An innovative web-based decision-aid about birth after cesarean for shared decision making in Taiwan: study protocol for a randomized control trial

Abstract

Background Taiwan has a high national caesarean rate coupled with a low vaginal birth after caesarean (VBAC) rate. This study aims to develop and evaluate a web-based decision-aid with communication support tools, to increase shared decision making (SDM) about birth after caesarean. Methods A quantitative approach will be adopted using a randomized pre-test and post-test experimental design in a medical centre in northern Taiwan. The web-based decision aid consists of five sections. Section 1 provides a two-part video to introduce SDM and how to participate in SDM. Section 2 presents an overview of functions and features of the birth decision-aid. Section 3 presents relevant VBAC information, including definitions, benefits and risks, and an artificial intelligence (AI) calculator for rate and likelihood of VBAC success. Section 4 presents the information regarding elective repeat caesarean delivery (ERCD), involving definitions, benefits, and risks. Section 5 comprises four steps of decision making to meet women’s values and preferences. Pregnant women who have had one previous caesarean and are eligible for VBAC, will be recruited at 14–16 weeks. Participants will complete a baseline survey prior to random allocation to either the control group (usual care) or intervention group (usual care plus an AI-decision aid). A follow up survey at 35–38 weeks will measure change in decisional conflict, knowledge, birth mode preference, and decision-aid acceptability. Actual birth outcomes and satisfaction will be assessed one month after birth. Discussion The innovative web-based decision-aid with support tools will help to promote pregnant women’s decision-making engagement and communication with their providers and improve opportunities for supportive communication about VBAC SDM in Taiwan. Linking web-based AI data analysis into the medical record will also be assessed for feasibility during implementation in clinical practice. Trial registration ClinicalTrials.gov identifier (NCT05091944), Registered on October 24, 2021.

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

“…We developed an AI predictive model for VBAC, which was integrated into an innovative DA birth choice platform for SDM. 10 In contrast to conventional DA like booklets, video CDs, pamphlets, or computer-based tools commonly used to aid pregnant women in making birth choices after previous cesarean delivery, 25 – 29 this platform integrates an AI predictive model. Through this model, women can input seven crucial factors—maternal age, gravidity, BMI, gestational week, history of previous vaginal birth, prolonged labor, gestational diabetes, and pregnancy-induced hypertension—to promptly ascertain their likelihood of achieving a successful VBAC.…”

“… 4 , 5 Drawing on previous research findings, 6 – 9 we created an innovative web-based platform serving as the basis for a decision-aid (DA) system for birth choices aimed at fostering shared decision-making (SDM). 10 Through this DA platform, we strive to enhance understanding, boost confidence, and improve communication between medical professionals and pregnant women. The DA platform could be easily operated for pregnant women and healthcare professionals through mobile devices, such as smartphones, tablets, and computers.…”

“…The DA platform could be easily operated for pregnant women and healthcare professionals through mobile devices, such as smartphones, tablets, and computers. 10 The DA platform is equipped with essential features, including a video to introduce SDM, an overview of the functions and features of the birth DA, and comprehensive information on the risks and benefits of VBAC and ERCD. 10 Most importantly, the platform incorporates an AI calculator to empower SDM between healthcare providers and pregnant women by providing personalized, data-driven insights and risk assessments for VBAC.…”

“… 10 Most importantly, the platform incorporates an AI calculator to empower SDM between healthcare providers and pregnant women by providing personalized, data-driven insights and risk assessments for VBAC. 10 …”

Effectiveness of shared decision‐making for mode of delivery after caesarean section: A systematic review and meta‐analysis of randomized controlled trials

“…Our systematic review revealed the current format of SDM inter- DAs fall into a different category of access to information because they present information in a way that is tailored to each individual (Chen et al, 2023;Syrowatka et al, 2016). These DAs also enhance understanding in a consistent way by providing systematic feedback to the user as well as using graphical, numerical and textual anchoring formats.…”