Time Pressure Experienced by Internal Medicine Residents in an Educational Hospital in Saudi Arabia: A Qualitative Study

Alqahtani, Dalal Abdullah; Mahzari, Moeber; ALQahtani, Alanoud A.; Rotgans, Jerome I.

doi:10.1016/j.hpe.2020.05.005

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…34 , 39 The time pressure in residency training was highlighted in a recently published study from an internal medicine residency training program, Riyadh, Saudi Arabia, which found that the increasing number of patients was the main source of time pressure. 40 Lack of integration between QIPS and the training program is another barrier highlighted by residents. This observation is congruent with previous studies as residents felt a separation between quality improvement and the patient care setting where they received training.…”

Section: Discussionmentioning

confidence: 99%

Quality Improvement and Patient Safety Education in Internal Medicine Residency Training Program: An Exploratory Qualitative Study

Qarni¹,

Al-Nasser²,

Alzahem³

et al. 2021

AMEP

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Background: Quality improvement and patient safety (QIPS) are a global health priority. Accordingly, QIPS education in medical education became mandatory. Despite that, information about QIPS education in postgraduate training in Saudi Arabia is limited. This study aimed to explore the educational aspects of QIPS in the internal medicine residency training program at King Abdulaziz Hospital in Al Ahsa, Saudi Arabia. Methods: This was a qualitative study employing the constructivist grounded theory approach. The sample size was determined using the theoretical saturation point, and we utilized a purposeful sampling technique. A semi-structured interview was used for data collection and was conducted between September 6 and October 20, 2020. Results: Twenty-two internal medicine trainee residents were required to serve the study purpose. The emerged themes were organized under awareness, education, barriers and opportunities and improvement priorities. Awareness of participants about the QIPS concept, importance, and value of education was found. The participants did not recognize specific dedicated QIPS education components under the structured training program. However, they recognized participation in patient safety-oriented activities but not in quality improvement activities. Consultants' observations and written exams were perceived as the assessment tools. Barriers including time limitation and opportunities including participation in quality improvement projects were identified. Participants suggested making QIPS education mandatory under the training program as an improvement priority. Conclusion:This study highlighted the awareness of internal medicine residents of the QIPS concept, importance, and value of QIPS education. However, we found crucial gaps related to education including lack of a dedicated QIPS component under the training program. There is a need for multicenter studies to measure the magnitude of our findings for improvement of QIPS education in residency training in Saudi Arabia. This is the first study about QIPS education in residency training in Saudi Arabia up to our best knowledge.

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Section: Discussionmentioning

confidence: 99%

Quality Improvement and Patient Safety Education in Internal Medicine Residency Training Program: An Exploratory Qualitative Study

Qarni¹,

Al-Nasser²,

Alzahem³

et al. 2021

AMEP

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Early identification of Family Medicine residents at risk of failure using Natural Language Processing and Explainable Artificial Intelligence

Joshi,

Mortezaagha,

Inkpen

et al. 2024

Preprint

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BackgroundDuring residency, each resident is observed and receives feedback based on their performance. Residency training is demanding, with a few residents struggling in their academic performance. A competency-based residency training program’s success depends on its ability to identify residents with difficulty during their first year of post-graduate education and to provide them with timely intervention and support.ObjectiveIn large training programs such as Family Medicine, identifying residents at risk of failing their certification exams is difficult. We develop a AI system using state-of-the-art technologies in Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and Explainable AI (XAI) to detect at-risk residents automatically.MethodsWe implemented ML, DL and NLP models for the prediction and its performance analysis. The target variable chosen for the prediction was the determination of whether the resident would fail or pass their certification exam. XAI was used to enhance the understanding of the model’s inner workings.ResultsIn total, there were 1382 data points of residents. The champion model, Support Vector Machine (SVM), achieved an accuracy of 89.05% and an F1 score of 74.54 for the multiclass classification when multimodal (text and tabular) data was used. This model outperformed the models that only used qualitative or quantitative data exclusively.ConclusionCombining qualitative and quantitative data represents a novel approach and has provided better classification results. This research demonstrates the feasibility of an automated AI system for the early identification of residents at risk of academic struggle.

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Time Pressure Experienced by Internal Medicine Residents in an Educational Hospital in Saudi Arabia: A Qualitative Study

Cited by 2 publications

References 24 publications

Quality Improvement and Patient Safety Education in Internal Medicine Residency Training Program: An Exploratory Qualitative Study

Quality Improvement and Patient Safety Education in Internal Medicine Residency Training Program: An Exploratory Qualitative Study

Early identification of Family Medicine residents at risk of failure using Natural Language Processing and Explainable Artificial Intelligence

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