Acceptability and feasibility of a low-cost device for gestational age assessment in a low-resource setting : qualitative study

Abstract

Background Ultrasound for gestational age (GA) assessment is not routinely available in resource-constrained settings, particularly in rural and remote locations. The TraCer device combines a handheld wireless ultrasound probe and a tablet with artificial intelligence (AI)-enabled software that obtains GA from videos of the fetal head by automated measurements of the fetal transcerebellar diameter and head circumference. Objective The aim of this study was to assess the perceptions of pregnant women, their families, and health care workers regarding the feasibility and acceptability of the TraCer device in an appropriate setting. Methods A descriptive study using qualitative methods was conducted in two public health facilities in Kilifi county in coastal Kenya prior to introduction of the new technology. Study participants were shown a video role-play of the use of TraCer at a typical antenatal clinic visit. Data were collected through 6 focus group discussions (N=52) and 18 in-depth interviews. Results Overall, TraCer was found to be highly acceptable to women, their families, and health care workers, and its implementation at health care facilities was considered to be feasible. Its introduction was predicted to reduce anxiety regarding fetal well-being, increase antenatal care attendance, increase confidence by women in their care providers, as well as save time and cost by reducing unnecessary referrals. TraCer was felt to increase the self-image of health care workers and reduce time spent providing antenatal care. Some participants expressed hesitancy toward the new technology, indicating the need to test its performance over time before full acceptance by some users. The preferred cadre of health care professionals to use the device were antenatal clinic nurses. Important implementation considerations included adequate staff training and the need to ensure sustainability and consistency of the service. Misconceptions were common, with a tendency to overestimate the diagnostic capability, and expectations that it would provide complete reassurance of fetal and maternal well-being and not primarily the GA. Conclusions This study shows a positive attitude toward TraCer and highlights the potential role of this innovation that uses AI-enabled automation to assess GA. Clarity of messaging about the tool and its role in pregnancy is essential to address misconceptions and prevent misuse. Further research on clinical validation and related usability and safety evaluations are recommended. Keywords: gestational age, gestation, gynecology, gynecologist, prenatal, antenatal, maternal, fetus, fetal, ultrasound, imaging, pregnancy dating, handheld, portable, trust, artificial intelligence, sub-Saharan Africa, Africa, low cost, LMIC, low income, feasibility, acceptability, AI, pregnancy, pregnant, maternity, women's health, obstetrics, obstetrician, rural, remote, remote location, misconception, eHealth, digital health