What the story of the “Three Little Pigs” can teach us about hosting

As a software company, we often design tools for our clients in the scope of a specific project. And a key success factor in any of our projects is the transfer of ownership of these tools and services we’ve designed to our client, often situated in LMIC countries. We advocate for it with tooth and nails.

Yet, we also always advise that any tools we provide remain “hosted” with us, rather than at a local infrastructure. Hosting for us is a zero-sum game, it’s far from being our most profitable line of business and it requires constant maintenance. 

So why do we offer it, and even recommend it? And wait, first, what again is hosting exactly?

Building a house for your data that won’t be huffed and puffed and blown away

You’ve heard the nursery rhyme about the three little pigs, one who built his house with straw out of laziness and whose house got huffed and puffed and blown away by a wolf. One who built it with sticks and whose house, again, got huffed and puffed and blown away by a wolf.

The third one, however, took the long road to the brickworks and built a sturdy house that the wolf couldn’t huff and puff and blow away. 

Well, hosting is very much like having a house. And you can choose the straw one, the stick one or the brick one – at your own peril. Every tool we create needs to be hosted (housed) on what we call a server (the actual house) somewhere. Somewhere with doors that only specific people can enter (enhanced data security), that won’t be easily damaged, etc. 

In fact, losing, damaging or having no steady access to your service can lead to disastrous consequences, especially in the case of health information systems.

So what type of house can you choose from to house your project?

The straw and twigs house: under-the-desk hosting

No, this is not a joke; yes this happens frequently; no not just for unimportant projects.

This kind of  hosting is a laptop dependent server. While this may be fine for people who want their own server, to game or code personally when they want to, it obviously cannot be relied upon for projects impacting the health of thousands of people.

This server is at risk not only of being closed by a well-intentioned coworker, but also of being in an unsafe environment. From high temperatures, to a lack of proper fire safety (computers don’t like water nor fire), non-existent redundancy for electric power or a guaranteed internet connection, etc.

Aside from the risks inherent to the placement of this server, scaling up the infrastructure as the project grows will be really difficult. In any case, it will be definitely harder than a click to get a bigger server or more storage for a few extra bucks paid in an instant with your credit card.

The bricks house: cloud hosting

These have a decent place to store your data: physical access is verified, a cooling system is in place, ideally there’s an uninterruptible power supply, a broadband internet connection, etc.

In the case of international hosting specifically, such as AWS, Azure, Google, etc, they even have all the right certifications in place, give easy access to instant upgrades and promote best practices in terms of keeping your house up to industry standards. In fact, we at Bluesquare use AWS and host our client’s projects on it.

But in both cases, clients still need to build up their experience and skills, and set up some processes themselves in case of an unfortunate event. And, most importantly, the team in charge of the server usually has limited knowledge on health data in LMIC countries (mostly DHIS2 based). They will probably execute some tasks once a year, where our team executes them on dozens of servers per year.

So why do we bother with hosting when it’s more work than profit?

Bluesquare hosts dozens of servers and invests in automation of provisioning (creating “new servers” based on specific characteristics), monitoring, troubleshooting, and capacity planning. On top of offering all that cloud hosting provides, we have a monitoring team expert in the DHIS2 and health data stack dedicated to investigating problems before they occur. We also minimize interventions directly on your servers by automating most regular tasks via our slack channel and bots.

And this is just the tip of what we do to keep our client’s projects from being huffed and puffed.

We do it because we are committed to making our projects succeed in the long term. Because we believe in transferring ownership, while ensuring our clients can make the most out of the tools we created without worry. We are dedicated to our clients, to our work, and hosting tools critical to their health systems is an integral part of it.

View our DHIS2 hosting options →

Bluesquare is one of the 2 organisations officially recommended by the University of Oslo to host and manage DHIS2 instances.

New features of D2D, our DHIS2 data integration tool

One of the biggest challenges we face during our monitoring and evaluation projects is the existence of multiple data sources that are not unified nor synchronized, making it difficult to have a steady and constant flow of data. To resolve this, Bluesquare developed D2D.

D2D is Bluesquare’s tool that allows DHIS2 data integration between multiple instances. This tool is very useful in the context of large programs or in countries that have different DHIS2 instances. D2D makes it possible to synchronize the different data sources in order to count on more reliable data. 

Bluesquare has at heart to offer to its partner continuous improvement on its tools and to make them accessible for all. D2D has been upgraded recently in order to make the interface more user friendly and simple to use for non-technical data and program managers. We have been working on 3 aspects to improve D2D. It is now more convenient, faster and more comfortable to use.

  • Convenience

To make DHIS a better partner for data pipelines, D2D gained the ability to export to a CSV directly to your own Amazon S3 Bucket. This CSV file could then be used in a larger data pipeline. This can both be run manually or on a scheduled basis (like all the other D2D tasks)

  • Speed

In our ongoing effort to make D2D smarter and faster we’ve also added features to limit the amount of data that needs to be transferred between DHIS servers. You can now instruct D2D to only handle data that was changed after a specific date (or within the last x days). This will speed up your data flows and make D2D run faster.

  • Comfort

We’ve also added a bit more insight to the D2D process, as a user you can ask D2D for a preview of the data that it will be pushing from the source DHIS to the target DHIS. After viewing this preview, you will be able to cancel or resume the task.

Bluesquare is very attentive to the needs of our partners. We make sure to improve our tools in the direction of their needs. Get in touch with our team to learn more about D2D !

Monitoring your program with DHIS2

Get the pointers needed to decide if DHIS2 is the best fit for your program

Bluesquare and IT4life are excited to announce the launch of  “Digitizing your programs: tools and tips”, a series of webinars to help project and M&E managers identify the best digital solutions for their operations, and make the right implementation decisions

The first webinar will explore DHIS2, an open-source software broadly used as a health management information system. Recently the tool has been increasingly used in support of education, WASH, and humanitarian aid projects, or as a monitoring and evaluation platform. DHIS2 allows for better data collection, management, visualization, and data use.

During the webinar, participants will have the opportunity to engage with Olivier Cheminat, DHIS2 specialist at Bluesquare, who will explore the advantages of DHIS2 to support project digitization or M&E processes. Through this webinar, you will be able to better grasp in which situations DHIS2 can be a good fit for your intervention, taking into account human resources and costs constraints

If you are an M&E, MEAL manager, or a project manager, and would like to learn more about the use of DHIS2 and alternative solutions to this technology’s shortcomings, this webinar is made for you. 

Register Thursday 10th June, 2 pm-3pm (Brussels Timezone)

To stay up to date on upcoming webinars in the “Digitizing your programs: tools and tips” series, contact Mireille Ntchagang, mntchagang@bluesquarehub.com.

OpenHexa, Bluesquare’s new Data Integration Platform

We often hear about the scarcity of health data coming from low resource countries. However, in the last decade, we at Bluesquare have experienced a significant increase in the volume of data being collected through the expansion of software solutions such as DHIS2.

However, with the development of data transmission through multiple communication networks, it can become hard to keep track of all the data being collected by public facilities, private actors, NGOs running campaigns, research projects or logistics systems. It is even harder to mobilize these various sources of data to create meaningful analysis and reports that can help guide public health decisions.

To help solve this problem, Bluesquare is proud to introduce OpenHexa, our open-source data integration platform targeted at health data professionals.

OpenHexa’s interface

Solving the key challenges of data processing : Exploration, Extraction, Visualisation

OpenHexa is focused on four key capabilities:

  • Data exploration through the data catalog component, which allows users to browse, search and discuss data from different sources (S3 buckets, PostgreSQL databases, DHIS2 instances…). This will make possible the synchronization of data from different sources and formats.
  • Collaborative interactive computing through the notebooks component, based on Jupyterhub, where users can create and share notebooks. This will allow software users to easily share their ideas and advancements across teams.
  • Automated data extraction and transformation using the data pipelines component, which will greatly speed up the extraction processes.
  • Data visualization through the visualization component, which provides an easy way to use OpenHexa data in different data visualization and business intelligence tools. 

OpenHexa also provides powerful access control features, allowing you to make sure that every user can only see and work on the data he has been authorized to use.

A user oriented open source platform

By developing OpenHexa as an open-source platform, we are tackling two challenges:

  1. The lack of open-source integrated data platforms: there are many  high-quality open-source software tools for data visualization, analysis or automation, but integrated platforms that combine the different aspects of data science tend to be proprietary, expensive, and plagued by opaque pricing structures.
  2. The brittleness of data workflows in public health projects: when working with health data, experts are often confronted with heterogeneous data, tasks that require manual operations, and siloed information, leading to analyses that are disorganized and difficult to reproduce .

OpenHexa offers a novel solution that is both integrated and 100% open-source (codebase available on Github). We can host the platform for you in our cloud infrastructure, or you can deploy it yourself on any cloud provider – even in your own infrastructure.

Exploration in OpenHexa

Our aim with developing this platform is to offer our partners and future partners a tool that enables programs and managers to automate processes that are often manual, time consuming and error-prone. We had at heart to make it open source and to have a user friendly interface to allow a great number of administrators and projects to benefit from it.

Are you as excited about the launch of Openhexa as we are? Participate in the development of the source code on Github or request a demo session with one of our experts.

Use case: Improving the national surveillance system for infectious diseases

While not limited to health-centric workflows, our platform has been developed with health data as the primary use case. 

OpenHexa offers an ideal environment for local universities, analytical units within Ministries of Health, Institutes of Public Health, National Statistical Institutes or international partners to implement a wide variety of data analysis, at national or sub-national level.

As an example, let’s consider an epidemiologist who is interested in improving the national surveillance system for infectious diseases. This system relies on weekly data collection on about 20 diseases collated at the district level. Each week, data is aggregated manually by a data manager in a provincial bureau and sent to the national surveillance team at the MOH. Then analysts from the national surveillance team evaluate the data using Microsoft Excel and try to identify outbreaks.

How can OpenHexa improve this workflow?

  1. Using the data pipelines component, automated extraction pipelines are implemented to ensure that up-to-date data is consolidated every week. Current use cases include data coming from DHIS2, Excel systems, EpiData collection systems, Access databases
  2. Within the notebooks component, a data scientist develops an outbreak detection algorithm in collaboration with national experts and academic teams and colleagues within and outside the country
  3. The data scientist can share the outbreak notebook with their colleagues and local experts
  4. The outbreak algorithm is then deployed as a data transformation pipeline and scheduled to run every week using the latest data
  5. Using a third-party visualization tool (such as Tableau or PowerBI) connected to OpenHexa, a data visualization expert creates a dashboard to visualize the outbreak data
  6. A local monitoring and evaluation team is trained to use the data integration platform, operate the outbreak detection code, and evaluate the data visualized in the dashboard, and oversees regular updates to the surveillance system. They can zoom on specific zones, compare outbreak cinetics from different years, articulate various relevant data series.

Thanks to OpenHexa, we have moved from a manual, error-prone process to an automated and reproducible solution which allows in-country teams to generate better insights into district-level epidemiological trends.

Webinar series : Digitizing your programs: tools and tips

Are you struggling to choose the right digital tools to monitor your program and leverage data ?

Digitizing project processes can lead to a significant improvement in data availability, timeliness, quality and consistency. However, with the array of digital solutions now available, project and M&E managers are left with a burning question: “what digital tools are best suited to monitor my program and leverage data for my specific needs and budget?”

And that is exactly the question we will offer an answer to in this series of webinars focused on real-life use cases. We will explore the best-in-class software and hardware solutions available for various use cases and openly discuss their advantages, constraints, deployment requirements and costs. As we know that technology is only a means to an end, we will also discuss change management practices that will allow you to get the best organization value and impact out of your digital investments.

Join us to get answer on issues such as :

  • Is DHIS2 a good platform to manage my M&E processes? 
  • What is the best mobile data collection tool for my specific use case?
  • How can I digitize beneficiary management? 
  • What are the best digital tools for stock management? 
  • Are there efficient solutions for activity tracking – and how can it be linked to M&E?
  • What are the best and most cost-efficient solutions for data visualization? 
  • What are the conditions for proper digital tools deployment ?
  • What is the best and most cost-efficient hardware to digitize my workflows ?
  • Practical considerations to scale digital tools within your organization
  • What does it take to create a data culture within your organization?

Digitizing data collection and management tools for the monitoring and evaluation of programs can bring a significant improvement in data availability, timeliness, quality and consistency. Numerous digital solutions are now available to support various data workflows, leaving M&E managers with a burning question: “what tools are the best fitted for my specific needs and budget?”

Sign up now for the first webinar in the series: “Monitoring your program with DHIS2” And stay tuned for the other webinars in this series to:

  • Get a clear understanding of the digital solutions for M&E environments
  • Get straightforward feedback from our pool of experts on your current challenges
  • Connect with an active community involved in setting up digital tools for M&E.

Stay up to date by signing up for our newsletter.

Strenghtening public health emergency operation centers through data support

Bluesquare is delighted to announce the launch of a new intervention in four West-African countries : Niger, Burkina-Faso, Cameroon and Ivory Coast. Funding for the project is provided by the Bill & Melinda Gates Foundation.

The objective of this intervention is to provide operational tools and processes to these four countries in order to improve rapid and appropriate decision-making in health crisis contexts. This project will benefit from the technical advances on which Bluesquare invested heavily in recent years.

We are honoured to be part of a project that contributes to improve global health, which lies at the heart of our values.

The intervention

Bluesquare is leading an intervention aimed at strengthening emergency operation centers (EOCs) by improving data management and data use across stakeholders and fostering health emergency preparedness through a sound use of data. Beyond providing EOCs with data management systems, the project leverages our solid engagement with IT teams in Ministries of Health to promote structural improvements for health information systems in these countries: mapping of health facilities, geospatial data to enhance campaign planning, program monitoring (especially Malaria programs).

Data use is of particular importance to promote next-generation, results-driven health policies, delivering on the promises of technology in deprived socio-economic context.

Our intervention is structured around four main areas :

  1. COVID-19 data system integration,
  2. Malaria and Reproductive Maternal Newborn Child and Adolescent Health (RMNCAH) data system integration,
  3. Support the development of a Health Facility Registry,
  4. Support the setup of common geo-registries.

The data systems consolidation part of the project (areas 1 et 2) revolves around the implementation of a data integration platform. This platform is configured as an open-source data warehouse, combined with powerful analysis tools enabling the management, use and sharing of health data by different partners.

The module dedicated to the strengthening of Health Facility Registries and common geo-registries is a key component of our interventions to strengthen health systems in low and middle-income countries in the long run. It is essential for us to improve the geolocation of essential health information (like health facilities location, service availability, villages location, population estimates, etc.) in order to improve general health policies and emergency responses.

Health Facility Registry in DRC

A collaborative project

Success will be achieved through close collaboration with the national authorities of the four partner countries (Ministry of Health, Statistical Institutes, authorities in charge of planning) but also with other non-governmental organisations and corporations working in the health sector in these countries. We are pleased to announce the successful start of this collaboration with national authorities in all four countries and active joint initiatives with other partners in our intervention.

Our goal, through the implementation of the data integration platform and support of the construction of an up-to-date digital health map, is to rapidly improve the general enforcement of health policies starting with the COVID-19 pandemic response, the Malaria elimination campaign and the improvement of mother and child health with possible extension to other priority topics in the future.

Improving school mapping in DRC thanks to consolidation of geospatial data

The implementation of information systems for government ministries in the health system is Bluesquare’s core business. In this innovation project, carried out in partnership with Cordaid, a similar approach was taken in the field of education, aiming to set up a school map for the DRC using the tools already available in the health system.

Building a school registry platform

This innovation project aims to provide the different partners (Ministry of Education, NGOs, private partners) with a platform, integrated to the DHIS2, to monitor in real time indicators related to education in the DRC. The 2 main objectives are i) to enrich the school map by integrating different existing databases and ii) to make available a set of tools and propose a methodology to improve the school map.

Currently, there are no readily accessible tools for partners and decentralized levels (divisions and subdivisions) to collect and update information on schools. As a result:

  • Each partner collects information on schools in different regions, but there is no real integration of these data into the Ministry’s tools at the national level.  
  • Lower administrative level (divisions, sub-division) reports information back to the national level (paper forms) but receives very little feedback or access to their own data
Section of the “Carte scolaire” or national school map. It shows the percentage of girls in the schools in Sankuru province.

Why is this important ?

Correct information about the location and functionality of schools allows better evaluation and planning in order to satisfy local populations needs. Once the database is completed, and in combination with other data such as population density, it will be easy to answer questions such as

  • Where do children have to walk particularly far to reach a school ?
  • Where are there too many children compared to the existing schools capacities ?

The challenges of the database construction

As in the health sector, from one datasource to another, the same school often has a slightly different name with no common identifier. Using algorithmic tools, based not only on name, but also on belonging to different provinces/divisions/ subdivisions and different geographical criteria (e.g. GPS positions when available) these sources can be reconciled. Once the different sources are put into our common repository tool Iaso, the different metadata, mainly on location, type of school, but also functional data when available can be added (e.g. number of students or classes by schools). All these steps of database construction are done in a facility registry matching software which allows to update the information, continuously, with validation processes, before being pushed to the DHIS2 school map (secured) on which is also connected to a public visualization platform.

Results of the integration of the different data sources: location of over 10.000 schools in the DRC.

Our georegistry platform in action

As mentioned, combining, harmonising and merging different data sources is a job for our georegistry software. Iaso combines a web dashboard and management interface with a mobile data collection tool. It allows easy query and display of geographic data, geared particularly towards the specific goal of data integration.

In the health sector, DHIS2 is the standard data management platform. In this project we used DHIS2 for handling the school data. Iaso is seamlessly integrated with DHIS2 allowing easy integration of DHIS2 data into Iaso for comparison with new data sources, and pushing the results of analysis in Iaso to DHIS2.

Innovation for a better society

Through this project, we wish to make accessible a national platform, piloted by the Ministry of Health, which could be digital, interoperable with the Ministry’s tools (eiter DHIS2 or SIGE) and continuously updated. For Bluesquare, innovative processes and the high-performance tools put in place are more important than the completeness of the data, which will follow.

From the positions of the schools and their belonging to a division and sub-division, we were able to artificially reconstruct a first version of the DRC geographical contours. This mappinp is still in the process of being improved.

Update on the new PBF manager app

Last March, following an in-depth training on our Performance-Based Financing tools, we asked our participants how we could improve our tools for all of our users.

6 months later,we would like to present you the updates we have made to the PBF management tools following these exchanges.

The new updates

A PFB program supported by DHIS2 typically uses a number of apps developed by Bluesquare that enhance the capabilities of DHIS2 for strategic purchasing: 

  • Hesabu is used to compute complex indicators such as quality of care metrics, performance scores, or payments. 
  • The invoice app is used to make invoices and payment orders available to the manager 
  • DataViz is used as citizen engagement interface 

Over the last months, we have developed a “PBF manager app”, a single DHIS2 application that groups together the different applications used for managing the different components of a PBF program. This app simplifies the overall management of the PBF scheme and provides new features and functionalities. 

Thanks to this “manager”, a PBF data manager will find all your activities in a single menu. Here is what it includes:

1. Managing Contracts

With a single click, it is possible to pull up the history of contracts for any specific health center:

This allows the PBF manager to have a clear view of the program. This is most helpful since the contract data is used in various other aspects of the PBF (for instance Invoice and tariffs)

2. Managing rates/prices/incentives

Bluesquare offers now a module that makes it possible to manage this information by period and for a complete region (what we call a “sub-pyramid”). 

3. Locking Invoices

We have added the ability to “lock” invoices – and thus avoid retroactive changes –  in the “invoice” section, where open or closed statuses are visible to everyone, and where authorized users can decide to block an invoice or reopen it for editing.

4. Miscellaneous Reports

The PBF manager allows programs to generate their own specific reports in the form of Excel tables available each morning via the application or sent directly by email to the team members concerned

5. A view on the rules

To help improve how to understand the complex payment rules in the system, the Hesabu application allows you now to observe the different health care packages, the related activities and the formulas used to calculate the payments

6. Publications

Finally, concerning the public portal, we have revised the publication module to improve the look and feel.

We look forward to sharing these various improvements with you and continuing to work with you on the next evolutions.

If you are interested to know more about the PBF manager, or the Bluesquare DHIS2 suite of tools, please contact us.

Healh Facility Registry Matching

Good information is a key input to successful strategy. If you saw that there’s a thunderstorm in the forecast, chances are you would rethink your trip to the beach. If you’re traveling somewhere you’ve never been before, you would probably use a map to figure out the best way to get there. Reliable, accessible data allow individuals to make the best decisions that they can. The same is true at the organizational level – governments, businesses, and nonprofits rely on what they know to manage their operations and inform their future plans. Unfortunately data in the global health sector are frequently scarce or fragmented. Bluesquare’s novel approaches to compiling and synthesizing that data help to create an information environment where decision-making is better informed and resource allocation more efficient.

Of the nearly 18,000 health facilities in the Democratic Republic of the Congo’s national health information system (SNIS), only about 35% have a GPS coordinate point. GPS coordinates are key to measure population access to services and products and help MoH and NGOs better estimating populations’ needs and better managing stocks. The SNIS is not the only source of data on the DRC’s health infrastructure, however. There is substantial information collected by NGOs, academics, and aid organizations working in the country that, combined with existing SNIS data, can significantly expand the information we have about the location of Congolese health facilities. For this work we used 24 sets of health facility data, containing a total of 73,190 individual points. 

Combining divergent data sources

There are three key challenges to combining these divergent data sources. The first is that the data are stored separately and organized in different ways, making integration a complicated and time-consuming process. Enters Iaso, Bluesquare’s geospatial data management platform. Iaso allows easy importing of data, can store as many different sources as needed, and lets users ‘link’ data points from different sources that represent the same clinic or hospital. Crucially, Iaso stores facility data in a consistent format that makes working with them straightforward.

Once all of the data are in Iaso, we face challenge number two: the inconsistency of facility names across datasets. While to humans it is clear that Mutiene Poste de Santé and Ps Mutien are the same facility, the slight difference in spelling and inconsistency in how the type of facility is specified makes it impossible for a computer to naively merge the datasets together. To address this issue we use a text-matching approach affectionately known as “the love machine.” Simply put, for a given facility from an outside source, we find the facility in the SNIS with the closest name. We then search the outside source for the facility whose name is closest to the name from SNIS that we just found. If we find the original facility name, we consider those two facilities a match and add the outside source’s coordinate data to our merged dataset. Where possible, sources’ geographic hierarchy are used to restrict matches to the correct province or zone de santé.

Synthetizing Geospatial data

The set of synthesized geodata from the sources matched by our love machine approach presents challenge number three. Namely, with multiple coordinate points for a facility, how do we determine that facility’s most likely location? Here we implement a GPS selection algorithm that takes into account the number of coordinate points available, their relation to the zone de santé that contains the facility, and how they are clustered to determine outliers and select the location closest to all of the valid points.

For example, the health facility shown in the map on the left, Bashimikie Centre de Santé in Lomami, has three points in the zone de santé (red, blue, and green). However, one is noticeably further away from the other two. The algorithm recognizes this, classifies the red point as an outlier, and takes the midpoint of the other two points (yellow) as the new ‘best’ coordinate location.

The facility on the right (Mambote Clinique in Kinshasa), has 4 points that are considered to be within an acceptable distance from the zone de santé. However after computing each point’s distance to the midpoint of the other points, the algorithm considers the green point in the upper left corner to be an outlier and selects as the ‘best’ point the midpoint of the three others, represented by the yellow dot

Our data synthesis approach increases the percent of facilities in DRC with coordinate locations from 35% to 73%, more than doubling the location data contained in the SNIS. The gains were not uniformly distributed – unsurprisingly the biggest improvements came from areas where aid organizations and academics have been most active (and thus we have the most data), such as the provinces of Kasai Central, Kasai Oriental, and Tanganyika.

Improving our knowledge about countries health infrastructures

Although we hope to have humans verify the matching process in the near future, quantitative analysis of the results suggests that the quality of the synthesized data is quite good. The histogram above shows that most facilities have two or more GPS data points contributing to its identified location. Furthermore, the average point that our GPS selection algorithm chooses is just 2 kilometers from the points identified in the matching process.

Using Iaso, our geodata management platform, as a backbone, we were able to combine data from the DRC Ministry of Health and third party aid and academic organizations using a text matching and GPS selection approach to increase the share of health facilities in the national health information system with location information by more than 100%. This work highlights how the platform can be used to compile and synthesize data from different sources to make substantial improvements in how much we know about the country’s health infrastructure. Better information in the hands of international actors and national policy-makers can make operations more efficient, strategy more effective, and improve the health of the Congolese people.

What is Bluesquare’s role in the development of openIMIS?

openIMIS is a free and open-source software developed to accelerate Universal Health Coverage in low and middle income countries. The tool provides a modular open source platform supporting health financing and social protection schemes.

OpenIMIS was originally created using proprietary technologies. The OpenIMIS community decided in 2016 to rebuild the tool in a modular architecture, using open source technologies. The challenge of this reconstruction was to build a new software, while keeping the old OpenIMIS active in places like Nepal where it supports the national health insurance. 

Since 2018, Bluesquare has been leading the architectural migration of openIMIS. The chosen approach is iterative and each iteration delivers a production-ready (partially migrated) software. While much more complex and time consuming, this approach reduces the migration risks of existing implementations to its minimum.

The new modular architecture is based on open source technologies: each country is able to choose the parts relevant to its context, adapt or even replace the ones that don’t fully match its needs… and develop (and eventually share) its own extensions.

openIMIS before Bluesquare

The history of openIMIS started in Tanzania in 2012 when the Swiss Development Cooperation (SDC) was supporting the management of the Community Health Funds’ (CHF) financing programmes. IMIS, the ancestor of openIMIS, was the result of the collaboration of the technical expertise of 3 different organisations: Swiss Tropical and Public Health Institute (Swiss TPH), the Micro Insurance Academy (MIA), and Exact Software.

While the software was primarily developed to be used only in Tanzania, it was soon clear that many other countries/organisations could benefit from it and, together with the German Development Cooperation (GIZ), the tool was brought open source. The objective was clear: build a large community of users, implementers and developers helping each other improve their shared tool. Cameroon and Nepal were the first to join the community by choosing openIMIS for their health financing and social protection programmes. Soon openIMIS was also set up in DRC, Tchad,…

At this point, the (Microsoft-licensed) technology stack and the (monolithic) initial design were identified as major drawbacks for further extensions and a full redesign and rewriting of openIMIS was decided.

Bluesquare as the leader of the architectural design

GIZ hired Bluesquare to define and settle the new architecture for openIMIS with 3 major objectives:

  1. Based on open sourced technologies
  2. Modular to ease (country specific) customisations and extensions
  3. Integrated in OpenHIE landscape

Bluesquare suggested an iterative roadmap, where each iteration would deliver a production-ready (partially migrated) software. In such an approach, each existing openIMIS implementation has a very limited transition risk. Furthermore, the first planned iterations (migrating claim processing and beneficiary enrollment processes) were undertaken in a  “fallback by design” strategy: migrated modules are fully backward compatible with legacy application, and reverting to previous versions is a matter of url activation.

In full awareness of the induced complexity of such an approach, the openIMIS community decided in February 2019 to embrace the journey all together.

The redevelopment of openIMIS

The very first step was to lay down the groundings for all modules and, by May 2019, a running openIMIS based on django (python) and React (javascript) was proxying the legacy application.

Although this (very technical) step had no added value (no new feature,…) for users, its impact on the development community was salvaging: not only its deeply integrated modularity provided the necessary mechanisms to further dismantle the legacy software piece by piece… but also, and nonetheless, it immediately facilitated the contributions of distinct teams with distinct focus to the software.

And indeed, by October 2019, SwissTPH/Soldevelo team delivered a FHIR API module, highlighting the commitment of the OpenIMIS community to integrate the OpenHIE landscape. So far, the FHIR module has been used to prototype integrations with OpenMRS but also DHIS2  and Bahmni. 

In parallel to this new extension, Bluesquare migrated the claim processing as well as locations & health facilities registrations from the legacy software to the new platform.

By December 2019, the new architecture had fully proven its value both for migrating existing features and for its easy flexibility/extensibility. 

In 2020, the development roadmap has been amended to integrate new objectives: the primary focus remains the legacy software migration to be continued where Bluesquare is currently proceeding with beneficiary enrollment migration,  but other teams are also working in parallel on two new modules:

  • AI-based claim fraud detection 
  • formal sector social insurance features

This current phase of work will be released around April 2021.

Bluesquare and openIMIS in the future

The complete migration of openIMIS to the new platform is not finished, and the chosen approach, minimizing risks undertaken by current implementations, leads us to a horizon 2022-2023. Still, openIMIS new architecture has proven to be extensible, customizable and easily integrable with other systems and, as such, is now ready to deliver its value in new context.