Research Letter

An immutable truth: planned home births in the United States result in avoidable adverse neonatal outcomes

Natural language processing—the branch of artificial intelligence concerned with the interaction between computers and human language—has advanced markedly in recent years with the introduction of sophisticated deep-learning models. Improved performance in natural language processing tasks, such as text and speech processing, have fueled impressive demonstrations of these models’ capabilities. Perhaps no demonstration has been more impactful to date than the introduction of the publicly available online chatbot ChatGPT in November 2022 by OpenAI, which is based on a natural language processing model known as a Generative Pretrained Transformer. Through a series of questions posed by the authors about obstetrics and gynecology to ChatGPT as prompts, we evaluated the model’s ability to handle clinical-related queries. Its answers demonstrated that in its current form, ChatGPT can be valuable for users who want preliminary information about virtually any topic in the field. Because its educational role is still being defined, we must recognize its limitations. Although answers were generally eloquent, informed, and lacked a significant degree of mistakes or misinformation, we also observed evidence of its weaknesses. A significant drawback is that the data on which the model has been trained are apparently not readily updated. The specific model that was assessed here, seems to not reliably (if at all) source data from after 2021. Users of ChatGPT who expect data to be more up to date need to be aware of this drawback. An inability to cite sources or to truly understand what the user is asking suggests that it has the capability to mislead. Responsible use of models like ChatGPT will be important for ensuring that they work to help but not harm users seeking information on obstetrics and gynecology.

In the United States, 98.3% of patients give birth in hospitals, 1.1% give birth at home, and 0.5% give birth in freestanding birth centers. This review investigated the impact of birth settings on birth outcomes in the United States. Presently, there are insufficient data to evaluate levels of maternal mortality and severe morbidity according to place of birth. Out-of-hospital births are associated with fewer interventions such as episiotomies, epidural anesthesia, operative deliveries, and cesarean deliveries. When compared with hospital births, there are increased rates of avoidable adverse perinatal outcomes in out-of-hospital births in the United States, both for those with and without risk factors. In one recent study, the neonatal mortality rates were significantly elevated for all planned home births: 13.66 per 10,000 live births (242/177,156; odds ratio, 4.19; 95% confidence interval, 3.62–4.84; P<.0001) vs 3.27 per 10,000 live births for in-hospital Certified Nurse-Midwife–attended births (745/2,280,044; odds ratio, 1). These differences increased further when patients were stratified by recognized risk factors such as breech presentation, multiple gestations, nulliparity, advanced maternal age, and postterm pregnancy. Causes of the increased perinatal morbidity and mortality include deliveries of patients with increased risks, absence of standardized criteria to exclude high-risk deliveries, and that most midwives attending out-of-hospital births in the United States do not meet the gold standard for midwifery regulation, the International Confederation of Midwives’ Global Standards for Midwifery Education. As part of the informed consent process, pregnant patients interested in out-of-hospital births should be informed of its increased perinatal risks. Hospital births should be supported for all patients, especially those with increased risks.