For medical institutions, Instant Medical Radar (IMR) is a platform for the aggregation of health parameters and risk exposure to create an early warning system before entering clinical analysis. Furthermore, IMR allows to collect and aggregate data in parallel to clinical analysis. One of the challenges experienced during these times is the inability of linking and aggregating different information sources. Right now, every one collects and stores data preventing the easy extraction of useful information from different data sources which is most present when it comes to "pre-clinical" data (early symptoms, feelings, and not yet severe pathologies). This is where the proposed platform enters as a tool: It allows extracting information from the combination of patient symptoms (through surveys), risk exposure (by geographical data, such as location histories) and individual parameters (such as age, gender, previous health condition). Aggregated metrics combine various results extracted from the three mentioned input sources. In a further step, also external providers could be linked. Patients get an easy-to-use interface for data input (their health parameters at a given time-interval, location history, and individual data). Hospitals and/or officials are guided to create surveys and to extract meaningful data and visualisations. An example for the current Covid-19 pandemic would include, for the patients, entering body temperature and observed coughing as symptoms, public transportation usage and being at crowded places as location history, as well as age and previous diseases as individual information. Possible metrics to monitor are the percentage of symptom-presenting people, the mean duration between first coughs and breathlessness, the correlation of public transport usage and infections, etc. They can be represented by fitting models or by displaying heat maps.\par
During the Hackathon, a first prototype was built on Adobe XD and a potential initial data structure has been established. The app will be realised as a mobile-friendly web-app which makes it accessible to most people. From the app's point of view, there are two types of users: Institutional users are affiliated to an institution (such as a hospital) and are able to create, edit, and publish surveys. Moreover, they are able to create and track aggregated metrics. The patients are identified by a unique code which they get from their doctor. They receive periodic (daily, weekly, monthly) email reminders to fill out the surveys. Patients can be subscribed to multiple surveys but they can only have one individual profile and one location history. Metrics are defined as mathematical operations of different (configurable) types. They are linked to value pickers which allow to extract a set of values (from the questions, geo data, individual data, other metrics, constants) and act as one of the metric's operands. This allows for dynamic calculation of the needed metrics. The first core deliverable is the web-app and its server back-end allowing for for institutional users to create surveys, send them to users and create output metrics. In a second step, the location history is included and interfaced to the metrics. Finally, the number of different question types, value pickers and metrics can be increased and templates should be added.