Together with the leading experts in the virology of the Charité Berlin, we made a chatbot based hotline available, which advises you if you should get tested or not. This weekend we would like to make the hotline ready for deployment in German and translate it into English.
just call +1 877-201-9777 from the US or +372 668 2808 from Swiss / Germany or +45 80 82 01 45 from Denmark and visit our landingpage Both in Europe and the USA, the hotlines supports all 29 questions, and the first test pilots started last week in Berlin.
How testing works in Germany
Germany is one of the leading countries in testing on COVID-19, with probably one of the lowest dark figures worldwide. This is mainly due to a skillful pre-selection of who is invited to a test. The patients are pre-selected based on 29 questions and divided into categories. The categories can be simplified with:
Which recommendation the patient receives is based on his symptoms, his exposure to other patients who have tested positive or his recent stay in risk areas. Besides, exclusion procedures are applied to see if, for example, it is only flu.
This questionnaire is currently filled by assistants on the phone and then entered manually into a web application. The used app https://covapp.charite.de/ is already provided in English by the Charité Berlin.
CovApp - Questionnaire by Charité
Within the web application CovApp the patient needs to answer 29 questions. These questions try to inquire the medical history of the user:
Following up on the questions, the answers are evaluated with the help of a decision tree in which each hospital can define themself, and a recommendation is given on how to behave. In addition, the user receives a QR Code - in which his answers are encoded as XML. This QR code is used to transfer the data to the hospital information systems.
Our approach - chatbot-based hotline using AWS
To provide the hotline, Amazon Connect is used as a scalable customer center solution. On this basis, a chatbot was programmed, which interacts with the user via voice or text input. Amazon Lex and AWS Lambda are used for speech recognition and validation. The current goal is to provide an interface where each hospital can create its decision tree. However, this interface will probably not be realized by us. In addition to our hotline solution, there is already an app and a webpage solution, which will be implemented this week. We only share some medical history based on 30 chatbot question, the hospital interprets it and can give their recommendation.
Simple word pairs - to be used as analog QR-code
The response possibilities amount to approximately 300 million combinations. To encode them in a user-friendly way, each combination is mapped to a word pair from the dictionary. The customer has verbally conveyed the word pair together with the recommendation after the survey. He can memorize it or note this word pair and share it with a doctor. A further service developed with Ionic and provided as an app can be used by health care personnel to generate and view the corresponding medical history from the word pair. Furthermore, a QR code is also created for which a procedure for integration into the hospital information system already exists.
Work in The "Second Swiss Weekend"
Our challenge for the weekend was to come up with a solution that would allow IT inexperienced people, to build their hotline. In the previous weekends, we have already developed an English and a German solution. To have a usable product, we had to create an interface that allows doctors to submit questionnaires themselves.
How we solved the problem
It was essential to consider the Amazon Connect structure in the background and, at the same time, provide an evaluation at the end of the process. On the other hand, the tool should not become too complex and incomprehensible. Unfortunately, the task of developing such a tool is very technical and time-consuming. The result might look relatively unspectacular, but is still the most ambitious part we have implemented yet. We had to adapt our hardcoded solutions, which we have been working on over the last two weeks so that it can handle flexible inputs.
This figure illustrated questions input fields.
You can test it under Github.It still needs some UX-Design, but works so far.
Technical Side problem
For the solution, we used a react redux framework, which creates JSON files at the end, which are then loaded back into amazon connect. In the Amazon instance, contact flows are created from the Json file, which define how the conversation will take place. In addition to the contact flows, the parameters for the evaluation Json are also set.
You can see it on this
This figure shows realized use cases depending on frontend, backend and amazon connect instance.
Many thanks to the team of the Swiss Hackathon. The mentors who supported us were great and the choice of mentors, even better. It's crazy that you managed to get us together not only with a hotline operator, but also with someone who programmed hotlines.
Unfortunately, we had so many meetings over the course of the weekend that we couldn't take full advantage of this support. But thanks a lot to our mentors and the organisers for all their effort.
This flow shows the procedure of the solution. To make it easier to understand, it is a simplified variant that does not represent all processes on the server-side.
This diagram is more complex and shows how the web app works in detail.
We are trying to implement a client-side solution for the Charite in the next days, and we are in contact with the responsible persons of the University Hospital. At the moment, our solution only works in hospitals/practices that use SAP. We want to use a medical standard and then connect it to the various information systems. We want to offer our service and provide a medical consultation via phone to another population group!
Best Project for Health Companion Services # CodevsCovid
Sportlight Best Public Health Information Sharing Project at Pandemic Response Hackathon
Within the #WirVsVirus Hackathon, we as a team consisting of Aaron Szerencses, Alexander Schoenhals, Anna Lueders, Christopher Seidemann, Fabian Lueders, Philip Ehret, Ruben Koch, and Thomas Hepp developed a solution in exchange with the responsible persons of CovApp!
Many thanks to the great Hackathon mentors for the support and help. Also, we would like to thank Amazon for providing the server structure. Many thanks to the Charité, who gave us insights into the structure of their app and actively supported us.