Intimate entanglements: big data in clinical trials
This project was completed during my studies at the Glasgow School of Art, working alongside the Institute of Cancer Sciences at Glasgow University to produce a vision of the future based on current trends relating to Precision Medicine (PM) and cancer treatment.
The focus of this project was whether a qualitative analysis of a patient’s lifestyle and environment, gathered by technological devices, could/should have a role to play in the delivery of precision medicine. Personal data is captured constantly and passively, becoming increasingly detailed and intimate as technological products dematerialise, making them more available to corporal integration. The shift towards wearable technologies that are able to gather data constantly regarding our physical state; for example our heart rate, body temperature and precise location - carries great opportunities for healthcare, but with this comes an array of ethical implications.
Diagram: how might AI impact clinical trials?
Prototyping data collection devices
People respond differently to the same treatments; depending on factors such as sex, age, race, ethnicity, lifestyle, and genetic background. As clinical trails form the basis of evidence from which the safety and efficacy of new medicines and treatments can be evaluated, it is crucially important that they represent a diverse cross-section of society. However, this is not the case, and there are many barriers to diversity in trials, facing both patients and clinicians.
Trials are complex, with long lists of inclusions and exclusions for eligibility, depending largely on the patient’s medical history, but also taking into account lifestyle and environment factors such as whether the patient smokes, their weight and their level of literacy. Furthermore, it is difficult for clinicians to be aware of all currently active trials and the individual stages these trials are in, at all times.
TrialSeek is a service which gathers and analyses an individual's lifestyle and environment data in order to match them with a suitable trial.