How we're is designing for
the future of pharmaceutical labelling.
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When was the last time you had a paracetamol?
Would you say that you trust what’s on the label? Do you think it’s up-to-date and accurate?
Some pharmaceutical labels constantly require updates and changes due to clinical trials and reports of adverse effects. Pharma companies deal with 1000s of changes to 1000s of products in multiple countries across the globe.
With so much complexity and volume, it sometimes takes years for these updates to come into effect on the label. Pharma Labelling involves a long and complicated process that's open to error. And the people involved are struggling.
We knew that there had to be another way. And so, this project was launched to find a better, smarter approach to pharmaceutical labelling. One that brings us closer to providing information that’s 100% up-to-date and accurate, at all times.
1. We observed
Design Research allowed us to deep dive into the current labelling processes and recognize the tensions between people and the systems they use today. This allowed us to identify three major themes:
Ways of Working
on average, 6 out of 10 people mention this theme.
Includes pain-points around process, responsibilities, day-to-day activities, tracking, and monitoring of changes.
on average, 3 out of 10 people mention this theme.
Includes pain-points around training, knowledge, and collaboration.
on average, 1 out of 10 people mention this theme.
Includes pain-points around usability, readiness of tools and integration with third-parties.
2. We learned
Our research led us to three core themes that have influenced the design. These themes represent the essence of the system we want to create, and what we want to achieve.
"With the current system, it’s almost impossible to know what the hell is going on."
As it stands people are working in silos, and are unclear about who's doing what. They feel over-whelmed by the complexity and chaotic nature of it all. People need transparency around all aspects of the process: what's happening, what stage they're at, what their responsibilities are.
"It would be great if the labelling team would know regulatory procedures and requirements. It is not to just prepare some text but also to be in-line with guidelines and requirements."
With current ways of working, people frequently side-step existing tools and processes because they don't meet their needs. Our goal is to build people's trust in their peers, processes and tools - as the foundation to better ways of working.
"My expert view (reviewing documents) comes from experience, ‘common sense’ and feeling. I have a scientific background, but no specific training."
We found that many people lack the training and knowledge needed to feel comfortable in their roles. They're unsure of who did what, and when. Many want to live in the shadows, unsure that they have the right answers. We want to inspire people's confidence in their knowledge of the process, tools and regulations.
3. We built
Our solution is based on data, not documents. But it still looks familiar to our users. We've focused our intention in making sure that whatever new feature we've added, wouldn't create any burden on people using it every day.
All made were backed by the findings from the Design Research. From the UX to the Analytics model.
3.1 Design Principles
Our design themes represent what we want to achieve with the system we are creating. Our design principles influence how we will get there.
We consolidate everything you need to achieve your goal, in one particular moment in time. You'll only see information that's relevant to the task that you're working on, and all unnecessary clutter is stripped away. The mechanics of the system in the background might be complex, but the users don't need to feel or see that. And while the interface is clean and sparse, people still have access to the full stack of information if they need it.
Focus on humans, relationships between workers, collaborations. Decisions are constantly being made by humans and machines. We make the reasons for those decisions transparent, so that people can learn from them and improve their knowledge day after day. So people can feel more confident in themselves and more trusting of the system.
The smartest systems adapt to you, instead of imposing new ways of working. We leverage all the data we have available to bring you the information you need. The system understands who you are, and what you need. It fits into the way you work, rather than forcing you to comply with a rigid, unyielding structure. At the same time, we try to encourage good habits, by nudging people in the right direction.
3.2 Design + AI
With this project, we've taken advantage of some technologies to make sure the pain-points we identified were addressed in an efficient manner.
To do so, we created an AI engine that converts documents into a maintainable database. This database becomes our single source of truth and it allows for transparency of process.
With analytics and natural language processing, we're also able to work with that data, and automatically create brand new pieces of text to help authors make better decisions.
Design made sure that only the relevant technology would be made visible and used. So that, both the users and the machine could get better along the way. Working with data requires specific steps, but we are integrating new requirements into recongizable forms.