Globally, infertility rates are increasing, and birth rates are declining, because of concomitant socio-economic, physiological and environmental factors. When women present for infertility care in their mid- or late 30s there are clinical challenges for MAR providers, given that treatment protocols (and expectations) are typically established in populations of normoresponsive women aged ≤35 years. How do clinicians set realistic expectations and plan the optimal treatment course for women of advanced maternal age (AMA) who do not fit the profile of normoresponsive women? Understanding both global and local trends in fertility populations is important; treatment improvements centre on the relevance of statistical KPIs, but acknowledge that individual experiences of MAR will be personal and unique. The primary learning points from this webinar are for health professionals to understand how MAR populations are changing (globally and locally), and how and why approaches to treatment need to adapt to these changes. Such understanding affects treatment and procedural choices in MAR: breaking the data down into what matters most to the patient is extremely relevant to good clinical practice.

KPIs benchmark clinical practice at local, national and regional levels, especially in the era of ‘big data’ and information sharing. To be widely adopted in clinical settings, KPI must be measurable, reproducible and consistent, and look beyond live birth rates or even cumulative live birth rates:. However, MAR and AI or big data technologies are developing so rapidly that relevant clinical benchmarks, expectations, definitions and processes can be unclear in everyday practice. Indeed, information sharing in healthcare is behind the curve, compared with other sectors. Although some reasons for this are valid (relating to concerns about data protection, information security and sensitivity), data to be obtained for registries, for example, need to be sufficiently granular to drive powerful insights without compromising patient integrity or routine clinical practice (where not all data are obtained, cleaned and stored consistently).KPI should be defined for each treatment step and MAR milestone, to reduce bias as much as possible.

Certainly, traditional reliance on randomized study data is evolving andreal-world outcomes are often highly regarded in MAR.Exploring the value of real-world data is key for future clinical decision making.