Principal Investigator

The principal investigator oversees every aspect of the study, including data and sample acquisition and storage, research outcomes, and funding.

According to Airwave’s principal investigator, a cohort is ‘grown’ over time, and it needs certain conditions to thrive. These conditions include the participants’ trust in the study (trust ensures they will consent to their data and biosamples being used for research); the value the study offers a participant (such as free health screening); and finally, regular funding. For a long-term cohort study, it’s vital to continually acquire funding so that data and samples can be regularly collected from the cohort. The more data collected the more valuable the study becomes, as more data inherently offers more scope for research insight.

After this interview with the principal investigator, I began to see the tending of a cohort study akin to the tending of a garden, and so used metaphors of growth and gardening to inform my artwork. The cohort point ‘cloud’ (in various shades of blue to allude to the traditional dark blue of police uniforms) grows over time from the ‘pile’ of trust, value and funding down below. All participant data points enter the cohort point cloud through a ‘window of consent’: without consenting to their samples and data being collected, the participants can’t join the study.

Nurse

When interviewing a nurse who has worked in study clinics, they said in their role their main focus is to ensure that they collect all data and biosamples in line with the study’s requirements. While doing this, they work to keep the participant comfortable by building a rapport with them through conversation and also being personable and friendly. Nurse/patient rapport is important in part because good conversation helps distract from possibly uncomfortable aspects of the screening (such as drawing blood) and helps put nervous participants at ease. Also, if the participant has a positive experience, they are more likely to take part in future studies, which is why having personable front-facing staff is an important part of any large study.

Through this rapport, a nurse will naturally end up discovering information about the participant they screen that while it may be interesting, isn’t information that is being collected by the study and so is lost once their shared interaction ends.

The screening is one of the few times within the Airwave System when the participant is ‘seen’ and interacted with directly as opposed through their samples or data, so in my artwork I chose to focus on representing the back-and-forth rapport and conversation that a nurse has with a participant in the clinic setting. Here, blue data points are being collected from the participant by the nurse through the back-and-forth rhythm of rapport, as represented by the arrows. The multi-coloured data points bouncing away are the known data points that aren’t collected by the study, and allude to the visualisations of spiralling, bouncing trails of particles used in quantum physics.

Participant

Participants (of which I interviewed six) give their time, information and biosamples to this study for several reasons. Many take part to ‘help out’ and advance research. Many also take part due to curiosity: they are interested in the research and end up taking part in multiple studies because of this. Finally, the free health screening that is part of Airwave is also an incentive for participating because similar commercial health screenings can be costly.

One participant likened their individual data in the study to being ‘a drop in the ocean’ that is the Airwave cohort:  they don’t know what research will come from their data and are aware that many studies might ‘come to nothing’ and not have much impact.

However, they know that studies need to be replenished with willing participants in order to advance medical progress and are happy to offer their time for this on the off-chance a groundbreaking ‘once in a blue moon’ study will arise from their contribution.

I made this artwork from the view of the participant during the screening process, as though they are sitting within the three-dimensional space of the clinic. Looking down at themselves, they see the multicoloured trails of data points collected by the study travelling through the ‘window of consent’ and dropping into the blue cohort ‘ocean’. Far away on the horizon, researchers are analysing the cohort data, and the reason for this distance is that when a participant is taking part in a screening, this future research is not at the front of their minds.

Clinic Logistics Manager

The clinic logistic manager supports the study logistics manager within the study clinic. They ensure that the setup of clinics in new locations across the country is efficient and effective. They will visit clinic sites and train nurses according to the study protocol and – through audits and rescreens – ensure that the data and biosamples are being collected in a consistent way. 

From their perspective, participants are always referred to as ‘the cohort’, even if when working in the clinic they may have built rapport with some of the individual participants they have met. By ensuring a clinic is running smoothly, they are always working towards one goal: to achieve a certain number of participants in the cohort (or at this later stage of the study, aiming to re-screen a specific percentage of original participants for a second time). 

In order to distill the insights of this interview into an artwork, I chose to depict the process of data collection in the clinic and the data’s transfer to the cohort, slowly reaching a ‘goal line’ of re-screens that the Clinic Logistics Manager is always working towards. Using similar iconography to represent the study clinic as other artworks, uncollected data points acquired from rapport with participants (the manager will sometimes meet the participants face-to-face) are represented as bouncing particles that aren’t formally collected. 

Study Logistics Manager

The study logistics manager oversees the smooth operation of the entire screening and data collection process within Airwave. This includes coordinating the logistics for everything from the participant’s initial screening invitation to the actual screening in the clinic. For them, since the participant-facing part of the Airwave system is so people-focused, having good interpersonal skills is vital to getting their job done.

The study logistics manager also said that in large studies like these participants can be described as a ‘resource’ and become numbers. For them, however, participants are not a ‘resource,’ but vibrant, multifaceted people, and treating the participants with empathy is important. This focus on empathy and rapport ensures that the participants have a positive experience when taking part in the study, which will make them more likely to engage with future studies.

This is the only artwork where the usually grey point cloud representing the participants is instead multicoloured. This is to represent the Study logistics manager’s perception of the participants as vibrant and more than a resource, more than numbers. As the study logistics manager is often in contact with participants within the clinic, the visual motif of conversational data points that are not collected by the study bouncing away is used here as well.

Lab Technicians

The perspective of the lab technician in a cohort study was taken from interviews with a group of lab technicians working as part of Imperial College Healthcare Tissue Bank (however, samples within the Airwave study are processed by technicians working at Charing Cross Pathology Laboratory).

The lab technicians said their focus is on preparing and preserving the biosamples they receive. Biosamples are seen as a precious resource that is challenging to acquire, and so the technicians want to make sure that they do all they can to minimise mistakes and ensure that every sample is treated with care.

Because of this, a lab technician doesn’t spend much time thinking or wondering about the individual that a particular biosample has come from; they ‘set the individual aside’ and focus on the task at hand to ensure the biosamples are perfectly prepared and preserved.

For this piece, my goal was to represent the focused vision of the lab technician through the creation of a tunnelling space that obscures everything but the samples. The work also shows the trail the samples take from the study clinic to the laboratory to represent the connection back to the study clinic but these trails as represented by dotted lines to highlight that these fall behind and are obscured through this focused gaze.

Database Manager

The database manager is the ‘data gatekeeper.’ They make sure that the study database is well-maintained and organised and that it can be updated with any new participant data. The manager also makes sure that participant data is without any errors and organised and prepared in such a way that makes it easy for researchers to use in their research.

Far removed from the study clinic and face-to-face contact with study participants, the database manager is the last major connection between the named individual and their data and biosamples.

To ensure that participants are unable to be identified, once participants are entered into the study they are identified only by a 7-digit number. Airwave database managers are the only people who hold the ‘key’ to re-link the names of the participants to their data.

A manager might access records by name if a participant would like to have their data deleted from the study, or when records need to be updated. Examples of these updates include reading death certificates to enter the date and cause of death into the database, or checks to ensure data is correctly matched to the proper participant.

This direct connection to the real individual represented in the data offers database managers a unique study vantage point: the administrator might interact with a named individual with the knowledge of the future health ‘stories’ their data holds, yet through the updating of individual records, they will also glimpse more of the individual than a researcher who looks at the same information reduced to ‘codes’ ever will. (As an example, updating a participant’s cause of death involves reading through a detailed death certificate, but much of this detailed information discovered by the manager will not be added to the database). However, of the thousands of participants within the study database, the manager said they will only look at a small percentage of these individually.

Within the work, I chose to highlight the data manager’s ‘gatekeeper’ role through representing them as the central connector between the participant’s life and the database.

Researcher

A researcher applies to access specific parts of the Airwaves dataset, and once their research is approved, they access the data using the Dementias portal. They are not physically situated in the clinical setting and each participant’s data is pseudonymised under the 7-digit participant ID.

Through this pseudonymisation and the fact that researchers will often work with hundreds of thousands of data records during their analysis, the chance that a participant could ever theoretically be identified by a researcher is very small. Researchers are not interested in the individual participant; they ‘stay far away’ from the participant when undertaking their research.

However, while far removed from the participant, a researcher is also in many ways very near to them, as the data offers insights into a participant’s health in ways that they wouldn’t be able to discover on their own. The cohort is typically represented as aggregated data, and an individual’s data might only be noticed in aggregate form if it is an outlier (a data point that is very different to other values within a dataset). Outliers are common and can result from errors in the measuring and reporting process. Through using standard data handling procedures, these outlying data points will be transformed in order to ‘clean’ and ‘wrangle’ the data into a form ready for statistical analysis, though the researcher will not look at a individual participant’s data as part of this process.

I chose to represent the cohort using more standard representations of data (a 3D scatterplot) to highlight the ‘data is data’ viewpoint of the researcher. Here, the issue of an outlying individual within the dataset is resolved through ‘trimming’ the outlier down to size (representing the data ‘wrangling’ process) and setting it to one side. I was drawn to using a blunt approach to represent the removal of an outlier to reinforce how for the researcher the individual data point is secondary to the ‘cleanliness’ of the aggregated cohort dataset.