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Photo: fizkes, Shutterstock

Photo: fizkes, Shutterstock

Data science shifting abortion healthcare

6 March 2019

The abortion care experience for Australian women could be revolutionised using cutting-edge research methods developed by a leading ANU epidemiologist and data scientist.

College of Business and Economics Associate Professor Bruce Shadbolt, is developing the models as a part of a collaborative project between Marie Stopes Australia and ANU, entitled “What Women Want in Abortion Care.”

The project aims to develop client-centred models of abortion care that are based specifically on the personalised feedback of women to deliver a high quality service for what is often a stigmatised area of healthcare.  

Associate Professor Shadbolt uses a response adaptive, statistical learning approach focused on a unique method of research which he terms, “human-research-machine-learning.”

The approach enables researchers to include a host of variables into predictive care algorithms that extend beyond the traditional epidemiological variables. These variables can include psychosocial issues such as reproductive coercion and trauma, presence of support teams and networks, education and health literacy, healthcare professional compassion fatigues, legal status of abortion, costs and general abortion access issues.

 “We know that just having a predictive model of treatment and care pathways isn’t enough,” says Associate Professor Shadbolt.

“We wanted to add a research aspect into our model which included other dimensions that influence clients’ experiences and outcomes.  To do this, we needed to be able to describe the landscape, a map of the dimensions involved and experienced by the patient herself as well as how she interacts with her environment.”

A community-built model

The first step was to establish focus groups which identified the main themes that influence the experience of a woman seeking care before, during and after an abortion procedure. 

“We did something a little different to a standard approach, in that we talked to clients, clinicians and the community to gather the diversity of themes to bring down into the model,” says Associate Professor Shadbolt.

These themes are being used to inform the creation of algorithms and models that can deal with the many dynamic context-based adaptive response ‘signatures’ women face when looking for abortion care.

“We take the real-time research responses from individual women and apply them to the next person who comes along with similar context ‘signatures’, and from there we start to build up a real-time learning model striving to maximise the best experiences and outcomes for clients. That’s the machine-research-learning aspect.

“The algorithm gets better about how it transfers knowledge to clients, support networks, health professionals and management. The unique learning is embedded in the process so it becomes organic.”

Research with impact

Findings from the initial research have already started to inform changes to the clinical practices at Marie Stopes Australia.

Associate Professor Shadbolt is now in the process of transforming the qualitative research into numeric models. Once completed, these models will be taken into the field with Marie Stopes Australia clinics and GPs. Baseline data gathered from the models will inform the development of technology that will cater to client’s needs in abortion care.

The research has garnered world-wide interest, with organisations in New York, Amsterdam, the UK, Japan, and Australia registering their interest to engage in further studies.

Associate Professor Shadbolt has also identified that many additional areas in health care will benefit from this ground-breaking research approach in the future.

 

 

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Updated:   6 March 2019 / Responsible Officer:  CBE Communications and Outreach / Page Contact:  College Web Team