To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address ‘system gaps’ and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, ‘holistically’ developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.
The majority of women suffering from maternal morbidities live in resource-constrained settings with diverse barriers preventing access to quality biomedical health care services. This study aims to highlight the dynamics between the public health system and alternative healing through an exploration of the experiences of health care seeking among women living with severe symptomatic pelvic organ prolapse in an impoverished setting.
The data were collected through ethnographic fieldwork at the hospital and community levels in the Amhara region of Ethiopia. The fieldwork included participant observation, 42 semi-structured interviews and two focus group discussions over a period of one year. A group of 24 women with severe symptomatic pelvic organ prolapse served as the study’s main informants. Other central groups of informants included health care providers, local healers and actors from the health authorities and non-governmental organisations.
Three case stories were chosen to illustrate the key findings related to health care seeking among the informants. The women strove to find remedies for their aggravating ailment, and many navigated between and combined various available healing options both within and beyond the health care sector. Their choices were strongly influenced by poverty, by lack of knowledge about the condition, by their religious and spiritual beliefs and by the shame and embarrassment related to the condition. An ongoing health campaign in the study area providing free surgical treatment for pelvic organ prolapse enabled a study of the experiences related to the introduction of free health services targeting maternal morbidity.
This study highlights how structural barriers prevent women living in a resource-constrained setting from receiving health care for a highly prevalent and readily treatable maternal morbidity such as pelvic organ prolapse. Our results illustrate that the provision of free quality services may dramatically alter both health-and illness-related perceptions and conduct in an extremely vulnerable population.