of SLE reports and feedback is essential for better identification and management of DiD.