Analytics: Unlocking The Data on Postpartum Health and Wellness

Here at the Center for Sacred Window Studies, we share voices from many perspectives and backgrounds. We believe that the sacred weeks post birth, and the experience of humanity is experienced in countless ways. We learn by listening to one another and honoring our stories. The views and opinions of our writers do not necessarily reflect the mission, viewpoints or opinions of the Center for Sacred Window Studies.

While analytics and postpartum care seem completely unrelated, they do in fact work very effectively together. Over the years, research-based and hands-on professionals in postpartum as well as postnatal care have generated tons of recorded healthcare data – and continue to do so today. Meanwhile, data is the fuel to the fire of analytics, which is concerned with finding actionable insights by collecting, organizing, and analyzing unique data sets. As we gear up for the upcoming online research summit on postnatal health and happiness, let’s take a closer look at how analytics is unlocking the data on postpartum care, health, wellness, and development.

Predictive Analytics for Postpartum Depression

Universities and hospitals across the world have been working together on building machine learning (ML) or predictive analytics models for postpartum depression (PPD). Using pregnancy data, university professors from the U.S. and China pursuing research-based careers in public health are using ML models to facilitate the earlier identification of – and intervention for – PPD. In one study involving 508 women, researchers collated pregnancy period information on the women’s social environment factors, mental health, and demographics. Using these data points as predictors, the researchers then used two algorithms to develop four different ML prediction models for PPD.

Machine learning is a field of study that uses computer algorithms that ‘learn’ or improve on their own through experience. The researchers found that in terms of developing the perfect predictive ML model for PPD, different algorithms were more suited for processing different sample sizes and types. Furthermore, the data also revealed that “In the prevention of PPD, more attention should be paid to the psychological resilience of mothers.” All of this underscores how the use of ML or predictive analytics for PPD is still in its developmental stages. That being said, this development has been rapid, which can be attributed to not just the advancement of analytics, but also the large sets of readily available postpartum data in the world.

Here in the U.S. for instance, the postpartum mental health initiatives of local hospitals have not only been successful, but can also provide unique new data sets for developing postpartum analytics tools. Through digital health coaching platforms, rural hospitals in Montana and Alabama are screening for data related to alcohol and substance use, anxiety, depression, and other social determinants of health in at-risk women early in their pregnancies. The programs have been awarded five-figure grants for their success in providing the appropriate care and resources for these women, which can be attributed to the streamlined data collection of their digital platforms. Indeed, the increase in virtual and online postpartum care is ensuring the arrival of fresh data sets for machine learning algorithms to digest. In short, while we are just witnessing the beginnings of how analytics can advance postpartum care, this specific field of analytics shows great promise.

Shaping the Future of Postpartum and Healthcare Analytics

Healthcare analytics itself has been a growing field in recent years, following the digital transformation of healthcare and other key industries. This is shown in how there is now a much wider range of jobs in healthcare related to analytics across all fields. Those training for a career in nursing can now pursue different data-driven and increasingly in-demand positions that bridge the gaps between healthcare and analytics. An example of this is a nursing informatics specialist whose job it is to increase the efficiency of data management within healthcare. And this is just the tip of the iceberg in terms of how analytics is shaping modern healthcare professions.

Given how these aforementioned data-driven competencies are fast growing in the field of public healthcare, the use of analytics for predicting PPD is just the start of this ongoing development. With genetic research and information in the mix, more hereditary mental and physical factors can be included into the overall data. This can lead to not just more accurate predictions, but more diseases and conditions to predict. As informatics takes the lead in healthcare facilities, data organizing, collection, and analysis can be further streamlined across healthcare applications.

In the era of big data, analytics is shaping the world’s most essential industries – and healthcare is no exemption. In the near future, we are bound to see more and even better applications of this technology, not just in postpartum care, but healthcare in general.

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