Women’s health 2010;857657
4.
https://www.sciencehistory.org/stories/ magazine/sickening-sweet/
5.
https://gatescientific.com/technique-geeks- blog/f/history-of-diagnostic-testing-a-march- to-point-of-care
6. The History of the Pregnancy Test from Rabbit Tests to Websites, https://www.early-
pregnancy-tests.com/history
7. Ibid. 8.
https://obgconnect.com/ curbside/2022/12/20/prom/ Premature Rupture of Membranes: Making the Diagnosis
9. NICE NG 35 Intrapartum Care, Evidence Review B
https://www.nice.org.uk/guidance/ng235/ evidence/b-initial-assessment-of-women- reporting-prelabour-rupture-of-membranes- pdf-13186672959
10. DeHaan HH, Offermans JPM, Smits F, Schouten HJA, Peelers LL. Value of the fern test to confirm or reject the diagnosis of ruptured membranes is modest in nonlaboring women presenting with nonspecific vaginal fluid loss. Am J Perinatol. 1994;11(1)
11. Olarinoye AO, Olaomo NO, Adesina KT, Ezeoke GG, Aboyeji AP. Comparative diagnosis of premature rupture of membrane by nitrazine
test, urea, and creatinine estimation. Int J Health Sci (Qassim). 2021 Nov-Dec;15(6):16-22. PMID: 34912184; PMCID: PMC8589831.
12. Preterm labour and birth NICE guideline [NG25] 13. Faron G, Balepa L, Parra J, Fils JF, Gucciardo L. The foetal fibronectin test: 25 years after its development, what is the evidence regarding its clinical utility? A systematic review and meta-analysis. J Matern Fetal Neonatal Med. 2020 Feb;33(3):493-523. doi: 10.1080/14767058.2018.1491031. Epub 2018 Sep 9. PMID: 29914277.
About the author
Martha Mackenzie is the Women’s Health Lead at BHR Biosynex, managing the company’s portfolio of women’s health products in the UK region, with a particular interest in pregnancy and birth. Martha has over 17 years of experience in medical technology and completed a Masters research project examining diabetes technology. In her honours year at Strathclyde University she focused on health economics, which included a research project on the economics of neonatal intensive care.
14. Watson, H.A., Seed, P.T., Carter, J., Hezelgrave, N.L., Kuhrt, K., Tribe, R.M. and Shennan, A.H. (2020), Development and validation of predictive models for QUiPP App v.2: tool for predicting preterm birth in asymptomatic high-risk women. Ultrasound Obstet Gynecol, 55: 348-356.
https://doi.org/10.1002/uog.20401
15. Chang Y, Li W, Shen Y, Li S, Chen X. Association between interleukin-6 and preterm birth: a meta-analysis. Ann Med. 2023;55(2):2284384. doi: 10.1080/07853890.2023.2284384. Epub 2023 Nov 27. PMID: 38010798; PMCID: PMC10836263.
Artificial intelligence to help increase maternity safety
A new ‘observatory’ system has been launched to help hospitals gain a better understanding of risks, outcomes and safety within maternity and neonatal services. The system is based on an AI-backed risk methodology, from C2-Ai which is already used in the NHS to measure safety and performance, highlight hidden risks in healthcare, and safely manage waiting lists. By shining new light on outcomes for mothers and babies, it will help healthcare providers to proactively identify and address areas of concern early within maternity services, before they escalate or become systemic problems. Frontline clinical teams will also be better informed about specific risks and care requirements for individual women, including any specialised support needed to ensure favourable outcomes. Dr. Mark Ratnarajah, a practising NHS paediatrician and UK managing director for C2-Ai, said: “Maternity services have come under close scrutiny in the public eye. By working closely with partners in the NHS, we will provide capabilities that can alert healthcare providers to challenges at the earliest of stages. And they will have new analysis to help them to demonstrate quality to regulators, maternity incentive schemes, and the outside world. Insights needed to achieve this
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can often be buried within data held in disparate places. We can now decode that complex clinical data, and unearth intelligence needed to support a learning environment. In addition to current evaluations of compliance with processes, services will have a new means to help them understand, interrogate, and enhance outcomes on an almost continuous basis.” Early adopters within the NHS are expected soon, with maternity teams in Trusts across several regions having already provided positive feedback on the observatory’s capabilities. The system works by calculating and comparing in-detail observed outcomes for women and babies, in relation to expected outcomes for those individuals. Tailored for the acuity level of each
maternity and neonatal service, the observatory uses AI and machine learning algorithms, widely proven in the NHS and internationally, to assess a total of 47 clinical factors. It takes into account case-mix adjusted maternal and neonatal clinical outcomes, impacts from social determinants of health such as ethnicity and deprivation, and comorbidities. Maternity services are then able to visualise in granular detail where they may need to focus attention. The same system then allows providers to track if policy changes and quality improvement measures put in place have led to improvements. Healthcare providers will be better equipped to identify patterns – for example the prevalence of sudden or unexpected increases in complications. They will be able to carry out deeper root cause investigations into adverse events. And they will be able to use insights to support accurate reporting on performance to regulators, and NHS Resolution’s Maternity Incentive Scheme. Analysis of community care, and maternity and neonatal outcomes, made possible through the Maternity & Neonatal Observatory, is also expected to support healthcare providers’ ability to measure progress in tackling inequalities.
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