How can we empower Sanitas to become tangible as an active dialogue partner for the customers?
What would the future of insurance look like?
Built a compression sock prototype that calls a designated number when the pressure sensor is activated.
Prototyped a mobile app.
Prototyped a voice-based chatbot with Google Home.
Designed and prototyped the mobile application.
Provided the visual designs for the product.
Helped prototype the voice-based chatbot.
Focus: Deep Vein Thrombosis
Deep vein thrombosis (DVT) is a chronic cardiovascular disease, which affects 0.2% of the Swiss population annually.
It occurs when a blood clot develops in the deep veins of the body, 90% in the legs. This blocks the blood flow and increases the risk of more severe health conditions, such as pulmonary embolism and postphlebitic syndrome.
DVT therapies require patients to frequently exercise and closely monitor their diet. If DVT is not treated correctly, secondary diseases emerge.
DVT patients frequently wear DVT compression socks as a precautionary measure to avoid emergency situations such as pulmonary embolism. TESSA is an immersive environment for DVT patients, with the smart compression sock acquiring health data and the smartphone app and Google Home watching the patient's diet and activity.
Understanding the User
Restaurant owner in Zuoz, Switzerland
55 years old
has DVT in his right leg
keeping track of his health
spending less time with his insurance claims
have someone/something remind him of taking his medications
Beat’s doctor prescribed Beat DVT-stockings to lower the risk of his thrombosis spreading. Beat decides to use TESSA. TESSA’s chatbot reminds him to take his medications via push-notifications on his smartphone so that he doesn't forget it even on busier days in his restaurant.
TESSA reminds Beat of exercising regularly and the benefits of doing so. One evening, Beat felt a strong pain in his leg. The pulse sensor on the sock detects this, and the in-app chatbot reacted immediately and asked him about his symptoms. TESSA identified an emergency, which required immediate attention. The chatbot reacted to both Beat’s verbal symptom descriptions and physical assessment. The chatbot set up an appointment with the nearest available doctor so that his wife Margareta could drive him there. With the help of TESSA, an acute pulmonary embolism was found and prevented with the immediate and aggressive use of anticoagulant medicines.
Case 1: Typical day
Case 2: Emergency
The chatbot asks Beat what he had for lunch. Beat takes a picture of what he had and sends it to the chatbot. The image recognition AI recognizes the food based on the picture, and suggests a healthier meal by sending him a recipe. When Beat comes home from his daily exercise, he asks the bot how much he ran today via Google home.
The compression sock tracks the user's vital signs and identifies symptoms of life-threatening conditions.
In emergency scenarios, the compression sock use its integrated GPS system to notify the nearest secondary healthcare provider. Live health data is sent to the healthcare provider so that the situation can be assessed prior to the treatment.
What it does
Primarily, the sock focuses on emergency prevention - the wearable device detects the patient's vital signs that are relevant to emergency pulmonary embolism such as pulse, heart rate, and body temperature. When it detects signs of emergency, the bot quickly asks the users if they're okay via smartphone app. When there is no response, the app immediately sends an ambulance to the sock's GPS location.
Creating an immersive environment
What we made
Using littleBits, we succeeded in making a basic working prototype of a sock that makes a phone call to the designated number when the user presses the button attached to the sock. For the smartphone app accompaniment, we designed and prototyped a basic chatbot app interface. We also made a voice-based chatbot prototype that can respond to users' voice commands via Google Home.
Emergency Scenario 01
The compression sock detects any sign of emergency and notifies the patient.
Emergency Scenario 02
The chatbot calls an ambulance.
Typical Day Scenario 01: Dietary Consultance
The chatbot can check on the patient's diet using image recognition.
We prototyped the sock prototoype with LittleBits, the voice interface with Google home, and the second iteration of the iOS app with inVision. Unfortunately, we couldn't get the footage of us interacting with the voice prototype.