Blurring faces to add clarity to healthcare research

Healthcare is a very personal subject and extremely emotive, especially for those suffering from serious illnesses, it can be tough to talk about. Research is used to uncover the hard truths that come with suffering from a serious illness, but there is the challenge of how to bring the patients to life, as they as so much more than a statistic.

Video has long been used within qualitative research for healthcare, to record sessions such as in-depth-interviews (IDIs) and Focus groups. However, many will have experienced the pain of working with this type of video. The requirement to watch and re-watch the content, while manually tagging, coding and matching notes!

Thanks to advances in technology, this process has been streamlined. Content can easily to ingested into video intelligence platforms such as LivingLens, allowing content to be easily managed and curated. Content is quickly transcribed, with a choice between machine speech recognition and professional transcribers, turning content into a fully searchable, time stamped resource. This enables you to search for key words and jump straight to the moment of interest in the video or to explore the themes and topics being discussed using the power of text analytics.

The popularity of video and ease of self-recording using a mobile device is also having an impact. Patients, carers and physicians can record themselves from the comfort of their own home or office, helping to provide a relaxed environment which facilitates an openness hard to achieve in a more formal setting.

 As well as the practical advantages of using a video intelligence platform, there are also other significant benefits being seen by those specialising in healthcare research:

  • Patients are put as ease through being in their own home, they are more relaxed and open – it delivers a more intimate way of managing pharmaceutical market research
  • Despite not interacting with patients face to face, video allows you to see patients in person and get to know them as individuals
  • Self-recorded video can provide additional context, you get an insight into a patient’s home, environment and surroundings
  • Through video not only do we gain insight into what people are saying, but also their tone of voice and facial expressions, we can understand their emotions

Our faces can reveal so much about how we are feeling. What we say is important, but when combined with our facial expressions we get a deeper level of understanding. It’s a process we naturally do when speaking to someone and that’s why video is the next best thing to meeting a someone face to face.

So, why would we want to blur faces?

Video is a powerful medium for storytelling, LivingLens enables you to quickly identify the moments that matter and build showreels to bring your insight and patients to life. However, due to privacy, anonymity and permission challenges, it’s not always an option to share the video responses with stakeholders, after all, videos which feature faces are classed as personally identifiable information.

 Facial blurring opens up the opportunity to share video feedback – for agencies this means the ability to share relevant content with end clients and for brands this could mean sharing the content more widely within the business. It’s an ideal option for when:

  • You’re unable to seek consent from participants to share their content, due to not being able to reveal the brand name
  • When a respondent would like to provide video feedback, but would prefer to not be visible to a wider audience

By blurring faces post-collection, you still benefit from the full range of capabilities available to extract data from your content, including facial emotional recognition. You are fully in control of what content is blurred and subsequently shared. For the end viewer, the blurred face video helps to bring the participant to life, it still creates an emotional connection that a written quote can’t.

How does it work?

Our facial blurring uses automatic machine technology to detect faces within video and image content. The faces detected are tracked within the video, so that each individual face that is detected can be blurred from all angles – clever stuff! 

However, it is still a machine, so 100% accuracy is not guaranteed. If the content is of low quality, or has many faces moving, the machine will find it more challenging to accurately detect and blur them. This is why we have also included the option of blurring the whole video/image when needed.

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