FACETIMING ULTRASOUND SKILLS TO LOW-INCOME COUNTRIES

Summary

This journal article explores the feasibility of using remote tele-intensivists to mentor non-physician healthcare workers in low- and middle-income countries (LMICs) to acquire quality and clinically useful ultrasound (US) images. In the study, nine Haitian non-physician healthcare workers received brief 20-minute training on basic US techniques and were connected via FaceTime to a remote intensivist located in the USA. 

The intensivist guided ultrasound imaging at various anatomic sites. The results indicated that 90% of the FaceTime images were of high quality, and the intensivist felt comfortable making clinical decisions based on these images 89% of the time.

The study concludes that non-physicians in LMICs can obtain high-quality and clinically relevant ultrasound images using video chat software, providing a cost-effective and accessible means of remote training and mentorship for healthcare providers in these resource-constrained settings.

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Quiz Ten

1 / 5

How can healthcare providers address algorithmic bias in endometrial cancer screening?

By avoiding the use of algorithms altogether

By limiting access to screening

By incorporating diverse datasets and regular algorithm evaluations

By using biased algorithms

2 / 5

What is a potential consequence of algorithmic bias in endometrial cancer screening?

More effective treatment options

Decreased screening costs

Reduced need for further testing

Increased false negatives in certain populations

3 / 5

Which of the following can be a source of algorithmic bias in cancer screening?

Standardized patient care protocols

Regular updates to the algorithm

Accurate diagnostic algorithms

Unrepresentative training data

4 / 5

How can algorithmic bias impact endometrial cancer screening outcomes?

By ensuring uniform results across all patients

By eliminating the need for human review

By disproportionately affecting certain patient demographics

By improving overall accuracy

5 / 5

What is algorithmic bias in the context of endometrial cancer screening?

Bias in patient treatment options

Bias in patient selection

Bias introduced by the screening algorithm affecting accuracy

Bias in the doctor's diagnosis

Your score is

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