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Designing for AI - A strategic approach to innovate the patient journey
- 1. Designing for Artificial Intelligence A strategic approach to innovate the patient journey
- 3. LEO Innovation Lab Improving the lives of people living with chronic skin conditions
- 4. Empower people to stay on top of their skin condition
- 7. OMG YOU SAVED MY LIFE!
- 10. IMPLEMENT EMPATHY DELEGATE COMPLEXITY SCORE WITH ASSIST IDENTIFYING THE AI OPPORTUNITY SPACE
- 11. DELEGATE COMPLEXITY Let AI do the heavy lifting on tasks that are either complex, or repetitive and mundane. BEST PRACTICE Identify the tough tasks that people don’t have a process for and start with those.
- 13. IMPLEMENT EMPATHY Like a caring parent, proactively solve for users’ true intents. Try asking, what would my parents do? BEST PRACTICE Develop strong hypotheses on what users intent may be. Assess the best way to help them, whether it is giving them information, recommendation, or taking action on their behalf.
- 16. SCORE WITH ASSIST Look for “assist opportunities” to amplify the experiential outcome. BEST PRACTICE Consider the capabilities and limitations of your main device. Consider the strength of other devices.
- 18. IMPLEMENT EMPATHY DELEGATE COMPLEXITY SCORE WITH ASSIST IDENTIFYING THE AI OPPORTUNITY SPACE
- 20. A strategic approach to innovate the patient journey
- 21. A strategic approach to innovate the patient journey
- 22. The future of the patient journey
- 25. Demo
Editor's Notes
- RIE: Senior Designer at LEO innovation Lab - where I try to ensure that we deliver great experience to our end users while using new technologies such as AI. When given the opportunity, I talk about the importance of designers getting more involved in both the debate and development of products applying new technologies - which is also the reason, I am here today. MELANIE: Senior Product Manager at Leo Innovation Lab where I try to ensure we best service the users while making sure we do it in a sustainable way. I will present one of the very exciting projects we are working on.
- What is Leo Innovation Lab? Leo Innovation Lab is an Innovation Lab created 3 years ago by Leo Pharma, a danish pharma company. Our mission at Leo Innovation Lab is to help people living with a chronic skin condition without using medicine but tech and especially AI. And one of the biggest issue for people living with a skin condition and that we are trying to solve is the fact that there is simply not enough healthcare professional in the world to help the millions of people living with a chronic skin condition.
- One of the project I will speak more about that I am product managing is Imagine. The goal of our work with Imagine is to help patients get the most positive interaction with the healthcare system as possible. This means getting to the right diagnosis as fast as possible and find the right combination of treatment and lifestyle changes as fast as possible. And we need AI to help us do that. But I’ll talk more about that later.
- Now that we did a quick introduction, let’s get talking about the reason why you are here. First, I’m going to present a framework to designing for AI - for everyone to use, even if you don’t know anything about AI Then, Melanie is going to tell about Images’ first attempt to use AI to provide a better experience for people living with a skin condition. And give you a couple of examples.
- So, I first started wondering about the user experiences that AI-driven products deliver after asking myself this a lot. And at the same time...
- I would rarely ask myself this…. I saw this big gap and I started to wonder, what’s the big difference, between these two moments?? Why do some AI-experiences feel awesome and why do some feel not so awesome?
- Then in 2016 at SXSW, I met these cool guys, who made this matrix for AI-powered user experiences and they talked about adopting a strategic approach when developing products based on AI. Based on these thoughts, I started to work with and further develop the framework for designing for AI.
- So, what this framework do (or attend to do) is helping designers, or others designing for AI, hitting that upper right corner.
- In other words, what I want to share with you today, is a framework that helps you identify the opportunity space for AI. Or answer the question of where and how AI makes sense in your user journey? Let’s look at some key pointers for identifying the AI opportunity space. And this is of cause only an overall framework to get you started on designing for AI. Okay, three of the most important things to consider when designing for AI is: (1) delegate complexity, (2) Implement empathy, and (3) score with assist. It is not jeopardy categories, but opportunity areas. And I want to show you some examples of how they can be activated when designing.
- Since AI is particularly good at delegating complexity, a really good place to look when designing for AI is where users feel overwhelmed by the task or experience particularly difficult job-to-be-done that they do not have a process for. Fx when people do not know how to get started - that is what you should design for. Fx I know how to order pizza. I do not need help for that, instead I want designers to help me accomplish more complicated things. Who is doing this really well?
- An example is AirBNB. Instead for leaving first-time hosts in the dark with regards to pricing, AirBNB gathers information from more than 72 data points from different sources and boil it down into one very simple and cool feature called smart pricing. This way they relief hosts from a task that normally would be an emotional investment and computational super difficult.
- Next up, implement empathy. Here I wanted to place a picture of my mother, but she wasn’t really fond of that idea, so I choose the lego minifigure instead. Anyway, do you know this kind of parent who always goes; Did you remember to do this? Do not forget to do that? Even though it can be annoying, it always comes out of caring and it feels good when you actually think about it. Let me specify this a bit...
- Left is what the weather report look like some weeks ago in Copenhagen. Rain. 40% sometimes 80%? If I ask my mother the same question, “what the weather is going to be like”, there is no way, she would let me leave without an umbrella. And what is it that mothers understand? They are simply really good at understanding that there is an overall intent with our question or actions. The thing is, we are constantly asking machine questions, and we are getting responses. The challenge for a designer is to move beyond simply giving smart answers and thinking about the end state that users are trying to get to. So, ask yourself: “what information or recommendation can you give” or “what actions can you take on behalf of users” with the AI that will take them the whole way.
- An example of this is “DoNotPay”, which most of you probably heard about. DoNotPay is a chatbot for people who gets a lot of parking tickets. The bot asks questions that are easy for people to answer, and in the end it produces and sends an appeal on behalf of person using it. (Last time I checked, it has won over 3 mio dollars worth of appeal) What this service do is taking people by their hand and leading them all the way. The same thing a caring mother would do.
- Last up, score with assist. Throughout the years got forced to watch a lot of soccer, and even though I find it kinda boring, I learned the notion of the assist: When you have good hand offs, you are way more likely to score. This is of cause a metaphor, but the same holds true for AI. Each device you are designing for has its own strengths and and weaknesses. Rather trying to force fit everything into one device, pause for a moment and consider the capabilities and limitations of you device. Then consider strengths and weaknesses of other devices. Where are you better off handing of the experience to others.
- Google does this really well - Alexa too. I only have one example for this one for the purposes of time, but a simple e.g is when I am cooking in the kitchen and I realise, we have run out of milk. It is a really convenient solution to say: “ OK Google, put milk on my shopping list” and Google does that. But if I want to recall my shopping list, Google without a visual interface is not the best place to do it. So, in terms of convenience, Google passes it on to my phone, so I can just pull out the list when I am shopping.
- Okay, we went through three types of opportunity areas and I hope, they make some kind of sense to you. Now Melanie is going to talk about how you can use these in relation to a user journey in order to design strategically for AI.
- Thx Rie ;) Now I want to tell you about a real world example of this framework Rie presented and apply it to a concrete user journey, the journey of people living with a chronic skin condition. At Leo Innovation Lab, we spend a lot of time thinking about how we, as patients, interact with the healthcare system and how to innovate this patient journey.
- The reason is that the current journey is far from ideal. If you were to have a skin condition, this is how traditionally, without tech, you were to move though the patient journey. They are a lot of pain points and in some countries, such a journey can lead to up to 2 years before you find the right treatment. But because we talk AI and not tech in general I would like to concentrate on 2 where we think AI can play a major role bearing in mind there isn’t enough HCPs for the share of people out there living with a skin condition. The first one is this one here is about going to your GP to get you first diagnose. This is a very very important step. It is especially important because not be able to get the right diagnose gets you on the wrong path that will oblige you to get into the long waiting lines most of the dermatological praxis. (it took me 6 month in Denmark to get an appointment to a derm for my rare for of eczema). The second one is finding the right personalised combination of lifestyle changes & treatment. What works for one doesn’t work for someone else and it is hard work to document your skin changes to understand your very personal combination of triggers. Now you are asking yourself what’s the link with AI and the framework presented.
- Well, using the framework Rie presented, we can look strategically at addressing this pain points using AI and especially the two I mentioned, diagnosing and finding the right combination of lifestyle changes and treatment. Diagnosing. It is an extremely complex problem where AI can do the heavy lifting and assist GPs & Derms but also anyone regardless of location and income with a skin condition to quickly get the right diagnose. The 2nd one is for patients to find & access their personal very best treatment. There we can score with assist providing patients with a their very personal skin coach in the shape of an app.
- And that is how the future of the patient journey looks like when we let AI do the heavy lifting of diagnosing and fully assist patients living with a skin condition to find their personal triggers & helpers. We can create a shorter journey to the end-goal: helping patient living with a chronic skin condition. That is what Imagine is about.
- Now of course I would like to show you how concretely we implemented AI to help patients living with a skin condition. The app does this 2 things: It assists people living with psoriasis, eczema, rosacea and acne to take consistent pictures of affected areas to monitor their skin between consultations and helps doctors getting to a more accurate diagnosis sooner.
- This all mechanism relies on one thing, users taking the best photos and that is why, the first patient-facing ML model we implemented is a model that assist users taking the best pictures. It is a case of assist in Rie’s words. That sounds simple? This isn’t a straightforward task. Taking pictures for clinical analysis is a job and a job our users are not that good at as we could see looking at our data before that model was implemented. But let’s look at it in details. 1st screen, is the affected area clearly visible? We know there is skin but no lesion. 2nd screen, the message says ‘make sure the image is clear and in focus’. We know the picture is of low quality and advice the user accordingly. Last screen, last screen is the ultimate scoring with assist example. We know users took a good quality picture of their affected area, we can remove all confirmation screen, they go directly to the survey screen where they can rate the discomfort they feel in that area. For me, this is an awesome assisting job done here and an awesome job designing for AI performed by Rie. This feels intuitive and magical.
- And this image data of high quality we now have is also used to feed the model I will demo to you now and which aim at removing the super high complexity of accessing the right diagnosis for patients everywhere in the world regardless of income. The demo itself isn’t a world premiere but the model a real breakthrough diagnosing chronic skin conditions starting by psoriasis, dermatitis and acne. Demo time.
- We have to say thanks to the iLab for making it possible for us to be here, to the Imagine team all hands who execute on the beautiful products I presented & to the wonderful designer here who designed our 1st patient facing model. Questions?