In this webpage, we can share with you different use cases, solutions and approaches that we have worked with different partners/clients:
In this webpage, we can share with you different use cases, solutions and approaches that we have worked with different partners/clients:
We specialize in creating powerful data platforms that transform how organizations manage and use their data with the goal of empowering our clients to unlock meaningful insights and drive impactful decision-making.
An organization plans to collect surveys in Kobo (a free toolkit for collecting data). However, we want to have it on a DHIS2 instance as well. To avoid making the data entry twice they want to transfer this information automatically (they will be events in dhis2).
More context items:
In order to accomplish this request, an ETL process was set up using Apache NiFi (an easy to use, powerful, and reliable system to process and distribute data). The main steps of that process are:
In this screenshot you can see the ETL process configured in Apache NiFi
We have a cluster of machines to track the behavior of every server that we are managing. This cluster consists in several BI platforms which allow to track and monitor different variables :
In addition to that, and for DHIS2 servers, we also provide a solution to monitor in close to real time:
We also provide a centralized alerting system, which is configured to send alerts to different channels (Teams, slack, Skype, emails, SMS, etc.) whenever there is a condition that triggers an alert. For example: Analytics are not running, DHIS2 is taking a long time to respond, cpu or memory reaching 80% utilization, etc.
We normally provide cloud management with AWS services or Azure, but we are open to exploring other options with you if desired. All DHIS2 servers will have the following setup:
All our servers meet all prescribed security protocols, including multiple levels of protection to ensure the confidentiality of the data.
Server security
Database Security
Webserver security
The use of smartphones and social media apps like Facebook Messenger, WhatsApp, Viber or Telegram was widespread among our target users (the health worker) to connect with friends and family.
Overcoming the impediments of traditional paperbased and custom-built mobile reporting tools, there is an approach for real-time disease notification mechanism using social media chatbots.
These tools were found to be ideal due to their ease of use (everybody has a social media account!) and require only minimal training, maintenance, and troubleshooting (no need to upgrade apps, manage user accounts, or ensure compatibility with mobile devices).
In order to facilitate the adoption of mobile case reporting solutions among the users, we designed and deployed several chatbots for different domains like health and education, using technologies like Microsoft Bot Framework or RapidPro.
Our chatbots are implemented on top of several social media tools and messaging applications, like: Facebook messenger, Viber, Whatsapp, Skype, Slack, Zalo, Telegram, among others.
These solutions enable the collection of case-based data from the bot and push them to any backend (i.e. to a tracker program in a DHIS2 instance). In addition, chatbot flows are being built out to facilitate communication and provide support to the end users.
Our chatbots are being used in Vietnam, Laos, Myanmar (covid and malaria cases), Philippines, Panama, and Indonesia (education).
In Myanmar, Population Services International launched the Notifiable Diseases Information System (“NODIS”) chatbot, that was intially built for reporting Malaria cases using Facebook Messnger and saved into a DHIS2 database.
The chatbot was updated with additional options for disease notification for tuberculosis (“probable TB”) and fever with rash (covering measles, dengue, chikungunya, etc.), as well as including an additional reporting channel, Viber.
Also, instant alerts to local health authorities are sent via automated SMS and reports are synced with open-source DHIS2 platform, which opens the door for potential future integration of data into national surveillance systems.
How are the end users navigating dhis2? What are the main dhis2 visualizations accessed? Are the dashboards really being used? All these are key questions in order to measure how the system is being used, and if the system is having an impact in terms of data quality, access and meaningful visualization.
DHIS2 has tools (eg. Usage Analytics app) that allow rendering visualizations for metrics such as favorite views, top favorites, etc. However, depending on the complexity of the metrics to render, the default DHIS2 apps are not currently as adequate at measuring dynamic analyses of different types of user behaviors and engagement.
Most of this information can be exposed in the system logs as free text, especially in the web server and application logs. It is possible to find in the navigation of those files information about the most common API endpoints used, latency time, devices and browsers accessing the system, and, if exposed, user names.
For complex usage analytics this is an approach that uses the DHIS2 system logs as a source for analyzing end user behavior and engagement with the system. Regardless of the size of the log files and their format, it is possible to transform the content of those files in dimensions and facts that can be stored in a data warehouse. It is then possible to connect the data warehouse with any BI tool (like PowerBI, Tableau, superset, etc.) to perform complex calculations, generate custom metrics, and render advanced visualizations of DHIS2 system use.
This solution is deployed for some clients, like Population Services International (PSI)