The missing link. Using data technology to close the gap in supply chain management

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: 

  1. 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.
  1. 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. 
  1. 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).

Out of stock data can be viewed at the health structure level (Kisamamba health center, DRC).

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. 

Supply chain for medecines and health products in the DRC in 2009

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.       

Risk Based Verification : Simple implementation using Hesabu and DHIS2

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 

Category  CriteriaQuantityverification(Facility Supervisors)Quantityverification(Field officers)
Green Reported within 5% marginof error in the last 2quartersOne visit per quarterto assess 18indicators across 3monthsOne visit per 6months per HF toassess 18indicators across 6months
Amber Reported between 5% and 10%margin of error in the last 2quartersTwovisitsperquarter and verify 36indicators across 3months. Mentoringand focus on rootcause noted.One visit everyquarter per HF toassess 36 indicatorsacross 3 months
RedReported beyond 10%margin of error in the last 2quartersEvery 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 ValueVerified 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.

Hesabu illustration

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. 

Monthly risk assessment on DHIS2 Pivot

Quarterly Risk assessment on Map

Geospatial Data: a Revolution in how we understand the accessibility of Healthcare Services

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.

Car travek Times to nearest facility vs Car catchment areas for facilities

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:

  • The high-resolution land cover maps provided by Sentinel mission.
  • Spatial demographic/population data maps developed by WorldPop.
  • Crowd-sourced geographic information from OpenStreetMap

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.
Official villages vs Detected villages in DRC

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.

Leveraging Digital Health Master Plan for COVID-19

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

To get more information about Bluesquare’s support to COVID-19 response.

Centralizing monitoring and evaluation data for the USAID IHP in DRC

Annotation 0604 (2)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.

  1. Facilitate the data collection and analysis process for the program’s 118 indicators to support the general monitoring and evaluation needs.
  2. Centralize all data collected as part of the program (inventory, household survey, quarterly report, etc.)
  3. 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; 
  • DataViz : to present selected data on a public “user-friendly” interface in order to share the program’s results: https://suivi-evaluation.ihp-prosani.com/data.

Copie de M&E Platform EN

A secure and reliable platform

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.

M&E platform EN-rr-1

 

Data Collect in the DRC: Patient feedback & monitoring socio-economic status

Health RBF programs typically evaluate patient satisfaction quarterly to verify the information declared by health centers about the services offered. Every quarter, a sampling of patients who visit the health facility are interviewed usually by Community-Based Organizations (CBO). Once data has been collected, a “community satisfaction score” is calculated for each provider, which impacts the payment/bonus allocated to that provider. In most countries, this process is paper-based.

Data Collect in Benin: near real-time service availability and readiness data

In early 2016, Benin’s Ministry of Health scaled up its Results-Based Financing (RBF) system. This move has ensured that all health care providers (all public, most faith-based, some private) are surveyed quarterly to externally assess service availability and quality of care. To aid Benin’s government, Bluesquare has been providing strategic support to their RBF data system.