🤖 AI Generated Summaries for each #DAC2025 session + Deep Dive Audio for Day 3

Resources - Ressources
1

In this dac2025 AI Summaries series we will provide you with:

  1. :headphone: Deep Dive Audio AI Summary for the entire conference day
  2. :memo: Summary for each session (ordered in the same sequence as in the agenda).

Please note that it’s possible that these AI generated summaries contain errors; therefore, this post has been made as a wiki so that necessary modifications can be made by other community members especially speakers.

Thank you, and enjoy! :grin::+1:

:placard: AI Generated summaries for #DAC2025 sessions Day 1
:placard: AI Generated summaries for #DAC2025 sessions Day 2
:placard: AI Generated summaries for #DAC2025 sessions Day 3 :left_arrow: (You’re here! :slight_smile: )
:placard: AI Generated summaries for dac2025 sessions Day 4 (soon)

:headphone: Day 3 Deep Dive (AI Audio Summary):

NOTE: This AI audio is an experimental feature using Gemini, so this audio deep dive (including the voices) are AI-generated and there might be inaccuracies and audio glitches. This audio is not a comprehensive or objective view of a topic, but simply a reflection of the YT videos and other material from the conference. We simply hope that you enjoy the audio overview of the whole day and that it sparks your curiosity!

Plenary Sessions:

DHIS2 Quiz 🚀 The "DHIS2 Quiz" session kicked off Day 3 of the DHIS2 Annual Conference 2025 with an exciting and interactive **Kahoot quiz**! 🚀 This session, held from 08:30 to 09:00, was led by **Karoline Tufte Lien** @Karoline, DHIS2 Product Manager at HISP Centre, UiO, and aimed to test participants' knowledge and foster engagement. It was open to both in-person and online attendees, featuring a mix of questions from previous sessions and general fun facts.

Here are some highlights from the quiz questions and answers:

  • Global Presence & Community :globe_showing_europe_africa::

    • A total of 70 countries were represented in person at this year’s conference.
    • A remarkable 267 abstracts submitted for the conference were reviewed by Bob.
    • The conference officially changed its name from “Expert Academy” to “Annual Conference” in 2019.
    • “DHIS2” is no longer an acronym; it just stands for DHIS2 now.
    • The Community of Practice (CoP) saw a significant growth with 3,500 new members joining in the last year, and Gazim, the CoP coordinator, had the most liked post.
    • The country with the largest population currently using DHIS2 at a national scale is Indonesia.
    • Cambodia was the most recent country to start using DHIS2 as its Health Management Information System (HMIS).

  • DHIS2 Technical Updates & Features :gear::

    • In DHIS2 v42, several apps and endpoints were removed but replaced, including the legacy tracker endpoint, legacy data entry app, and tracker capture.
    • For those curious about DHIS version history, Kala’s favorite DHIS version was 1.4.

  • Health & Climate Focus :thermometer::

    • Malaria was identified as a climate-sensitive disease often monitored in DHIS2.
    • Among climate events, heat waves were noted for having direct health impacts.

The quiz also included a fun fact about the sunset time in Oslo on the conference day, which was 10:39 PM :sunrise:.

The session concluded with congratulations to the top performers, particularly Megamind (Eric Chingalo @ericchingalo, an online participant), who was recognized as the “real winner” among non-UiO attendees :tada:. @Ameen and @DavidCKen were also among the top scorers. Participants were encouraged to look forward to the next annual conference! :blush:.

DHIS2 in National Architecture🗺️ The session on "**DHIS2 in National Architecture**" 🗺️, held on Day 3 from 09:00 to 10:20, delved into how DHIS2 fits into national health information systems, focusing on crucial aspects like sustainability, resource management, and standards. The session featured a distinguished panel including **Ms. Aishath Samiya** from the Ministry of Health, Maldives; **Dr. Salomatu Land Dogma** from Ghana Health Service; **Dr. Adam** (Edem Kossi) from HISP West & Central Africa; and **José Costa Teixeira**, a Standards Advisor from PATH.

Here’s a breakdown of the key discussions and insights:

:glowing_star: Maldives’ Digital Health Journey

Maldives has integrated DHIS2 as a core component of its national health strategic action plan since 2019.

  • Phased Rollout :rocket:: Initially, it supported aggregated data reporting from all levels (islands feeding data to atolls, then to the Ministry of Health).
  • Individual-Level Tracking :busts_in_silhouette:: Since 2022, they’ve transitioned to individual patient-level data tracking for various modules, including immunization (across public and private sectors), Non-Communicable Diseases (NCDs) for those over 18, lymphatic filariasis (due to an outbreak), and tuberculosis, among others.
  • Strong Governance & Ownership :handshake:: The success is attributed to strong local capacity building, moving from external dependence to internal expertise. Core working groups, involving program staff, technical experts, and health information teams, are engaged from conception through implementation and monitoring, ensuring changes are vetted and applicable locally.
  • Key Messages :white_check_mark:: Start small, scale smartly, engage partnerships, and foster local ownership and oversight.

:ghana: Ghana’s Decade of DHIS2

Ghana has been leveraging DHIS2 since 2012, making it the nationwide Health Management Information System (HMIS) and the official repository for health service data, covering all 261 districts and over 11,000 facilities.

  • Unified Data Flow :bar_chart:: DHIS2 aggregates both facility-reported summaries and eTracker individual data, ensuring a unified flow from community clinics to the national dashboard.
  • Key Achievements :tada::
    • Single Source of Truth :white_check_mark:: Eliminates parallel systems and ensures reliable data.
    • Culture of Data Use :chart_increasing:: Dashboards and scorecards empower decision-makers at all levels.
    • Programmatic Gains :flexed_biceps:: Improved tracking in Maternal and Child Health (MCH), HIV, Tuberculosis (TB), and immunization.
  • Challenges :construction:: Connectivity and device limitations, reliance on parallel paper systems (leading to duplication and fragmentation), staff turnover affecting user adoption, data quality gaps, and interoperability/sustainability issues due to manual reconciliation and donor dependence.
  • Future Directions :right_arrow:: Nationwide eTracker rollout by 2026 for MCH, HIV, TB, and immunization; expansion of the Electronic Immunization Registry (eIR) linked with birth registration; and development of a Health Information Exchange (HIE) to link DHIS2 with logistics, labs, and surveillance systems.

:gear: Architecture, Interoperability, & Standards

A significant portion of the session focused on the broader challenges and opportunities in health information system architecture.

  • Evolution of Interoperability :globe_with_meridians:: Discussions highlighted the journey since early 2000s, from failed attempts at health suites to initiatives like OpenHIE, FHIR, OpenHIM, and OpenFn.
  • FHIR’s Role :link:: FHIR (Fast Healthcare Interoperability Resources) is seen not just as a technical standard but as a foundation for digital health, an engaged community, and a tool to standardize interoperability. It’s crucial for establishing national data architectures by exposing DHIS2 metadata in a standard, machine-readable format.
  • “Architecting” as a Process :construction_worker_woman:: The concept of “architecting” was emphasized as a continuous process rather than a final product. It involves starting small, learning, and adapting what is available to address pain points, rather than waiting for a “perfect” system.
  • Challenges in Interoperability :chart_decreasing:: Lack of clear national digital health policies, fragmented ownership, heavy reliance on donor funding (affecting long-term sustainability), and concerns about data security and privacy remain significant challenges.
  • Community Building :houses:: Building a community that includes ministries, vendors, universities, and hospitals is vital for driving standardization and sharing experiences.

:hammer_and_wrench: DHIS2 Core Teams & Capacity Building

The importance of DHIS2 Core Teams was highlighted as central to sustainable implementation and evaluation.

  • Local Ownership :light_bulb:: These teams, preferably full-time, manage DHIS2 projects and initiatives, ensuring local customization and scale-up, and are crucial for fostering local ownership.
  • Capacity Assessment Toolkit :straight_ruler:: A standardized toolkit helps identify and track learning gaps among team members (Operational Leads, Program Implementers, Technical Implementers, Trainers). This facilitates planning individual and team learning paths, justifying budget requests to partners, and assigning responsibilities based on skills.
  • Ghana’s Experience :graduation_cap:: Ghana utilized this assessment to strengthen its core team, emphasizing continuous training over one-off sessions, aiming for long-term ownership and localization.

This comprehensive session underscored the dynamic nature of health information systems, the critical role of DHIS2 as a national backbone, and the continuous efforts needed in governance, standardization, and capacity building to achieve integrated, data-driven health outcomes.

Parallel Sessions:

Tracker to aggregate📊 The session on "**Tracker to aggregate**" 📊, held on Day 3 of the DHIS2 Annual Conference, focused on a crucial aspect of DHIS2 implementation: how to transform detailed individual-level tracker data into summarized aggregate data. This conversion is vital for various reasons and has seen significant improvements, especially with the introduction of **Program Indicator Disaggregations** in DHIS2 v42.

Here’s a breakdown of the key insights from the session:

What is “Tracker to aggregate”? :chart_increasing:

“Tracker to aggregate” specifically refers to the process of taking aggregate counts from DHIS2 tracker programs and storing them as data values in the aggregate data model of DHIS2. While tracker data can be analyzed in other ways (like Event Report/Visualizer apps or directly using program indicators), this session emphasized the need to store aggregated values for institutional reporting and broader analysis.

Why is it important? :thinking:

Storing tracker aggregates is often required because:

  • Integration with Existing Systems :handshake:: Tracker data often needs to be part of a larger health information system where most data is already aggregated.
  • Triangulation :straight_ruler:: It allows for combining tracker data (like vaccine-preventable disease surveillance or immunization registries) with other aggregate data sources (such as vaccine stock data or population estimates) for a fuller picture.
  • Official Reporting & Analysis :white_check_mark:: Aggregate reports are frequently an institutionalized part of official statistics, requiring sign-off and approval at various levels. Tools for aggregate data analysis are often more mature and performant.
  • Unified View :globe_showing_europe_africa:: Transforming tracker data into aggregate values allows it to be used within the established Health Management Information System (HMIS), enabling analysis alongside non-tracker data and leveraging powerful analytics tools.

The Recommended Approach :sparkles:

The supported and built-in way to achieve “Tracker to aggregate” in DHIS2 without relying on custom tools involves:

  • Program Indicators (PIs) :light_bulb:: These define how individual data from tracker programs is counted and linked to aggregate data elements. With version 42, Program Indicator Disaggregations significantly reduce the configuration complexity and improve performance.
  • Data Exchange App and Service :counterclockwise_arrows_button:: This defines the periodicity (how often) and the organizational unit level at which data should be aggregated.
  • Scheduler App :alarm_clock:: This allows you to set up how often the aggregations should actually run.

The process typically involves raw tracker data elements, enrollment data, and tracked entity attributes feeding into tracker analytics tables, which then serve as the source for program indicators. These are processed by the Data Exchange Service, converted into aggregated data value sets, and re-imported into the aggregate data value tables within DHIS2 for reporting and visualization.

Key Innovations and Improvements :rocket:

  • Program Indicator Disaggregations :star_struck:: This new feature, introduced in DHIS2 v42, is a game-changer. Instead of creating separate program indicators for every combination of categories (e.g., age and sex), you now need only one program indicator for each piece of logic, with all disaggregations defined in one central place within the program metadata.
    • This vastly reduces the number of program indicators needed (e.g., from 621 to 27 for Ghana’s HIV system).
    • It also improves dashboard rendering speed by fetching fewer, albeit more complex, program indicators.
  • Improved Performance :high_voltage:: The new disaggregations significantly boost performance for data fetching and transfer. In Ghana’s tests, data fetching was 12.5 times faster, and the entire transfer was 5.75 times faster using the new PI disaggregation compared to the old method.
  • Built-in Tools :hammer_and_wrench:: The Data Exchange API and app now provide built-in support within DHIS2 for scheduling tracker to aggregate jobs, eliminating the need for external scripts.
  • Organization Unit (Org Unit) Sync :world_map:: A reference implementation for syncing organization units across different DHIS2 instances has been developed, addressing challenges when moving aggregated data between separate tracker and aggregate instances.

Challenges and Future Directions :construction::right_arrow:

While significant progress has been made, ongoing challenges include:

  • Edge Cases for Data Modification :no_entry:: A minor issue exists where the Data Exchange service might not delete previously aggregated values if a count becomes zero. This is being worked on.
  • Overall PI Performance :turtle:: Although disaggregations help, the general performance of complex program indicators still needs improvement, and solutions are being explored, including dedicated analytics backends for v43.
  • Complexity of Option Set Mapping :link:: For data elements with a large number of options, defining mappings for each option can still be complex and requires manual duplication between option sets and category options.

The community is encouraged to submit ideas for new features to ensure development is driven by user needs. Upcoming plans include further enhancements and continuous refinement based on user feedback.

This session highlighted DHIS2’s commitment to making data aggregation from trackers more efficient, reliable, and user-friendly, paving the way for better data-driven decision-making in national health systems. :globe_with_meridians::white_check_mark:

Lightning talks: Immunization and health planning⚡️ The "Lightning talks: Immunization and health planning" session ⚡️ brought together experts to share concise updates and innovations across three key areas: immunization, nutrition, maternal and child health (MCH), and health planning.

Here’s a quick recap of the insights shared:

:syringe: Empowering Health Officials: DHIS2 and Immunization (UNICEF)

This talk by Fabrizio Giovanni Vaccaro from UNICEF Uganda focused on improving immunization data quality and use.

  • Challenge :construction:: The session addressed issues like poor numerator data quality, including outliers, missing data, and implausible relationships between indicators in routine administrative data. Population estimates derived from census data, updated annually with projected growth rates, often don’t accurately reflect current population dynamics.
  • Solution :light_bulb:: UNICEF in Uganda developed Excel templates by region to collect feedback from the Ministry of Health to improve data quality. They also utilize various algorithms and statistical approaches to automatically identify facility-level data quality issues, addressing them through country consultations and feedback.
  • Methods :gear:: Five distinct methods for outlier detection were applied to monthly health facility immunization service data, capturing data variability and potential anomalies. They also explored alternative denominators, such as DTP1-derived denominators, to address inaccuracies in population estimates.
  • Impact :tada:: Near real-time monitoring enabled timely identification and resolution of issues. Automation was key to producing monthly, quarterly, and annual actionable data products at national, regional, district, and health facility levels after intensive data quality improvements. This helps identify root causes and enables appropriate actions at the grassroots level.
  • Challenges in Implementation :warning:: These efforts require intensive country engagement, collaboration from national to facility levels, and can face competing priorities and low motivation due to lack of incentives.

:red_apple: Integrating Nutrition and MCH Information Systems (Indonesia)

Taufiq Sitompul from HISP Indonesia presented on integrating nutrition and Maternal and Child Health (MCH) information systems.

  • Context :world_map:: Indonesia has been using DHIS2 as its national Health Management Information System (HMIS) since 2018 for reporting, dashboarding, and data warehousing. Currently, 13 health information systems are used for nutrition programs, owned by the Ministry of Health, with DHIS2 centrally hosted along with these applications across over 500 districts and 12,000+ primary healthcare facilities.
  • Challenge :puzzle_piece:: The primary challenge identified was fragmented data across these numerous systems.
  • Solution :white_check_mark:: They implemented an API integration process where multiple existing Health Information Systems (HIS) provide an API for data extraction, which is then mapped and transformed into DHIS2 data entry format by an API service, and automatically updated into DHIS2.
  • Benefits :sparkles:: This approach requires no changes in existing data entry at health facilities, enables automatic consolidation into a single dashboard, and leads to improved data quality and consistency.
  • Impact :globe_with_meridians:: It provides near real-time access to information for stakeholders, supports monitoring and evaluation across multiple programs, and contributes to better health outcomes through unified health data. This is seen as a crucial step towards a nationally integrated HIS in Indonesia.

:memo: Empowering Health Planning in Ethiopia with DHIS2 (HISP-Ethiopia)

Melaeke Serawit presented on leveraging DHIS2 for health planning in Ethiopia.

  • Background :scroll:: Ethiopia previously relied on an Excel-based planning system since 2007, which resulted in long planning cycles (over 4 months), poor alignment with routine HMIS data, manual errors in target calculations, and inconsistent baselines. This highlighted a critical need for a digital solution.
  • Approach :hammer_and_wrench:: The solution involved an agile development approach with continuous stakeholder engagement and iterative phases of needs assessment, application development, and refinement. The app was developed within the national HMIS.
  • Key Features :light_bulb:: The developed DHIS2 Planning App includes both aggregate and non-aggregate analysis modes, and supports hospital planning. It automatically updates baselines whenever performance changes.
  • Results :trophy:: Implementation significantly reduced planning cycle time from over 4 months to weeks. It also led to improved plan quality and efficiency, enhanced data accuracy (due to automation), better resource allocation aligned with national priorities, and enhanced decision-making through real-time tracking of targets.
  • Achievements & Next Steps :rocket:: DHIS2 has streamlined health planning and strengthened data-driven decisions, aligning with national health strategies. Future plans include user-driven enhancements and continuous training for sustainability.

Overall, these lightning talks showcased how DHIS2 is being innovatively used to tackle core public health challenges, from improving data quality and integration to streamlining national health planning processes.

Digital Public Goods and Infrastructures (DPG/DPI)🌐 The "Digital Public Goods and Infrastructures (DPG/DPI)" session 🌐 was a core highlight, showcasing how open-source solutions are driving positive change and fostering collaboration in digital health. The session was introduced by Pamod Amarakoon from the University of Oslo's HISP Centre, which coordinates DPG and DPI activities, and featured Liv Marte Nordhaug, CEO of the Digital Public Goods Alliance (DPGA), along with a panel of DPG product owners.

Here’s a visually engaging summary:

What are DPGs and DPIs? :thinking:

Digital Public Goods (DPGs) are open-source technologies that advance the Sustainable Development Goals (SDGs), are open by design (licensing, documentation, platform independence), and aim to “do no harm”. Digital Public Infrastructure (DPI) refers to solutions and systems that enable essential, society-wide functions. The DPGA is a UN-endorsed multi-stakeholder initiative formed to accelerate international digital collaboration for SDGs, addressing issues like duplication and fragmentation in development efforts. DHIS2 itself is a recognized DPG, having been foundational for the concept of digital public goods due to its long-standing open-source nature and practical impact.

Key Themes & Insights :light_bulb:

  • Community Focus :handshake:: The session emphasized moving beyond individual products to focus on the broader community of digital public goods. This includes fostering local ecosystems and leveraging existing capacities like the HISP network.
  • Local Ownership & Capacity Building :flexed_biceps:: There’s a strong push to encourage local companies to be competitive in contracts and to stimulate local creation capacity. DPGs are seen as crucial tools for local capacity building, with a focus on proactively strengthening local familiarity with relevant DPGs through documentation, training, and certification. The academic connection, particularly PhD programs, plays a vital role in long-term capacity building and fostering implementation research.
  • Addressing Fragmented Data :puzzle_piece:: Many countries, like Mali with its 53 applications financed by various donors, face fragmented digital health information systems. Indonesia highlighted its success in integrating 13 existing health information systems for nutrition and Maternal and Child Health (MCH) into DHIS2 via an API integration process, creating a centralized dashboard without disrupting existing data entry workflows. This offers near real-time access and improves data quality.
  • Real-world Use Cases & Integrations :rocket::
    • Automated Birth Registration & National IDs in Ghana :baby::id_button:: OpenFn, a DPG for data integration, is connecting DHIS2 with identity platforms and birth/death registration authorities. When a birth is registered in DHIS2 (even outside hospitals), OpenFn automatically sends details to the ID authority to generate a national ID and to the birth/death registry for a birth certificate. This information is then sent back to DHIS2, and an SMS is sent to the mother, reducing the need for travel to government offices and providing immediate access to social and health programs.
    • Comprehensive Patient Journey :world_map:: A fictional yet illustrative example demonstrated the seamless flow of data across multiple DPGs: a child’s birth registered in Community Health Toolkit (CHT), notifying DHIS2 or OpenCRVS (Civil Registration and Vital Statistics); ID enrollment in MOSIP (Modular Open-Source Identity Platform); social protection enrollment in OpenSP, verifying identity with MOSIP and birth certificates from OpenCRVS; and health check-ups in CHT, syncing to DHIS2 via OpenFn, leading to aggregated reporting and potentially triggering cash transfers via OpenSP based on program compliance (e.g., vaccination status).
    • Streamlining Data Entry with Journal Input :writing_hand:: In countries like Ukraine, Kyrgyzstan, and Tajikistan, where social workers use paper journals in the field, a new DHIS2 embedded app allows for batch/journal entry. Users can input hundreds of rows of data quickly, reducing mistakes and integrating with DHIS2’s organization unit tree and security.
    • Credit Management System Integration :money_bag:: DHIS2 is being adopted to manage Village Savings and Loan Associations (VSLA) in Mozambique, allowing for mobile data capture of routine operations and web-based mapping, key indicator capture, data analysis, and reporting. This improves transparency, offers timely information, and enables remote monitoring and data security.
  • Challenges & Future Directions :construction::right_arrow::
    • Integration Complexity :chains:: A common challenge is focusing too much on the technical “how” rather than the actual needs and value for end-users and government leadership.
    • Financial Sustainability :money_with_wings:: Heavy reliance on donor funding for initial setup poses a challenge for long-term maintenance. There is a need for a grant component large enough to bolster the public interest landscape and drive DPG product development and prioritization, avoiding a “zero-sum game” mentality where DPGs compete for scarce resources.
    • High-Income Country Adoption :globe_showing_europe_africa:: There’s an ambition for developed countries to adopt DPG technology, which would solidify the maturity of the technology itself and provide assurance to lower-income countries.

The session highlighted the critical role of collaboration, strong governance, and ensuring that interoperability efforts provide tangible benefits to healthcare workers and policymakers to achieve better health outcomes.

DHIS2 skills assessment and learning paths🗺️ The session "DHIS2 skills assessment and learning paths" 🗺️ dove deep into building and sustaining local expertise in DHIS2. It highlighted how a structured approach to **capacity building** is crucial for long-term ownership and effective system implementation.

Here’s a quick tour of the key insights:

:bullseye: What is a DHIS2 Core Team?

A DHIS2 Core Team is a local group responsible for managing the planning, implementation, and evaluation of DHIS2 in a country. Their role goes beyond just using the system; they are meant to drive the implementation forward, constantly learning new features, developing guidelines, and securing funding. This framework aims for long-term, sustainable capacity by recognizing that DHIS2 skills are complex and require strategic investment in local staff.

:hammer_and_wrench: The DHIS2 Core Team Assessment Toolkit

A standardized toolkit was introduced to identify and track learning gaps within these core teams over time.

  • Roles Assessed :man_technologist:: The assessment focuses on four key roles: Operational Lead, Program Implementer, Technical Implementer, and Trainer.
  • Methodology :straight_ruler:: It involves a self-assessment questionnaire, which can be self-administered or guided by a supervisor, to score competencies from “Not yet achieved” (0) to “Mature” (3). Results contribute to both individual and team profiles.
  • Purpose :light_bulb:: The assessment helps to:
    • Identify gaps in skills and knowledge.
    • Justify budget requests for training and capacity building to partners.
    • Plan individual and team learning paths, including identifying appropriate job descriptions and retaining staff.
    • Align responsibilities with existing skill sets.
    • Foster long-term ownership and localization.

:globe_showing_europe_africa: Real-World Experiences & Lessons Learned

  1. Ghana’s Journey :ghana::

    • Ghana began using DHIS2 in 2012, and it now serves as the country’s official repository for health service data, covering all 261 districts and over 11,000 facilities.
    • The Ghana Health Service manages DHIS2 implementation, making it the backbone for reporting.
    • They conducted a quick self-assessment for their 12-person core team. While initial results were viewed with some caution due to limited pre-assessment orientation, the exercise was seen as “very, very useful” for strengthening the core team and supporting sustainability.
    • Key Achievements: DHIS2 has become a single source of truth for health data, fostering a culture of data use with dashboards empowering decision-makers at all levels. They have consolidated vertical program reporting, improving data quality and reducing fragmentation. Tracker systems, like the MCH eTracker and Immunization eTracker, are driving client-level innovations and building unified longitudinal records per patient.
    • Challenges: Included connectivity and device issues, reliance on paper leading to parallel systems and duplication, user adoption challenges due to staff turnover, and data quality gaps.
    • Future Directions: Ghana plans for a nationwide Tracker rollout by 2026, expanding the Electronic Immunisation Registry with birth registration and SMS, developing Health Information Exchange (HIE) for interoperability, and enhancing data use. They also aim for budgeted system ownership to ensure long-term resilience.

  2. Lao PDR’s Experience :laos::

    • Lao PDR has been using DHIS2 as its national HMIS since 2015.
    • Their core team assessment was part of a broader DHIS2 Maturity Assessment, involving the Ministry of Health, WHO country office, and the DHIS2 Core Team.
    • Observations :face_with_monocle:: Operational leads had an early understanding of proposal development and budgeting but limited financial forecasting skills. Program implementers showed strong skills in dashboards and data visualization but gaps in custom reports and tracker data quality. Technical implementers were strong in configuration but lacked proficiency in advanced data quality features and predictive techniques. Trainers were proficient in aggregate workflows but needed more clarity on tracker workflows and localization of training materials.
    • Recommendations :handshake:: Included structured workshops on DHIS2 proposal writing, staff work plan creation, formalizing quarterly partner coordination meetings, hands-on training in various DHIS2 features, and promoting peer-learning and mentorship within the team. They also suggested enrolling in DHIS2 online courses for budgeting and planning.

:right_arrow: Key Takeaways for Sustainability

The sessions underscored that sustained capacity building is paramount, moving beyond one-off training events. Effective implementation also requires stakeholder buy-in and a clear understanding that the assessment is for improvement, not punishment. Continuous assessment and adaptation are essential to address evolving needs and ensure DHIS2 remains a powerful tool for health transformation.

Lightning Talks: Research (Part 2) 🎤 The "Lightning Talks: Research (Part 2)" session 🎤 showcased cutting-edge studies using DHIS2 data, highlighting its versatility for research in health systems and beyond. The session featured three insightful presentations:

:baby::chart_decreasing: COVID-19 Lockdowns and Fertility (Karen Grepin, University of Hong Kong)

This research explored the impact of neighborhood-specific COVID-19 lockdowns on fertility and births in Kinshasa, Democratic Republic of Congo.

  • Key Finding: The study found strong effects of lockdowns on fertility, noting that many births were likely unintended, especially among women under 20.
  • DHIS2’s Role: DHIS2 data, even “basic” count data, proved valuable in detecting these significant changes, which might not be captured by traditional household surveys due to sample size limitations. This demonstrated that a lot can be done with a pretty basic signal from DHIS2, challenging perceptions of its data quality for research.
  • Policy Implications: The findings underscore the need to integrate responses to such demographic shifts into planning for future pandemics.

:cloud::woman_feeding_baby: Climate Hazards and MNCH Service Delivery (Yoshito Kawakatsu, UNICEF)

This presentation focused on the impact of climate hazards on Maternal, Newborn, and Child Health (MNCH) service delivery.

  • Data & Methodology: Researchers utilized DHIS2 monthly health facility data from January 2018 to December 2024, focusing on Ethiopia, DRC, and Ghana. They applied mixed-effect linear regression models to analyze how climate hazards affect health services like antenatal care, deliveries, and vaccinations.
  • Key Findings: In Ethiopia, both severe and moderate rainfall were associated with a decline in the utilization of essential health services. A greater distance to paved roads from health facilities was also linked to reduced access and use of multiple services, especially when combined with rainfall intensity.
  • Visualization: Geospatial risk maps were generated outside DHIS2 using its API data, allowing for visualization of high-risk areas. The process for generating these maps can be automated for monthly updates.

:baby_bottle::bar_chart: Newborn/Stillbirth Data: IMPULSE Study (Donat Shamba, Ifakara Health Institute)

This session provided insights from the IMPULSE study, aiming to improve newborn routine data quality and use in low- and middle-income countries, particularly in Africa.

  • Scope: Phase 1 of the study was conducted across four countries (Central African Republic, Ethiopia, Tanzania, Uganda), 15 regions (including humanitarian areas), and 154 sites.
  • Objectives: The study aimed to map newborn indicator data availability, assess data quality, understand data use by various stakeholders, and analyze factors influencing data quality.
  • Observations: Reporting indicator data was streamlined in Tanzania and Ethiopia, more diverse in Uganda, and limited in CAR. Challenges identified included lower data quality and use for quality improvement at the facility level, confidence-competence gaps among health workers in data interpretation and use, and resource availability issues like power, internet, computers, and printers at lower health facility levels.
  • Future Plans (Phase 2): The study’s second phase focuses on co-creation to strengthen data quality and use for newborns. This includes capacity building, developing a standardized Data Quality Assessment (DQA) tool, and establishing Work Improvement Teams in facilities. Educational activities and exploring the use of case notes are also part of the plan for high-mortality settings. The goal is to move towards linking these tools with DHIS2 to complement existing functionalities.

The session highlighted the ongoing commitment to leveraging DHIS2 for vital public health research, addressing data quality challenges, and exploring innovative ways to inform policy and improve health outcomes.

Health information systems architecture: Integration & interoperability🌐 The session "Health information systems architecture: Integration & interoperability" 🌐 delved into the complex yet crucial journey of connecting diverse health information systems using DHIS2 as a central component. Experts from HISP networks in Rwanda, Zimbabwe, West & Central Africa, and Ethiopia shared their experiences and insights into building cohesive digital health landscapes.

Here’s a breakdown of the key discussions:

:light_bulb: The Evolution of Interoperability

The conversation highlighted that the pursuit of interoperability isn’t new, dating back to the early 2000s. Early attempts included a “health suite” combining DHIS2 with other applications, and a 2010 “connectathon” in Ghana showcased initial interoperability efforts. While standards like SDMX-HD were explored, later initiatives have focused on Open HIE, FHIR (Fast Healthcare Interoperability Resources), OpenHIM, and OpenFn. FHIR, in particular, is seen not just as a technical standard but as a foundation for digital health with a growing ecosystem and engaged community.

:globe_showing_europe_africa: Current Realities: Diverse Approaches to Interoperability

After 15 years, countries show varied progress and approaches to interoperability:

  • Interoperability to Create Order: Some countries, like Kenya, South Africa, Rwanda, and Ghana, are actively using interoperability to establish order in their health information systems. This progress is often driven by specific programs, such as HIV/AIDS or immunization.
  • “Doing with What is Available”: In situations without a clear digital health strategy or proper governance, countries often integrate systems using available means, sometimes through direct API-to-API links or SQL queries. While this addresses immediate pain points, it can lead to issues like corrupted metadata or inflated figures.
  • “Interoperability as a Technical Lure”: This occurs when there’s no digital health strategy or governance, leading to minimal investment, duplication concerns, and often linking immature systems.

:construction: Key Challenges Encountered

Several common hurdles persist in achieving robust interoperability:

  • Governance & Policy Gaps :scroll:: A significant challenge is the lack of clear national digital health policies, standards, and legal frameworks for data exchange and privacy. Fragmented ownership and coordination among government entities also pose difficulties.
  • Financial Sustainability :money_bag:: Many initiatives heavily rely on donor funding for initial setup, with limited government budgets allocated for long-term maintenance and scaling. Demonstrating tangible Return on Investment (ROI) can be difficult, hindering domestic funding. The overall cost of such architectures can be “hugely costly”.
  • Security & Privacy Concerns :locked:: Ensuring the confidentiality, integrity, and availability of sensitive patient data, and building public trust in data sharing mechanisms, remain critical.
  • Complexity of Health Domain.
  • Incomplete Metadata Documentation and Limited Use of Standards in legacy systems.
  • Dependence on External Vendors for systems not managed by the Ministry of Health.
  • Performance Issues with aging server infrastructure and limited system integration testing and monitoring.

:seedling: Pathways to Progress: Recommendations for the Future

The session outlined several key prospects and recommendations for overcoming these challenges:

  • Capacity Building :graduation_cap:: Investing in training a skilled workforce in health informatics, data science, and IT is crucial. This includes fostering local innovation and solution development.
  • Strong Governance :handshake:: Developing clear policies, legal frameworks, and ethical guidelines for data collection, exchange, and use is essential. Establishing multi-stakeholder governance bodies to oversee development and operations is also vital.
  • Value for End-Users :sparkles:: Solutions should provide immediate, tangible benefits to healthcare workers, and users must be involved in the design and implementation process to ensure usability and adoption. Ghana’s use of dashboards to drive data culture is a prime example.
  • Embracing Open Standards & Open Source :puzzle_piece:: Adopting internationally recognized interoperability standards (like FHIR) and utilizing open-source software can reduce costs and foster local capacity building.
  • “Start Small, Think Big” :rocket:: It’s recommended not to wait for a “perfect system”. Instead, identify a critical pain point, learn from initial pilots, and gradually scale complexity over time. The process is about “architecting,” rather than just building static “boxes”.
  • Effective Change Management :busts_in_silhouette:: Addressing resistance to change through effective communication, training, and incentives is necessary to highlight the benefits of the architecture to all stakeholders.

:round_pushpin: Country Spotlights: Real-World Implementations

  • Maldives :maldives:: DHIS2 is a core component of its national health strategic action plan and digital health architecture blueprint. It began with a modular rollout in 2019, focusing on aggregated data, and by 2022, shifted to individual patient-level tracking for various modules like immunization, NCDs, and cancer. The country has a single DHIS2 instance for numerous programs. Maldives is integrating DHIS2 with its national eFaas ID system and other sector-specific systems (like education and environment). Strong local capacity building and continuous adaptation are prioritized.
  • Ghana :ghana:: DHIS2 has been Ghana’s official repository for health service data since 2012, covering all 261 districts and over 11,000 facilities. It serves as a “single source of truth” for health data, consolidating vertical program reporting. Ghana is implementing various DHIS2 Tracker systems, including for MCH, HIV/TB, and Immunization, building unified longitudinal records per patient. They plan a nationwide Tracker rollout by 2026 and aim to develop a Health Information Exchange (HIE) on DHIS2. Challenges include connectivity, parallel paper systems, staff turnover, and data quality gaps.
  • Rwanda :rwanda:: With over 30 health systems, 55% of which are DHIS2-based, Rwanda has more than 10 active integrations. Their digital health ecosystem leverages OpenHIM middleware to integrate EMRs with the DHIS2 HMIS. Integration projects include connecting CRVS with immunization registries, DHIS2 Tracker with HMIS Aggregate, and a Cancer Registry with CanReg5. Rwanda is also exploring AI tools for climate and health prediction.
  • Zimbabwe :zimbabwe:: Many HIS systems exist, with efforts to enhance existing infrastructure for integration. All data exchange is intended to happen through OpenHIE. Key integration projects involve eLMIS sending data on vaccine stock and cold chain equipment to DHIS2, and EHR/LIMS data flowing to Impilo DHIS2 via OpenHIE.
  • Ethiopia :ethiopia:: Has undergone significant HMIS reforms, transitioning to a unified eHMIS (DHIS2). Their national health ICT infrastructure includes shared services like a Master Facility Registry (MFR) and Client Registry, with an interoperability service layer. Ethiopia is actively working on improving DHIS2-MFR connector applications.
  • Mali :mali:: DHIS2 has been the national platform since 2016. Mali faces a challenge of fragmented systems, with around 53 applications funded by various donors. Integration efforts are crucial to improve data quality, sharing, and decision-making.

The session emphasized that building effective health information system architectures is an ongoing process (“architecting”) that requires continuous investment in people, governance, and technology, fostering a collaborative community approach to ensure sustainable and impactful digital health transformation.

AI-Powered Innovations in DHIS2🧠💡 The session "AI-Powered Innovations in DHIS2" 🧠💡 explored the exciting intersection of Artificial Intelligence (AI) and DHIS2, showcasing how new technologies can enhance health information systems while also prompting important discussions about potential risks to core DHIS2 principles. The presenters delved into different AI techniques: **predictive analytics**, **classic machine learning**, and **generative AI**.

:robot: Demystifying AI & Machine Learning

The session began by clarifying some common terms:

  • Artificial Intelligence (AI) :brain:: Historically defined by the “Turing Test” as a machine’s ability to converse and convince a human it is also human, a concept conceived over 70 years ago. Early AI systems like Eliza demonstrated rudimentary conversational abilities.
  • Machine Learning (ML) :bar_chart:: Described as a more “sophisticated calculator,” ML is used to find specific patterns and relationships in data, often through “learning by example” (supervised machine learning).
  • Generative AI (GenAI) :writing_hand::microphone::framed_picture:: This branch of AI specifically generates content such as text, images, or audio, as popularized by models like ChatGPT. It can summarize information, translate, assist with learning, and perform complex mathematical or programming tasks.

:police_car_light: Real-World Applications & Impact

The session highlighted two key areas where AI is being piloted with DHIS2:

  1. Automating Event-Based Surveillance (EBS) Triage in Tanzania :tanzania:

    • The Challenge: Manual triage of health alerts in Tanzania’s EBS system was inefficient due to high volumes and reliance on human intervention, leading to significant delays in response time for outbreak detection. For instance, manual triage typically took 36 hours.
    • The Solution: Leveraging DHIS2, the team implemented a rule-based approach using keyword filtering and exploring vector similarities, with future plans for Large Language Models (LLMs). This system aims to filter relevant alerts from “noise”.
    • The Impact: This automation drastically reduced triage time to almost instantly and successfully processed about 85% of alerts. The ministry was pleased with this significant milestone, recognizing that rule-based methods can be further enhanced by machine learning algorithms.
    • Next Steps: Standardizing EBS metadata, enhancing AI capability with LLMs, expanding deployment within DHIS2, and collecting user feedback for refinement.

  2. AI-Assisted Data Entry into DHIS2 :memo::speaking_head:

    • The Challenge: Manual data entry from paper forms (like registry books) or voice notes into DHIS2 is time-consuming, resource-intensive, and prone to errors. This delays data availability for decision-making.
    • The Hypothesis: Can generative AI streamline this process by transforming images of paper forms and audio recordings into digital data for DHIS2?.
    • The Approach: A web application was developed where users upload images or audio. GenAI processes this input (e.g., audio to text), and with specific instructions called “prompts,” it generates a JSON file that can be sent to DHIS2. Human validation is still considered essential as AI models are not perfect.
    • Demos Showed: Examples included converting a handwritten emergency room register, a photo of a malaria case email, and a voice note into structured data.
    • Promising Results: Initial testing indicates that audios are generally easier for LLMs to process than images, and models perform surprisingly well with various languages like Spanish, English, and Swahili, even with long audio recordings containing irrelevant information. The quality of the input image is crucial for accuracy. Prompt engineering (adapting instructions to the AI) is key to improving accuracy. Comparisons between open-source and proprietary models show no clear winner, both performing well.
    • Future Steps: Further field testing with pilots to measure quantitative (accuracy, time saved) and qualitative (staff reaction, usability) results. Discussions are ongoing with DHIS2 product managers about adapting this into the core system, while continuously monitoring the rapid evolution of AI technology and new models.

:balance_scale: Balancing Innovation with Core Principles

While these innovations present immense opportunities, the session also raised critical questions about potential risks to DHIS2’s long-standing principles:

  • Accuracy :white_check_mark:: A major concern is ensuring the reliability of AI-generated data, especially when building applications on top of these technologies. The research aims to measure accuracy by comparing different models and human data entry against expected results.
  • Security & Privacy :locked:: Where will sensitive patient data be stored and processed when leveraging external AI services?. The exploration of open-source models that can be hosted locally is a direct response to this concern.
  • Local Ownership :houses:: A cornerstone principle, ensuring that solutions are hosted and managed by local organizations or within the country, is vital when integrating AI tools.
  • Open Source vs. Proprietary Models :puzzle_piece:: The community has historically promoted open-source solutions, and the discussion highlighted the balance between using readily available proprietary models and fostering open-source alternatives that allow for local control.
  • Metadata Integrity :bar_chart:: LLMs are being explored for tools that can automatically check the semantics, naming conventions, and identify conflicts or duplicates in metadata, which is crucial for system health.

The overall message emphasized that integrating AI into DHIS2 is an ongoing journey of “architecting” rather than a static achievement. It requires continuous investment in people, governance, and technology, with a collaborative community approach to ensure sustainable and impactful digital health transformation.

Upgrading and testing DHIS2 ⬆️🧪 The session "Upgrading and testing DHIS2" ⬆️🧪 was all about ensuring the DHIS2 system remains robust, reliable, and up-to-date. Speakers Lina Zubyte, Tuzo Engelbert, Philip Larsen Donnelly, and Elmarie Claasen shared insights into both internal testing methodologies and best practices for system upgrades.

The core goal of the session was to explore how to minimize the risk of extended downtime, prevent data loss, and ensure existing functionalities are not disrupted during updates, while also introducing new features.

Here’s a breakdown of the key takeaways:

:gear: How DHIS2 is Tested (Internally)

The DHIS2 team employs various testing methods:

  • Manual Testing :writing_hand: and Automated Testing :robot:.
  • Current Process: Bugs, features, and tasks are tested on a case-by-case basis.
  • Regression Testing: Comprehensive manual testing is conducted before patch releases to ensure no existing functionalities are broken.
  • Extra Initiatives:
    • Bug Bashes: Events inviting the community to test new features.
    • Beta Testing Campaigns: The first public campaign was held this year, allowing external users to get involved. This helps capture real user needs, guide development, discover new features, and gather feedback to shape the future of DHIS2.

:rocket: The Upgrade Journey: Process & Lessons Learned

HISP South Africa shared their structured approach to upgrades, emphasizing a standardized process involving planning, execution, and verification.

The typical upgrade process includes:

  1. Decision to upgrade :clipboard:.
  2. Download the .war file (or Docker image) and set up a test environment :laptop:.
  3. Implement any necessary changes to the war file.
  4. Conduct thorough testing :white_check_mark:.
  5. Obtain stakeholder approval :+1:.
  6. Initiate and notify about the change request.
  7. Follow all change notification steps.
  8. Perform final testing to validate a successful upgrade :tada:.

Crucial Lessons Learned from their experience include:

  • Stay within the latest 3 supported versions for smoother transitions.
  • Testing, testing, testing is paramount :test_tube:.
  • Performance testing is essential.
  • Have a clear process for app updates.
  • Be aware that local customisations can delay upgrade readiness.
  • Always develop new systems on the latest DHIS2 version.
  • Standardize versions as much as possible across implementations.

:warning: Challenges & Tips

  • Resource-Intensive: Testing, especially beta testing, requires significant time and resources from administrators and teams.
  • Advocacy: It’s vital to advocate to leadership about the importance of dedicated time for testing, as system failures can impact trust.
  • Dedicated Environments: Always conduct training and testing in a dedicated DHIS2 environment to avoid impacting live production data.

:books: Available Resources

  • JIRA and TestPad: Internal tools for tracking test cases and bugs.
  • DHIS2 Website: Provides release pages and upgrade documentation.
  • Community of Practice (CoP): A platform for ongoing questions and discussions.
  • Expert Lounges: Opportunities for direct engagement with DHIS2 developers and implementers for specific queries.

The session underscored that maintaining DHIS2 requires continuous effort in testing and upgrading, emphasizing a collaborative community approach to tackle these complexities effectively.

Lightning Talks: Innovative Use Cases💡🗣️ The "Lightning Talks: Innovative Use Cases" 💡🗣️ session showcased cutting-edge extensions and creative applications built upon the DHIS2 platform, highlighting how communities are pushing the boundaries of what's possible with health information systems. Here's a glimpse into the diverse innovations presented:

:globe_showing_europe_africa: Climate Health Vulnerability Toolkit (CHAT)

e-Health Africa presented their Climate Health Vulnerability Toolkit (CHAT), a tool integrated into DHIS2 to strengthen health systems and respond to public health emergencies, particularly in the context of climate adaptation. This toolkit helps identify vulnerability levels for health facilities against climate-related events and informs where interventions are most needed. The dashboard visualizes vulnerability across different domains, like health workforce capacity, which is crucial for preparing for events such as floods.

:memo: Streamlining Data Entry: Journal Input

From Ukraine, the Alliance for Public Health demonstrated an innovative tool to streamline data entry in DHIS2 using “Journal Input”. Addressing the challenge of social workers manually recording client interactions (e.g., for HIV prevention) in paper journals, this tool allows for quick data input. It’s designed to make data entry more natural, allowing users to see an entire page of entries at once, which reduces mistakes and saves time, particularly useful for high volumes of data. The application leverages DHIS2’s existing organizational unit tree and user permissions.

:money_bag: DHIS2 for Credit Management (VSLA)

Saudí, from Mozambique, shared their experience adopting DHIS2 to manage information for Village Saving Loan Associations (VSLA) :handshake:. This initiative provides transparency in fund management, allows for the inclusion of other saving mechanisms, and ensures timely information for remote monitoring and data security. The solution integrates both mobile (for member registration and transaction recording) and web platforms (for mapping, configuration, analysis, and reporting). Future plans include linking this system with mobile wallet platforms :mobile_phone:. Challenges include managing different group activity flows, constant platform change requests, and users with low technology literacy.

:baby: Auto-Generating National IDs for Newborns

HISP Ghana, in collaboration with OpenFN and the Ghana Health Services, presented a project focused on auto-generating national IDs for newborn Ghanaians using DHIS2 data :id_button:. This innovative workflow addresses the difficulty of unique identification for newborns by automating the process: once a birth is registered in DHIS2, OpenFN (a digital public good for data integration and workflow automation) triggers the generation of a national ID and a birth certificate. These details are then sent back to DHIS2, and an SMS notification is sent to the mother. This project promises enhanced birth and death registration, improved data quality and accuracy, and increased access to identification and services for children. The project is currently preparing for rollout.

Cancer Registries🎗️📊 The session on "Cancer Registries" 🎗️📊 delved into how DHIS2 is being leveraged and integrated with global tools to enhance the collection, management, and use of vital cancer data. The panel included Brian O'Donnell (HISP Centre, UiO), Morton Ervik (IARC, WHO), Dr. Hong Chu (WHO DI), and Ms. Aishath Samiya (Maldives Ministry of Health).

The core aim was to demonstrate how DHIS2 can work seamlessly with existing systems to gather individual-level cancer data, addressing global data gaps, and aligning with international standards.

Here’s a breakdown of the key discussions:

:bullseye: The Global Cancer Data Challenge

  • Cancer poses an increasing burden of disease globally.
  • A significant challenge is the lack of comprehensive data: less than half of countries can accurately identify the real cause of death, and only about a third possess some form of a cancer registry.
  • Data on screening, early detection, vaccination, treatment, and cause of death are often scattered and fragmented. The ambition is to bring all this information together to provide a holistic picture.
  • Two projects leveraging DHIS2 are underway: one on cancer registries (with IARC, implemented in Rwanda and the Caribbean) and another on a global platform for early childhood cancer, where DHIS2 serves as a data mechanism for countries to stream data up to a global level.

:maldives: Maldives’ DHIS2 Cancer Registry Journey

The Maldives Ministry of Health shared its experience, highlighting DHIS2’s role in their National Cancer Registry due to existing familiarity and support.

  • Vital Role: The registry supports cancer surveillance, epidemiological research, control and prevention efforts, quality improvement, resource planning, and patient support.
  • Data Collection: The process involves three main steps: patient registration, diagnosis information (verified by clinicians), and long-term follow-up.
  • Standardization: The system uses global standards like CanReg5, ICD-10, and ICD-O to ensure international compatibility.
  • Real-time Insights: Dashboards provide real-time visualization at national, atoll (group of islands), and health facility levels, enabling data-driven planning, screening, and tracking of key indicators.
  • Integration Efforts: Currently implemented in main tertiary hospitals, with ongoing API integration with electronic medical record (EMR) systems to reduce workload, prevent duplication, and improve data timeliness and accuracy. Efforts are also being made to include private tertiary hospitals.
  • Impact: It provides immediate insights into cancer trends, facilitating targeted planning and interventions, and increasing visibility into diagnostic and treatment options.
  • Future: The registry aims to evolve into a national resource for cancer policy and research, paving the way for broader digital transformation in health.

:globe_showing_europe_africa: Global Standards and Integration with CanReg5

The International Agency for Research on Cancer (IARC), a specialized WHO agency, highlighted its efforts to promote international collaboration in cancer research, particularly through cancer surveillance.

  • Global Initiative: IARC’s Global Initiative for Cancer Registries aims to improve the quality, availability, and use of population-based cancer registry data through regional hubs and training.
  • CanReg5 Software: This tool is designed to help cancer registries input, store, check, and analyze data in a standardized way, facilitating comparable analysis across populations. It offers features like standardized reports, interactive browser tools, and data import capabilities.
  • Innovate Project: A key initiative involves developing a standardized cancer module within DHIS2 and a tool to link DHIS2 data to CanReg5.
    • Use Cases: This linkage allows either DHIS2 to function as the primary data entry system for the cancer registry (with data exported to CanReg5 for analysis), or CanReg5 to serve as the main cancer registry database while DHIS2 handles data entry.
    • Pilot: This project has been piloted in Rwanda and the Caribbean.

:light_bulb: Key Insights & Discussion Points

  • DHIS2 vs. CanReg5: The choice of which system to use as the primary cancer registry depends on existing infrastructure and familiarity. For decentralized data entry, DHIS2 is often preferred, with data then exported to CanReg5 for analysis or as a central cancer database.
  • Addressing Data Quality: The Maldives tackled complex data quality issues and automatic ICD coding by implementing an “immense number of program rules” within DHIS2. The goal is to share these learnings to avoid other countries “reinventing the wheel”.
  • Beyond Core Data: While currently focusing on core registry data, there’s exploration into integrating other analyzable data from EMR systems, including the potential for radiology images to be brought into DHIS2.
  • Community & Collaboration: The session underscored the importance of strengthening standards and bringing different partners together to effectively manage cancer data. The ongoing implementation in the Caribbean, for example, is providing valuable lessons on challenges like CanReg checks and adapting to specific data entry forms.

The session concluded by emphasizing that maintaining robust cancer registries requires continuous effort in data collection, standardization, and integration, often leveraging existing platforms like DHIS2 to drive national health system improvements.

1 Like