Data in healthcare: time for action
With a device in our pocket or strapped around our wrist, the Quantifiable Human has become reality. We’re sitting on a huge pile of valuable data, but all too often we lose sight of the data’s real worth and what it can mean for users. Time to make data actionable.
We live in an age where data is abundantly available and accessible as never before. We have more services and devices that generate data around us in the blink of an eye, yet there remains a big elephant in the room: all too often, we don't know how to look at data correctly and fail to process what it means for users.
Drowning in data
Graphs, charts and numbers. Everyone who hasn’t been living in a cave has grown so much more accustomed to a truckload of data since the Covid pandemic began. But that doesn’t mean this has influenced our behaviour significantly. We have seen first hand - and on an unprecedented scale - that too much data risks drowning out the message. This is not a pandemic-specific issue, but rather Covid has highlighted a bigger problem: it’s no longer the question of how to collect the data, but more than ever about what to do with data.
The quantified self 2.0
As well as public data about a new virus, we have seen the advent of handheld devices capable of detailed health analysis. The life science industry is working hard on the miniaturisation of various electronic diagnostic tests for viruses, cholesterol, glucose, and STDs etc. And health apps, FitBits or smartwatches are here to stay. Numerous apps and small hardware devices have been popping up, aiming to make a user’s life easier or even save a trip to the doctor.
Tracking your life is no longer a conscious choice. The massive data footprint this leaves behind makes gaining any valuable insights a challenge.
Whereas earlier, we needed to actively choose to track our data, now the device in your pocket or on your wrist is so advanced and packed with sensors that much of the tracking is done automatically. Tracking your life is no longer a conscious choice. The massive data footprint this leaves behind makes gaining any valuable insights a challenge. The signal-to-noise ratio becomes substandard and so we look at algorithms to tell us what is important and noteworthy. And therein lies the biggest weakness: the reliance on opaque mechanisms that interpret data and make decisions for us.
Designing data-rich applications
How should we deal with the abundance of data? From our own experience in building data-rich applications, we’ve put together a few pointers on how to design an effective interface around numbers and insights that offer clarity and real value for users.
1. Focus on the most important data first
People are never 100% focused when they open your app. Try to pick one meaningful statistic that’s immediately relevant to your user and build your interface around that. Users who want more can dig for detailed data on a deeper level.
2. Give context
Numbers by themselves hardly mean anything if you can’t compare them. Always show a frame of reference around your number. Using averages over time intervals is a popular tactic to communicate how you’re doing. Think: “How far along am I on my daily goals?”, “Is today a good day or an average one?” or “Am I still improving?”
3. Translate data into actionable insights
Numbers can give you the naked truth, but they fail to convey anything meaningful without context. Use words to describe the current states and suggest the next action to take. For example: when showing a positive virus test result, suggest going to the doctor to discuss treatment.
4. Communicate insights with visuals, numbers and words
Make sure your message is accessible to all. Combine numbers, text, colours and graphs. Colour coding can make values instantly readable and graphs are ideal for comparing with averages.
5. Protect the privacy of your user
When dealing with sensitive medical data, don’t assume the user’s device is always used in a private setting. Try to put as little private information as possible on your app’s launch screen.
Make data great again
The real goal is, of course, changing human behaviour. We need to understand that the human brain is inherently lazy and runs on auto-pilot when confronted with familiar situations. The behavioural scientist BJ Fogg has created a model of three factors that influence behaviour change: motivation (the willingness to change), ability (time, money, energy) and an external trigger. We should use this to understand how the right data insights can trigger behavioural change.
To transform data into triggers, we need to process it into personal, contextual and actionable chunks. Taking into account the person the data is presented to and tuning it to their preferences can be the difference between ignoring an insight and being motivated by it to change your patterns.
Long-lasting behaviour change doesn’t happen overnight of course. The Fogg model teaches us that there are different ways of changing behaviour. From starting a new behaviour, all the way to stopping unwanted behaviour. Whatever the pattern, change starts with taking small steps; reminding someone to take a run at a time when you typically see very low movement is a good example.
Once you succeed in triggering users to implement these one-time changes, you can focus on creating patterns of behaviour, such as going running every day for a month. And that’s the main goal: to implement lasting change in a person’s behaviour and to offer a truly meaningful experience. To make data actionable.
