Team 3 Kioni Bot

Solving day-to-day friction healthcare in Africa


Kioni bot

Solving day-to-day friction healthcare in Africa

When we study healthcare data from Africa, we find many rich descriptive analyses that that could be used for simple statistics but not for data science; it´s data that is hard to quantify, therefore hard or impossible to model. What if there is a technology that can gather quantifiable data in a friendly manner? This is what chatbots are already doing. They mimic a conversation with the user and by collecting their replies, it creates a new database.

During the hivhack the skills of design, data science, psychology and chatbot development teamed up in Kioni bot, a messenger interface that informs patients where to find the optimal health facilities that match the treatment they are looking for. Patients struggle friction healthcare, like long travelling hours (e.g. 5 hours walking) and long waiting hours to be attended. In addition, not all health locations provide all treatments. To avoid hiv stigma, the service is provided for different illnesses (e.g. malaria, dengue).

The “optimal” location criteria is  based on the treatment provided, the travelling distance, how crowded a facility is and other features. The database used during the hackathon was provided by Bluesquare, referred to the D.R. of Congo. This database provides geoinformation that in some occasions in not not registered in google maps.

In the future, the friendship between bots and patients will offer many advantages. It is a win-win situation where the user can receive psychological support, have a friendly reminder to follow up its treatment, have info about stock outs and more. For the stakeholders, the bot can provide unique (up to now) information about the movement of people and many other medico-socio indicators that are relevant to model hiv drug resistance.