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Hackathon: Versus Virus (First online inclusive ecosystem to tackle the COVID-19 crisis and largest collaborative online event in the history of Switzerland)
Date: 03.04.2020 – 05.04.2020
Project duration: 48 hours
Industry: Healthcare
Team: 15 people (9 nationalities, 12 languages, 1 goal)
Role: UI/UX Design, Motion graphics
Tools: Slack, Miro, Google workspace, Figma, Adobe Illustrator, AdobeAfter Effects.
Hackathon: Versus Virus (First online inclusive ecosystem to tackle the COVID-19 crisis and largest collaborative online event in the history of Switzerland)
Date: 03.04.2020 – 05.04.2020
Project duration: 48 hours
Industry: Healthcare
Team: 15 people (9 nationalities, 12 languages, 1 goal)
Role: UI/UX Design, Motion graphics
Tools: Slack, Miro, Google workspace, Figma, Adobe Illustrator, AdobeAfter Effects.
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Banyumas, Central Java, Indonesia. 4. October 2020. Photo from Mufid Majnun.
Problem to solve
The Coronavirus pandemic in Switzerland brought along high mortality rates, and all hospitals did their best to cope with the situation. One major challenge was keeping a timely and accurate overview of medical equipment availability at an appropriate level of detail, which hampered the efforts to fight the pandemic. This situation compromised medical professionals who worked long hours and experienced stress while running their facilities at full capacity. It also prevented the full capacity available at a regional and national scale from being leveraged, thereby preventing new patients from being attended with the proper equipment.
Ideation
Based on the team's criteria of impact, timing, feasibility, and reliability, we selected a topic and data source provided by the Hackathon. The category "Hospital & Medical care" and the data source of the "Swiss public medical infrastructure" was chosen due to its perceived reliability and potential to have a strong impact in supporting efforts during the pandemic.
Once we had chosen our area of focus, we collectively brainstormed through a Miro board and generated several ideas. We then voted to select the idea with the highest potential impact.
Solution
The solution we came up with is to develop a web app to assist medical professionals at all hospitals in Switzerland that identifies and forecasts spare capacity early to allocate ICU beds accordingly based on demand.
How it works?
ICUCH is a front-end web application with back end integration supported by AI (SIR) model. ICUCH works as a real-time, data-driven system that aggregates all available information and data to analyse the real-time supply and demand allocation of ICU beds and medical supplies across all hospitals in Switzerland. The data source is based on Swiss public medical infrastructure.
ICUCH is a front-end web application with back end integration supported by AI (SIR) model. ICUCH works as a real-time, data-driven system that aggregates all available information and data to analyse the real-time supply and demand allocation of ICU beds and medical supplies across all hospitals in Switzerland. The data source is based on Swiss public medical infrastructure.
Who it's for?
Medical professionals at all hospitals in Switzerland.
Medical professionals at all hospitals in Switzerland.
The problems it solves:
- Identifies bottlenecks of ICU bed capacity.
- Forecasts spare capacity of ICU beds as early as possible.
- Maximize allocation of ICU beds according to demand.
- Facilitates fast decision making and action taking.
- Provides with an availability overview of ICU beds and medical supplies.
- Forecasts spare capacity of ICU beds as early as possible.
- Maximize allocation of ICU beds according to demand.
- Facilitates fast decision making and action taking.
- Provides with an availability overview of ICU beds and medical supplies.
Feasibility:
The solution relies on a standard web application software, currently available data as well as forecasting models.
The solution relies on a standard web application software, currently available data as well as forecasting models.
Scalability:
The solution scales by distributing tracking and monitoring efforts to medical professionals.
The solution scales by distributing tracking and monitoring efforts to medical professionals.
Transferability:
The solution can be practically accessed in medical centers via desktop or mobile to monitor, request and release critical items.
The solution can be practically accessed in medical centers via desktop or mobile to monitor, request and release critical items.
The vision
Establish a data-driven and AI supported supply and demand allocation system for ICU beds and medical supplies.
User interface designs
Two main tasks at beginning of the journey with a simple interface.
The main interface relies on a map interface that display available ICU beds through a color and size based system that display available bed capacity to the hospitals in the surrounding area.
When hospitals are in need of an extra ICU bed, the staff can find the nearest available in the surrounding hospitals and request the allocation.
The timeline feature allows users to check the demand history of beds to plan and reserve beds in the future based on the estimated times when beds will be available.
Live data informing the current status of beds allows medical staff to better understand the current situation of bed availability. In the case beds are not available medical staff are offered the opportunity find suitable options.
Status quo:
- The web application has been built and is ready for use.
- The forecasting AI model has been developed.
- The forecasting AI model has been developed.
Challenges we ran onto
- Time constrain
- Data partially available which led us to take some assumptions.
- Data partially available which led us to take some assumptions.
Accomplishments
- Achieved challenging timelines by leveraging the different skills of team members.
- Funding awarded as part of 16 selected projects from over 1'000 submissions.
- Funding awarded as part of 16 selected projects from over 1'000 submissions.
Future potential:
- Extend with additional medical equipment, e.g. life support machines.
- Provide access to medical suppliers.
- Open to research centers, developers via API (revenue generation).
- Open to the public.
- Use anonymised data of patients to create risk profiles.
- Extend scope beyond Switzerland.
- Provide access to medical suppliers.
- Open to research centers, developers via API (revenue generation).
- Open to the public.
- Use anonymised data of patients to create risk profiles.
- Extend scope beyond Switzerland.
More about the project:
Contact us at: icuch.team@gmai.com
🫶🏽
Special thanks to all nurses who saved my Mother's life against COVID.
