Aaron Dees

Historically women+ have been significantly underrepresented with regards to medical research (Merone, Tsey, Darren, Nagle, 2022). This is seen in both policies relating to women+ health, research in clinical trials but also the presentation and management of conditions in clinical settings (Merone, Tsey, Darren, Nagle, 2022)( Maucais-Jarvis, Merz et al., 2020). For example in 1977 the Food and Drug Administration in the US released policy guidlines for clinical trials in General Considerations for the clinical Evaluation of Drugs which was based on data that excluded all ‘’Women of Childbearing Potential’’ from phase 1 clinical trials, regardless if they were on birth control, single or if their husband had a vasectomy (U.S. FDA, 1977). It wasn’t until 1993 that the FDA revised its publication from 1977 allowing women+ to be included in all stages of clinical trials if it met certain criteria (U.S. FDA, 1993). This was done as there was a “growing concern that the drug development process does not produce adequate information about the effects of drugs on women’’(U.S. FDA, 1993).

In addition to the exclusion of women+ from clinical trials and health data collection processes, the presentation and prevalence of health conditions can vary according to gender ( Maucais-Jarvis, Merz et al., 2020) but this is not always considered in clinical management of female patients. For example, clinical evidence shows that women+ are half as likely to receive interventional medicine for coronary artery disease when compared to their male counterparts (Weisz, Gusmano, Rodwin, 2004).

Although there is clear evidence of exclusion and bias in women+ healthcare, one must first know where the deficiencies and bias lie within particular conditions to be able to appropriately address them. Machine learning has the potential to make a vast impact on women+ centered health by analysing health related data of various conditions, identifing these key defiencies and biases and addressing these key deficiencies to develop a more appropriate approach to the management of these conditions.

Research Aim: The aim of this research is to explore the deficiencies in the management of women+’s health conditions from presentation to diagnosis and treatment across the lifecycle and investigate issues of bias and exclusion.

Objectives: The first steps in this research will involve a qualitative study to explore the key deficiencies in the management of women+’s health with key experts in the areas of health (GP’s and Pharmacists) and health related policy makers. The data collected will be analysed using thematic analysis (Braun and Clarke, 2006) and the results will then directly inform subsequent data driven explorations in particular health conditions where deficiencies and biases have been identified.

Data Exploration – Some of the main sources of data initially identified include:

• The Autoimmune Association {Charity/Research organisation}
• DAISy PCOS {Research organisation}
• SWAN datasets {Open Source Data }

Statistical Analysis of data from stage 2 to determine critical factors that have the largest impact for health outcomes for women+