A survey of barriers and facilitators to ultrasound use in low- and middle-income countries

Abstract

Point-of-care ultrasound has the potential to help inform assessment, diagnosis, and management of illness in low- and middle-income countries (LMIC). To better understand current ultrasound use, barriers and facilitators to use, and perceptions and practices in LMIC, we conducted an anonymous online global survey targeting healthcare providers training and using ultrasound in LMIC. A total of 241 respondents representing 62 countries participated and most were physicians working in publicly-funded urban tertiary hospitals in LMIC. Most had received ultrasound training (78%), reported expertise (65%) and confidence (90%) in ultrasound use, and had access to ultrasound (88%), utilizing ultrasound most commonly for procedures and for evaluations of lungs, heart, and trauma. Access to an ultrasound machine was reported as both the top barrier (17%) and top facilitator (53%); other common barriers included access to education and training, cost, and competition for use and other common facilitators included access to a probe, gel, and electricity, and acceptance by healthcare providers, administrators, and patients. Most (80%) noted ultrasound access was important and 96% agreed that ultrasound improves quality of care and patient outcomes. Improving access to low-cost ultrasound equipment is critical to increasing ultrasound use among those who are trained.

Deep learning to estimate gestational age from fly‐to cineloop videos: A novel approach to ultrasound quality control

“…A primary concern is the training and credentialing of healthcare workers, such as midwives and nurses, who will operate these devices. 4,5 Although training healthcare workers to perform basic sonography tasks like fetal biometry is achievable, ensuring the quality and consistency of ultrasound examinations at scale is a more complex task. 6,7 A system capable of automatically monitoring the quality of ultrasound examinations while producing minimal disturbance to care delivery on a busy obstetrics service would provide significant value in addressing this challenge.…”

“…However, implementing and expanding the WHO's universal ultrasound recommendations in such settings faces numerous logistical, infrastructural, human capacity, and financial challenges. A primary concern is the training and credentialing of healthcare workers, such as midwives and nurses, who will operate these devices 4,5 …”

A pre-implementation study on the experience of ultrasound scanning in selected healthcare facilities of Lusaka, Zambia: the perspectives of pregnant women and male partners

“…More importantly, one ultrasound scan is recommended for pregnant women before 24 weeks gestation because it allows for accurate and consistent use of the estimated delivery date and, as a result, has a high potential to reduce preterm and stillbirths [1,26,27] . According to a number of authors [18,28] and various literature from African countries [20,29] , including Zambia [30][31][32][33][34][35] , for health interventions to be effective, they must improve implementers' skills, promote user interaction, be relevant, appropriately inform users, and be accessible, inexpensive, culturally and socially appropriate, and sustainable. Despite the [25] recommendations for reducing perinatal mortalities, Zambia faces a signi cant challenge in effectively preventing and managing preterm birth and stillbirths due to the lack of an accurate, consistently applied estimated date of delivery (EDD) for each pregnant woman, which is obtained through ultrasound scanning.…”

“…Individual social factors mainly associated with low levels of education [15] , include excessive stress levels, traditional beliefs, and nonattendance to prenatal care, contribute to the etiological causes [3,16] . Other structural factors include a lack of infrastructure, a shortage of maternity sta ng, infection exposure, a lack of ultrasound assessment, and a lack of reliable transportation to and from healthcare facilities [17][18][19][20][21].…”

Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks

“…In addition to its enhanced portability, safety, and affordability, LUS has exhibited diagnostic accuracy for a variety of respiratory conditions that is comparable to traditional modalities, such as chest radiography [1][2][3][4] and computed tomography [2,[5][6][7]. Despite mounting evidence for its efficacy, there are barriers to the widespread adoption of POCUS, including reduced availability of training and lack of access to devices [8][9][10]. Multiple studies have proposed machine learning solutions for routine tasks in LUS interpretation, citing automation as a means to improve access to LUS [11][12][13].…”