Dataset Information

Monitoring Personalized Learning Curves for Emergency Ultrasound With Risk-adjusted Learning-curve Cumulative Summation Method.

ABSTRACT:

Background

Ultrasound (US) has been a regular practice in emergency departments for several decades. Thus, train our students to US is of prime interest. Because US image acquisition ability can be very different from a patient to another (depending on image quality), it seems relevant to adapt US learning curves (LCs) to patient image quality using tools based on cumulative summation (CUSUM) as the risk-adjusted LC CUSUM (RLC).

Objectives

The aim of this study was to monitor LC of medical students for the acquisition of abdominal emergency US views and to adapt these curves to patient image quality using RLC.

Methods

We asked medical students to perform abdominal US examinations with the acquisition of 11 views of interest on emergency patients after a learning session. Emergency physicians reviewed the student examinations for validation. LCs were plotted and the student was said proficient for a specific view if his LC reached a predetermined limit fixed by simulation.

Results

Seven students with no previous experience in US were enrolled. They performed 19 to 50 examinations of 11 views each. They achieve proficiency for a median of 9 (6-10) views. Aorta and right pleura views were validated by seven students; inferior vena cava, right kidney, and bladder by six; gallbladder and left kidney by five; portal veins and portal hilum by four; and subxyphoid and left pleura by three. The number of US examinations required to reach proficiency ranged from five to 41 depending on the student and on the type of view. LC showed that students reached proficiency with different learning speeds.

Conclusions

This study suggests that, when monitoring LCs for abdominal emergency US, there is some heterogeneity in the learning process depending on the student skills and the type of view. Therefore, rules based on a predetermined number of examinations to reach proficiency are not satisfactory.

SUBMITTER: Peyrony O

PROVIDER: S-EPMC6001586 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Publications

Monitoring Personalized Learning Curves for Emergency Ultrasound With Risk-adjusted Learning-curve Cumulative Summation Method.

Peyrony Olivier O Legay Léa L Morra Ivonne I Verrat Anne A Milacic Hélène H Franchitti Jessica J Amami Jihed J Gillet Ariane A Azarnoush Kouchiar K Elezi Arben A Bragança Adélia A Taboulet Pierre P Bourrier Pierre P Fontaine Jean-Paul JP Resche-Rigon Matthieu M

AEM education and training 20171122 1

<h4>Background</h4>Ultrasound (US) has been a regular practice in emergency departments for several decades. Thus, train our students to US is of prime interest. Because US image acquisition ability can be very different from a patient to another (depending on image quality), it seems relevant to adapt US learning curves (LCs) to patient image quality using tools based on cumulative summation (CUSUM) as the risk-adjusted LC CUSUM (RLC).<h4>Objectives</h4>The aim of this study was to monitor LC o ...[more]

PMID: 30051059

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Project description:BackgroundThe varying experience of surgeons and ultrasound physicians, and their collaboration with physicians, may affect operation time and efficiency. We evaluated the learning curve of ultrasound-guided vacuum-assisted excision (VAE) of breast lesion with collaboration between different physicians, and assessed characteristics associated with operation time.MethodsThe sample population of this retrospective study was divided into two groups: 49 consecutive patient surgeries completed by skilled surgeons and novice ultrasound physicians (U group); and 30 consecutive patient surgeries completed by skilled ultrasound physicians and novice surgeons (S group). Cumulative summation graphs were used to evaluate operation time and calculate the turning point of the learning curve. Patients in the U and S groups were divided into exploration stage and proficiency stage according to the turning point, and the differences in influencing factors were compared. A total of 548 patients who underwent vacuum-assisted breast excision performed by a combination of skilled surgeons and skilled ultrasound physicians were selected as the reference group (R group). The differences among the three groups were compared. The relationship between the operation time and other factors in the different groups was analyzed using linear regression.ResultsThe best learning curve of the sample population was the quadratic fitting equation, and the turning point was the 19th case in the U group and the 14th case in the S group. The total operation times in the proficiency stage were significantly shorter than those in the exploration stage in the U and S groups (P=0.012 and P=0.003, separately). Patient age, long diameter, short diameter, and depth of masses related to the operation time.ConclusionsOur data suggest the existence of different learning curves in ultrasound-guided vacuum-assisted excision for the collaborations of different stages surgeons and ultrasound physicians. Through the accumulation of experience, it is feasible to safely perform ultrasound-guided VAE of breast lesions.

Dataset Information

Monitoring Personalized Learning Curves for Emergency Ultrasound With Risk-adjusted Learning-curve Cumulative Summation Method.

Background

Objectives

Methods

Results

Conclusions

Publications

Monitoring Personalized Learning Curves for Emergency Ultrasound With Risk-adjusted Learning-curve Cumulative Summation Method.

Similar Datasets

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