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
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.
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.
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:
Based on open sourced technologies
Modular to ease (country specific) customisations and extensions
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
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.
Have you noticed that wherever you are in the world, you can almost always buy a bottle of Coke? Easier to find than some essential medicines, the secret lies in supply chain management. Every. Link. Matters. Yet in the world of global health, product supply chains are fraught with problems. Particularly in low and middle-income countries (LMICs).
From national warehouses to rural health facilities, for a health system to deliver, a global view of the supply chain ecosystem is needed. At Bluesquare, we know that digital information systems are a vital part of this picture. But integrating and analysing the needs to build an accurate overview of supply chain management is not easy. That’s why we are channelling 8 years of operational experience to close this gap.
Why are supply chain ecosystems so hard to map in LMICs?
Experts in health data management, Bluesquare have identified three core challenges that hinder the management of supply chain data:
Validation requirements: With the transfer of stock comes the transfer of responsibility – and that means procedural and legal expectations around supply chain management are high. Unlike health data, paper-based signatories and processes are still considered the best way to ensure safe transactions in many settings.
Internet access at the point of care: Internet access remains problematic in many LMICs, particularly when it comes to ‘last mile’ healthcare delivery. As a result, many facilities cannot use digital data collection tools, and have to self-report stock levels using paper-based forms.
A lack of interoperability: Where digitisation is possible, facility-level tools rarely align with warehouse management and/or broader health management information systems (including DHIS2, the preferred tool of all our in-country partners). This lack of ‘interoperability’ leaves data sets to exist in silo – making it impossible to build an accurate picture of supply chain ecosystems, or to triangulate logistics data with key health information.
Tackling supply chain data challenges in the field
Despite these challenges, many countries are now looking to digitise paper-based procedure, strengthen and streamline their supply chain management. Proud to be part of this process, in 2019 Bluesquare launched a new partnership with SANRU and Cordaid (supply chain management partners for the Global Fund) in the Democratic Republic of Congo. Working together, we have developed innovative new methods to tackle supply chain data challenges in the field. These include:
The integration of siloed data sets with national health management information systems, in this case DHIS2.
The computation of consistency checks and logistic metrics using our own open-source solution, Hesabu.
Advanced visualization of supply chain data through DHIS2, our new Logistics app and public portal, Dataviz.
Each tool helps closing a critical gap in supply chain data management. And as our partnerships develop we are looking to evolve these solutions even further – focussing particularly on the digitisation and integration of Warehouse Management Systems (WMS).
Innovations to integrate warehouse management
In many countries, including the DRC, it is the end user (primary health facilities) that drive the restocking process. Yet as we have seen, high validation requirements and poor internet connectivity make digitisation extremely difficult. This means our partners were reliant on facility-level, paper-based data. This was then input into digital systems at 15 different regional warehouse sites, with (expensive) interoperability tools linking this information to DHIS2.
Bluesquare’s new urban direct deliveries programme inverts this process – streamlining supply chains by empowering regional warehouses to take the lead. Within this, the increased use of third party logistics contractors creates new opportunity to collect on-site data. Input using offline functionality, information is uploaded at the nearest point of connectivity (for example, on return to the regional warehouse) through mobile devices. Our new product, IASO, that leverages Kobo (ODK) makes it easy to collect and integrate warehouse data with DHIS2. Beyond this, we are also developing an affordable, standard interoperability layer that links open source Warehouse Management Systems (WMS) to DHIS2. Gone are the days of siloed data sets. Bluesquare helps warehouse managers and supply chain partners with easily configurable packages that handle all their data needs.
The missing link. Closing the gap in supply chain management
The result? A digital solution that brings health and logistics data into a single, manageable framework. As our work in the DRC demonstrates, Bluesquare is developing the tools governments and global health actors need to take control of their supply chain and:
Integrate health and logistics data to create an accurate, real-time view of the supply chain ecosystem.
Identify trends and build a supply chain system that responds to population needs.
Understand, calculate and track average monthly consumption rates.
Adjust systems to meet seasonal trends and peak demand.
Track inventory and validate checks to ensure proper usage and prevent corruption.
Forecast and plan to make sure that drugs and supplies are available 24/7.
With our support, users have everything they need to integrate, digitise, track, check, forecast and replenish essential drugs and supplies. For it is only by embracing new solutions in data technology that we can close the gap in supply chain management and move closer to a future that delivers true health for all.
Performance Based Financing(PBF) is an innovative, results-oriented approach that incentivizes providers based on their achievement of agreed-upon, measurable performance targets. Incentives include financial payments, bonuses, and public recognition. In Performance Based Health Financing, monitoring and evaluation is key. Program implementing partners should constantly monitor field activities through verification of field work for them to be able to rationally provide adequate resources where they are needed most. To minimise efforts, avoid wasting resources, reduce cost and time spent performing monitoring and evaluation activities, the Performance Based Financing program in Zimbabwe has introduced a risk based approach on data verification. On the risk based approach, Health Entities are categorised based on how accurately they report services they delivered to clients. The implementing partner will perform a verification of the reported figures and possibly perform a counter verification through an independent partner. When there are discrepancies in claimed and verified figures, for the Zimbabwe PBF program entities are classified into three categories, RED, AMBER and GREEN based on the discrepancy. Through Risk based verification, entities that have higher discrepancy are then given more verification priority than those with lower discrepancies performing well. The table below shows the categories and the verification schedules. Bluesquare has been contracted to build a software system to support the PBF program operations including the Risk Based Verification
RBV Categories and verification schedules
Reported within 5% marginof error in the last 2quarters
One visit per quarterto assess 18indicators across 3months
One visit per 6months per HF toassess 18indicators across 6months
Reported between 5% and 10%margin of error in the last 2quarters
Twovisitsperquarter and verify 36indicators across 3months. Mentoringand focus on rootcause noted.
One visit everyquarter per HF toassess 36 indicatorsacross 3 months
Reported beyond 10%margin of error in the last 2quarters
Every month visit toassess all 18 indicators.Mentoring and focuson root cause noted.
One visit everyquarter to assess all18 indicators for each month
Formula for computing the error margin:
(Claimed Value – Verified Value)/Claimed Value
Each indicator’s monthly error margin is awarded a score and those scores are aggregated and used to categorize the entities
RBV Implementation using Hesabu and DHIS2
At Bluesquare we developed a tool called Hesabu that allows PBF programs to integrate with HMIS systems, not replacing them but instead it enhances them, offering additional capabilities with more advanced formulas, the capacity to use data from sibling or parent org units and sliding periods. Think of it as an Excel sheet running on top of your DHIS2 – you can define formulas, apply formulas to the output of other formulas in order to create a whole chain of transformation and, more importantly, send any result (final or intermediary) back to DHIS2 as a normal data element value.
With this tool, we were able to implement the risk based verification algorithm which included computing discrepancies between claimed and verified values on selected service delivery indicators, awarding risk based verification scores for each entity and finally classifying the entities into the respective RBV categories biannually. Hesabu is of great help on computing and associating a score with each indicator for the six month period, aggregate them to compute the final score for each facility which could not be achieved just with DHIS2 indicators. After performing the RBV score computations and categorization, all the computed results are sent back to the HMIS, in this case DHIS2 for visualisation and reporting.
Visualization in DHIS2
After Hesabu provides the data back into DHIS2, charts and graphs are created to visualise the data on RBV that managers can now use to enhance their operations.
Mrs X was eight months pregnant when she died. There are many reasons for her death, and any one could have tipped the balance. Health education. Family planning advice. Better access to antenatal and facility-based care. It is a familiar story, and one that must still be told*.
The fact is, that even in today’s era of Sustainable Development, 50% of the world’s population live without full access to essential health services. It’s a complex issue. About more than just the availability of health centres and facilities, there are countless variables to consider, including cultural acceptability, affordability and physical accessibility. Focussing on the latter, if the world wants to make universal health coverage a reality, it must build an accurate picture of the situation on the ground. The ‘haves’ and ‘have-nots’ of healthcare access. Experts in health data science and technology, at Bluesquare Hub we believe that geospatial data has the power to unlock this information – and we want to take this chance to explain why.
The limitations of current accessibility models
Traditional measures of physical healthcare access – Euclidean distances and 5km catchment zones – simplify what is, in reality, a far more complicated situation. Evidence shows that service uptake decreases with distance, travel time and other logistical barriers. An expectant mother might live within 5km of her local health centre, but if it’s too far to walk, there is no car, affordable public transport, or the road becomes impassable, then she cannot get there.
It is a reality compounded by the fact that current geospatial models do not give policy and decision-makers the information they need to effectively improve healthcare accessibility. Produced on a per-project basis, methods are rooted in regional population estimates and siloed datasets that are static and imprecise. The results are paper-based reports that simply cannot account for the variables that define healthcare access as experienced on the ground.
In this situation, accessibility measures become theoretical. There is little space for local validation of resulting models, with real-time variations of service availability rarely examined. Yet if ambition towards universal health coverage is to be achieved, we must understand and account for reality. Only then will governments and global health actors have the information they need to drive development forward.
Combining geospatial modelling and Big Earth Data to better understand healthcare accessibility
Leveraging recent advances in geospatial modelling, the team at Bluesquare Hub is working to build digital tools that allow for the automatic and rapid computation of accessibility metrics from multiple data sources. For example, in recent years we have seen the development of several open-access libraries providing essential, high-quality data. These include, but are not limited to:
Capturing variables that influence healthcare access in real-time (topography, road networks, population distribution and demographics), together these sources create a newly available set of ‘Big Earth’ data that can be easily read, updated and integrated into modern accessibility models.
Combining this information with routine health data, BlueSquare Hub joins the Spatial Epidemiology Lab from the University of Brussels and the University of Geneva in new research to ‘productivise’ this process – using machine learning and artificial intelligence to automate near-continuous updates. This combination of advanced geospatial modelling, ‘Big Earth’ and real world data allows users to develop digital accessibility maps that empower them to:
Understand healthcare accessibility down to a local, village level.
Calculate key accessibility metrics in line with local public health priorities.
Accurately map facility catchment areas, population access and travel time.
Focus on specific population subsets and demographics (e.g. women of childbearing age).
Account for socio-economic variables, including ethnicity, religion and household income.
Tailor input data to account for multiple scenarios, including seasonal changes.
With pilot models currently focussed on the Democratic Republic of Congo, Niger and Senegal, for the first time in history, policy and decision-makers can have an up-to-date picture of health service access as it happens on the ground. Not only does this process allow for enhanced accessibility mapping, it makes it dynamic – allowing global health actors to track healthcare accessibility in evolving situations and tailor their response accordingly.
There will be some hard truths to face. With preliminary work in the Republic of Senegal showing 80% of women living with reduced, or no, access to family planning, current understanding around healthcare access will almost certainly shift. It is important that it does. By improving the way physical healthcare accessibility is defined and measured, we can create new opportunities for a data-driven future. One that allows for the smarter allocation of health resources and has the power and potential to revolutionise access to healthcare.
*The story of Mrs X was first told by the WHO in 1988 and retold in 2012. Click here for more information.
For governments to effectively manage the health crisis caused by COVID-19, they must have the ability to make decisions based on reliable and consolidated data. Today, the data that are key in this decision making – specifically in low- and middle-income countries – are those related to the availability and location of the infrastructure and sanitary facilities necessary to meet the needs of their populations. Identifying the infrastructure facilities will allow governments to allocate the necessary funding to the essential health services. During a crisis, time is critical. Governments must be able to rely on what has already been accomplished to improve the information system and health surveillance. Those who started this work before the crisis will have a head start. They will already have critical data at their disposal to manage the crisis. This kind of data management and collection should not stop during the crisis. On the contrary, it is one of the crucial elements of health risk management and mitigation. In the below, based on Bluesquare experience in the DRC and Niger, we describe the approach to leverage Digital Health Master Plan (Carte sanitaire in French) to tackle COVID-19.
Initial and ongoing work to build the Digital Health Master Plan
We have been supporting the DRC and the Niger Ministries of Health in the implementation of an interactive and dynamic health map of public and private health facilities (pharmacies, clinics and health centres).
The results of this work provide routine information, taking into account the health pyramid and population, to the health authorities (the Minister, Technical and Regional Directorates of the Ministry, Health Districts, Projects and Programmes, Partners, external agencies, NGOs, hospitals and health workers). With this information, they can better allocate resources and target underserved regions and populations.
Bluesquare has specifically focused on identifying databases containing GPS coordinates of health structures, with precise information, such as photos and features that are specific to the DRC and Niger. In Niger, we identified 14 different databases, including the one from the SNIS (HMIS).
We also identified new sources of data to help increase the number of GPS coordinates on the health map, based on existing data. In the DRC, we have identified the following databases: Geolocation of health facilities in the Ebola response, a database from MSF/common geographical repository, a database from the PDSS containing the GPS coordinates of about 500 health facilities in Kinshasa and Kasai.
These multiple data sources, combined with a constant change of data (population estimates from remote sensing, settlement location, names of villages and settlements, health areas, health districts, health facilities and “découpage communal”), inconsistency between sources (for example, the health layer does not always correspond to the administrative layer) require continuous integration.
Thanks to our visualisation tool, Dataviz, we have set up public portals to visualise the ongoing work on the health map (carte sanitaire) in Niger and the DRC.
Work that facilitates a quick response to COVID-19
In the framework of the COVID-19 response, countries must quickly identify the infrastructures and services they have at their disposal to address the disease and to target funds to acquire the ones still needed.
In the DRC, where we have been working with the Health Ministry, supporting health information systems and helping the government to create the “carte sanitaire”, we are currently working with the national COVID-19 Task Force to help them tackle the disease.
We are providing support for the geolocalisation of the COVID capacity response (screening devices, in-patient beds, ventilator beds, critical care beds, laboratories, oxygen production units, etc.).
The work that has been done, and which is continuing with the health map (“carte sanitaire”), supports the establishment of an “état des lieux” of the existing infrastructures and services for a COVID response. We are putting in place an android app for the “carte sanitaire” update.
We are also investigating other databases that provide integrated information on the COVID Capacity response in order to consolidate data into a single digital database.
This work, while seemingly low-impact, reveals its importance with the COVID-19 pandemic. Ministries of Health and different stakeholders are even more tempted to work with non-centralised databases, gathering data through excel files and sharing them by email, creating different versions of the same database. More than ever, a centralised digital database, to which the different actors can contribute, is critical.
In the first phase, Bluesquare, in partnership with the Agence Nationale d’Ingénierie Clinique d’Information et d’Informatique de Santé or ANICiiS, is identifying the different existing data flows and databases to have a clear view of the full process (what kind of data exist, how data are gathered, by who, and how often etc..) and inviting the different partners to share any complementary existing databases. When complete, the second phase is to propose a set of tools that take into consideration the actual flow of information, while switching from a decentralised multi-source database to one central tool.
This information will be made publicly available via the public data visualisation interface: “STOP COVID“
The Integrated Health Project in the DRC (IHP USAID)
The IHP USAID program (in French Programme de Santé Intégré de l’USAID en République Démocratique du Congo (PROSANI USAID) ) aims at strengthening the health system in the Democratic Republic of Congo. It is funded by the US Development Agency (USAID) in close collaboration with the government of the country. The program focuses on maternal and child health, family planning, nutrition, malaria, tuberculosis and HIV.
Bluesquare, as the program’s partner for data management and the development of digital tools, has provided support in three key areas.
Facilitate the data collection and analysis process for the program’s 118 indicators to support the general monitoring and evaluation needs.
Centralize all data collected as part of the program (inventory, household survey, quarterly report, etc.)
Implement dashboards that can be made available to program technical advisors.
DHIS2… and more
Bluesquare has therefore developed the Mesure & Evaluation Platform (M&E Platform) for this program. Similar to a conventional DHIS2 platform, it includes some additional applications to support the specific needs of IHP USAID:
D2D: to transfer data in just one click directly from the official Health Management Information System DHIS2 (National HMIS DHIS2) to IHP DHIS2 – especially for all M&E indicators that rely on national data;
Iaso: to transfer Etat des Lieux data to IHP DHIS2;
Hesabu : to calculate the most complex indicators of the logical framework and store the results in the IHP DHIS2;
Access to the M&E Platform is ID and password protected. These can be provided upon request by the M&E team. Each user is assigned one (or more) role(s) based on what they will need to work on in the platform. This approach helps to limit the risk of errors that could cause problems later when using the tool.
In the case of this platform, 4 roles have been defined that can be combined by need and by geographic areas in order to further limit the risk of error (for example: encode data for a province difference from your own): Input, Visualization, Analyst, Superuser.
Data extraction for the development of project evaluation indicators
The source of data
A total of 118 indicators are used to assess IHP USAID’s progress. These indicators are calculated based on data from a variety of sources. The more sources there are for the data the richer the insights we can gather from these indicators. The integration includes mainly the data collected by the Ministry of Health (available in the National DHIS2 HMIS) and some additional data from the project (Project Monitoring Report – PMR data). Thanks to the tools described above, Bluesquare was also able to enrich this with additional data from one-off surveys (IHP Household Surveys or data being collected by the Ministry of Health to evaluate the status of the health system at any given time called “Etat des lieux”) as well as other external surveys and disease specific databases.
Ensure a very large amount of data extraction
Using the D2D tool, Bluesquare will extract these different data and merge them with IHP DHIS2 data.
To provide a sense of the process to manage such complex routine data integration we herewith provide an overview of the transactions and some data security measures put in place to ensure quality data exchanges.
Data from the National health information system (HMIS) are imported quarterly from a copy of the DHIS2 SNIS (so as not to endanger the proper functioning of the real DHIS2 HMIS during the data transfer). This also means that the HMIS data is not updated on a daily basis and thus remains “fixed” once imported into the platform which allows for methodological consistency to be maintained in trends analysis. The HMIS DHIS2 remains an essential data source for day-to-day data analysis and to observe “absolute” figures.
Data from the Project Monitoring Report (PMR) are added directly to the M&E Platform via data collection forms available on a monthly, quarterly or annually basis depending on the monitoring needs of the M&E team.
Data from État des Lieux (EDL) This data is collected in a tool called IASO, that collects and manages the data in parallel to the DHIS2.
The remaining data (IHP Household survey and other sources) are manually imported into the IHP DHIS2 by the Bluesquare team.
How the platform will be implemented going forward
The M&E Platform is now being used for ongoing reporting.
To ensure the tools meet the user needs, run smoothly and capture data effectively, the M&E leads will test the PMR data entry forms on a provincial level. This evaluation of user needs will play a key role in the next iteration of potential improvements to ensure the quality of the data captured for the program monitoring remains consistent.
Additional dashboards will also be developed by the Bluesquare team to help M&E teams capture and visualize the essential information on the definition of indicators.
And the M&E platform itself will continue to be adapted thanks to ongoing dialogue and discussions on the tool, its use and its effectiveness to be sure it continues to meet the needs of the IHP USAID team.
We have had 6 busy months since January and we have plenty of new features to announce!
New – portal homepage
To provide visitors with a clear introduction and overview of the site, we have added a fully customisable homepage to all portals. You can now present the program objective, data collection process, partners, useful links, publications and more directly on this page.
View a facility’s categorical data
Using the DHIS2 organisation unit groups, we are now able to show the ownership of each facility to one or multiple groups on the map.
When you zoom out of the map, the statistics for that zone appear as you hover over them.
Additional options to visualise your data
In order to respond to the growing diversity of our clients’ needs, we added a few new ways to visualise your data.
Best used to compare current values of connected indicators.
True / False key indicators visual improvements
Perfect for visualising service availability like water/electricity access or stock out monitoring.
Monitor resources over time with this widely requested chart type option. Define your own color scale for each indicator using color legends set in DHIS2.
Inaccuracies reporting tool
We added this very simple tool that allows someone to easily report issues regarding the data for any facility displayed. It opens a mail with all the facility details and allows the person to explain what and where the issues are.
Improved mobile experience
We noticed an increasing number of users viewing the portals on mobile devices. With this a need to easily report inaccurate information via mobile is growing. So we have focused on improving the user experience for mobile.
Map layers are now grouped and easy to switch
Download a graph as an image
That’s all for now but do not hesitate to reach out with ideas or feedback on your Dataviz instance. We are happy to support your programs with this tool.
Sleeping Sickness is a parasitic disease transmitted by the tsetse fly. It is lethal in most cases and has been an important cause of death in sub-Saharan Africa over the past century.
Professor Marleen Boelaert and her team from the Institute of Tropical Medicine (ITM) in Antwerp, Belgium, recently launched a new project to reach the global targets for eliminating sleeping sickness. ITM researches new ways to combat the disease and is in charge of an international elimination initiative in DRC, financed by the Belgian Development Cooperation and the Bill & Melinda Gates Foundation. They are working with several partners. One of them is PNLTHA, the National Program against Sleeping Sickness in the DRC.
The project is focused on improved medication and testing, smaller and more effective fly traps and on digital data processing. You can read more about the ITM project here:https://www.itg.be/E/sleeping-sickness
Bluesquare has been working with ITM to develop and enhance the existing data processing tools, which includes two main parts:
A mobile application
The mobile application is used on Android tablets to collect data about diagnostic tests that are performed in villages in DRC.
The paper based processes that have been in use, with good results, in the DRC over the years, have the drawbacks of requiring that all the information be gathered physically at a central level and letting spelling errors in names of places slip through. These factors make it more difficult to produce geographically grouped reports.
The mobile application replaces those processes and provides multiple advantages:
It enforces improved encoding by for example providing prefilled lists of places and checking that encoded ages are realistic.
It allows transfer of data over the internet when connectivity is available, avoiding difficult travel for the PNLTHA teams across the country, and drastically shortening the time to collect data centrally.
It can take pictures and videos (through microscopes) of test results. These are taken to double check the results at health zone, provincial or national level, in order to increase the quality of the testing.
These advantages are very important and justify the use of digital tools over the traditional paper approach, but the use of a tablet application has its own challenges to overcome.
First, it requires electricity. This has been solved by using solar panels.
Second, it needs to work offline, with the absence of internet connection in most of the DRC. Then when the tablet user reaches a place where internet connectivity is available, it needs to be able to synchronize all the collected data with a central server, securely.
Third, the process must not break the workflow of existing testing teams, and be, whenever possible, as convenient as the paper based processes.The teams are used to working in parallel with encoders for the patient names, with the testers when collecting blood and finally with the verifiers to proceed to additional tests in case of a first positive diagnostic test. We are tackling these problems by using NFC and Bluetooth synchronization between tablets to avoid multiple encoding at the different stages of the process. The advantage of these technologies is that they do not require internet connectivity to transfer data between devices.
An online dashboard
The dashboard offers many features aimed for use by the PNLTHA members.
First and foremost, it collects all the data about the tests performed in the field, that have been encoded either through the mobile application or through various Microsoft Access files over the years. This makes it a comprehensive electronic record of all recent sleeping sickness cases in the DRC. Our team worked hard to ensure that this data are as clean as possible, allowing us to match cases with the location where they have occurred and been diagnosed, and to ensure that no case is either forgotten or encoded twice. We are also making progress on making links between various tests performed on a given patient.
In the end, the keyword here is traceability: we want to allow users to easily recover any information on a test that has been performed. For example, in which village, by which PNLTHA member and on exactly which date. Dates and GPS coordinates are collected by the tablets during encoding and are used in the dashboard to navigate test information.
Second, it includes tools to track the progress of the testing work in the field, by displaying which tablets have been doing which test, when and where. Statistics about the tests campaign can be viewed online in these tools.
Third, in the very near future, the dashboard will allow us to proceed to quality controls at various levels, by allowing officials of the PNLTHA to see pictures and videos of tests completed in the field and double check the diagnoses that have been performed.
Fourth, the dashboard offers tools to plan the work of testers in the field, while optimizing the travels of the teams and the epidemiological efficiency. This is crucial in the last steps of elimination of the sickness, where tracking down the last cases requires a level of accuracy that was not really needed in the past, when more broadly cast surveillance networks have done their job efficiently.
All these features are provided while ensuring a fine grained access control where all users of the systems only get access to the parts that are relevant to their work responsibilities. Notably, they only have access to the geographical regions that they are in charge of.
Tools used (for the technology wonks out there):
For the android application, we use Cordova, React, and various libraries for NFC, USB storage (for backup) and external USB camera support. The data is stored in Couchdb, which allows relatively easy replication between devices and the servers.
The online dashboard is written in Python using the Django framework, with the frontend using React, Leaflet for the maps, and d3.js for data visualization.
We are making progress on all aspects of the digital tools for the elimination of the sleeping sickness in the DRC project. Overall, we seem to get a good adoption rate of the tools by the different teams, which is always a major challenge for any digitization project. This is attained through constant feedback loops with people in the field and rapid adaptation of the tools to the needs expressed. Already, hundreds of thousands of tests have been recorded and made available for statistical analysis.
We would like to thank the members of the PNLTHA for their willingness to test the tools and to give feedback and the ITM for trusting us with this important and challenging project.