Trust is at the core of every relationship. Relationship between a user and a service is no exception. With the growth of technologies and AI, building trustworthy products is a key to making people want to use them. When it comes to healthtech, transparency becomes even more vital as we share our medical records and sensitive data. 🧑⚕️
So, how can we build products and services that help people feel safe and respected?
Trust is at the core of every relationship. Relationship between a user and a service is no exception. With the growth of technologies and AI, building trustworthy products is a key to making people want to use them. When it comes to healthtech, transparency becomes even more vital as we share our medical records and sensitive data. 🧑⚕️
So, how can we build products and services that help people feel safe and respected?
One of our speakers at UX & Product Design Week – Projects by If – are sharing their approach to creating trustworthy healthcare experience they used in the project for DeepMind Health's Streams. Check out our favourite insights below.👇
It's not enough to just share numbers and stats. We need to think about what patients, doctors, and others really want to know. This helps build trust and keeps AI systems accountable. For example, instead of just saying "We processed 10,000 patient records," explain how this helped diagnose conditions earlier or reduced wait times.
Giving too much info at once can be overwhelming. It's better to let people choose how much they want to know. Start with the basics, and let them dig deeper if they're interested. For instance, a patient app might show a simple summary of how their data was used, with an option to see more detailed logs if they want.
It's important to clearly show how AI is making healthcare better. For example, adding info about how well the AI worked to documents patients already know, like discharge letters, can help people understand its value.
It's crucial to have strong security measures. The Streams project used three different ways to check if someone should have access: fingerprints, passwords, and special cards. This three-factor authentication helps make sure only the right people can see sensitive information.
There are ways to make sure data is being used correctly. By using special tech called verifiable data structures, we can show people exactly how their information is being used. For example, this could allow patients to see a trustworthy record of every doctor who accessed their file and why.
Healthcare AI should work smoothly with existing systems. Using open standards helps different systems talk to each other, making everything work better while still keeping data safe. For instance, using the FHIR standard allows an AI system to safely gather information from different hospitals a patient has visited, giving a more complete picture of their health.
By mapping out difficult journeys a patient might face and seeing how they interact with doctors and health data managers, we can spot big risks and create solutions for them. For example, some scenarios could help you design better ways to handle data securely in urgent situations.
It's vital to keep chatting with patients, doctors, policymakers, and others throughout the whole process. This helps spot potential problems, address worries, and build trust in the AI system. For example, regular focus groups with patients could help identify concerns about data usage that developers might not have considered.
By following these ideas, companies making healthcare AI can create systems that not only help patients get better care but also make everyone feel more comfortable about using them. The Streams project showed that when done right, AI in healthcare can save lives and still keep people's trust.
For more detailed insights, refer to the original article by Projects by IF.
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