In case you don’t know what it is that I am talking about here, let me begin with an introduction. Fetal heart rate monitoring has historically relied on maternity professionals to hear or see information about the fetal heart rate, and to apply their knowledge to interpret fetal heart rate patterns. Then the professional categorises the pattern as normal or not. This category is then (ideally) considered in relation to what else is known about the woman and her fetus and a recommendation for ongoing care is decided.

As a consequence of the ongoing failings of CTG monitoring to produce the sorts of outcomes that people imagine that it is able to, technology has increasingly been introduced into this process. CTG data has been digitised so it can be displayed on screens, moved to other places (hence the creation of central fetal monitoring), and viewed by multiple people. It cal also be subject to analysis by software designed to interpret sections of the CTG in real time.

These technologies are complex, expensive, and time consuming to develop. They are expensive to introduce and maintain. They require additional staff training, and introduce new problems to maternity care that did not exist before (Small et al., 2021). So why bother? There must be a really seductive reason to go to the lengths required for multiple versions of advanced fetal monitoring technology to have been developed, with an ongoing huge interest in coming up with something new.

The triple aim

Author Jessica Morley (2025) sets out the rhetoric that drives digital health technology investment in general (not specific to fetal monitoring), writing –

The idea is that by using digital health technologies and artificial intelligence to collect, integrate, and analyse clinical, multi-omic, and epidemiological data on individuals, it will be possible for researchers, clinicians, and system designers to develop a more detailed understanding of the individual mechanisms of disease (ie. prognosis, diagnosis, and treatment) and so learn how to make the practice of medicine more personalised, predictive, preventative, and participatory. In turn, it is hoped that this learning process will result in an improvement in the clinical efficaciousness and cost-effectiveness of care, and ultimately enable healthcare systems to achieve the so-called “triple aim” of healthcare outcomes, improved experience of care, and reduced per capita costs.

You can see how this has played out for fetal monitoring. Digitising the CTG and storing that data in large databases has provided a mass of data for researchers to analyse. This has in turn made it possible to develop and test artificial intelligence algorithms for CTG interpretation. Women have not been adequately consulted on whether they want to contribute to this data collection, the use of their personal data in this way, and there is no mechanism for women to be financially rewarded when the resulting technology their data made possible generates an income for the technology developers.

It is also clear that one of the “triple aims” is driving technology development more forcefully than the others, and this is particularly visible in the UK. The high cost of legal claims for maternity care provision settled through the NHS Resolution scheme is clearly driving the Avoiding Brain Injury in Childbirth (ABC) project, which makes use of fetal monitoring technology systems. While this project has one eye on improved clinical outcomes, the cost inventive clearly captured the minds of decision-makers in the health system, and justified the investment in the program.

Here’s the irony…

If CTG monitoring actually worked as people hoped and imaged it does, there would be fewer poor outcomes from maternity care, and the cost of claims would be less. There would therefore be less incentive to develop CTG technology. The mismatch between what CTG monitoring is assumed to achieve and what it can actually achieve sits at the very heart of technology development in this field.

There is a financial boon happening for the tech developers who manage to get a product to market. Women’s data about themselves and their fetus(es) is fundamental to the development, training, and assessment of these systems. Women are barely being asked to consent to CTG use in clinical practice, so I’m confident that they are not being asked to permit the use of their data in this way. It reminds me of current concerns that writers and artists have about the use of their outputs to train artificial intelligence models that might then replace the need for their work.

The ethical quagmire here is significant. Women are expected to make use of a technology that produces demonstrably worse outcomes for themselves, without evidence of a better outcome for their babies. They are asked to do this so that technology can be developed that reduce the costs of compensation claims for healthcare systems. It remains possible that some of the cost reduction will be achieved through avoiding poor outcomes. But it is also possible that there will be little change in perinatal outcomes but more protection for maternity professionals against litigation. If the algorithm said that all was well and something bad happened, the professional will have little to answer to.

In other words it will make it more difficult for women to be financially compensated if their baby is harmed during their birth.

Sartwelle, Johnston and Arda (2017) summarise this nightmarish situation well:

The use of a scientifically bankrupt machine solely to protect healthcare providers from trial lawyers and law suits when the machine is known to be harming mothers and babies is an egregious conflict of interest and outrageous endorsement of obstetrical defensive medicine … and is undeniable proof that evidence-based standard of care and bioethical principles are nothing more than empty rhetoric.

p. 6

I do hope I will ultimately be proved wrong. The history of the development of fetal heart rate monitoring technology has thus far not worked favourably for women. If future technology is to solve the problem then co-design approaches with women must be part of the process from inception.

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References

Morley, J. (2025). Healthcare and Well-being. In A Companion to Digital Ethics (eds L. Floridi and M. Taddeo).

Sartwelle, T. P., Johnston, J. C., & Arda, B. (2017). A half century of electronic fetal monitoring and bioethics: silence speaks louder than words. Maternal Health, Neonatology, & Perinatololgy, 3(1), 21. https://doi.org/10.1186/s40748-017-0060-2 

Small, K. A., Sidebotham, M., Gamble, J., & Fenwick, J. (2021, Jun 24). “My whole room went into chaos because of that thing in the corner”: Unintended consequences of a central fetal monitoring system. Midwifery, 102, 103074. https://doi.org/10.1016/j.midw.2021.10307

• Should fetal blood sampling become a thing of the past?

Categories: CTG, Reflections

Tags: ABC, Artificial intelligence, central fetal monitoring, computerised interpretation, ethics, litigation, technology