From the Field to the National Level: Cross-Sector Collaboration and AI Technology Drive the Transformation of Maternal and Child Health Services
- SID Developer
- Apr 21
- 4 min read
Maternal and child health challenges remain an unresolved global issue. Adverse pregnancy outcomes continue to be one of the leading causes of neonatal mortality, with the highest burden occurring in low- and middle-income countries, particularly those with limited access to antenatal care services for pregnant women. At the same time, the current risk assessment methods are often insufficient, especially in resource-limited settings, resulting in suboptimal early detection.
In Indonesia, this situation is reflected in the persistently high maternal mortality rate, which stands at around 189 per 100,000 live births, as well as a stunting prevalence of 19.8% in 2024. The challenge is further complicated by the increasing incidence of non-communicable diseases among pregnant women, such as hypertension and gestational diabetes, which contribute to a higher risk of complications.
At the IDE Katadata Future Forum 2026, the CEO of the Summit Institute, Yuni Dwi, emphasized that collaboration is the key to overcoming barriers in healthcare services. This approach highlights the importance of synergy between government, researchers, and field implementers, through both top-down and bottom-up mechanisms, ensuring that solutions are not only strong in policy but also relevant in implementation. This aligns with Katadata’s view that cross-sector collaboration is a crucial factor in healthcare transformation.
This initiative is part of the Indonesia–Australia strategic partnership under KONEKSI, led by the Summit Institute for Development (SID) and involving cross-institutional collaboration, including Universitas Mataram (UNRAM), BRIN, and international partners such as CSIRO.
The implementation of this initiative is aligned with and supports the policy direction of the Ministry of Health as the main authority in Indonesia’s health sector. In a broader context, SID is also part of the Ministry of Health consortium in accelerating the Primary Care Integration (ILP). This approach places the principles of Gender Equality, Disability, and Social Inclusion (GEDSI) as a foundational framework, ensuring that the innovations developed reach vulnerable and underserved populations.
Laying the Foundation: Strengthening Healthcare Workforce Capacity
Digital transformation cannot stand alone without strong human resources. Therefore, this approach begins with a competency assessment of thousands of midwives across various regions to map their capacity in providing maternal and child health services. This assessment is based on core midwifery competencies, covering key areas across the continuum of care. The mapping results serve as a foundation for identifying capacity gaps and designing more targeted interventions. This approach reinforces the idea that strengthening the health system must begin with strengthening its workforce.
Building on Existing Systems: Optimizing Data and Infrastructure
Rather than developing a completely new system from scratch, this approach focuses on optimizing existing resources. One of the richest sources of data is the Maternal and Child Health (MCH) handbook (Buku Kesehatan Ibu dan Anak), which has traditionally been paper-based. Through the use of Optical Character Recognition (OCR) technology, data from the MCH handbook can be digitized simply by taking a photo. This step accelerates data recording while opening opportunities for real-time data utilization.
Next, integration is carried out using interoperability standards such as Fast Healthcare Interoperability Resources (FHIR), enabling data to connect across existing health systems. This approach supports the development of an open and interoperable ecosystem. In a federated FHIR system architecture, each server node adhering to the same standard can securely share data without requiring centralization in a single system. As a result, data exchange across facilities and regions becomes more flexible while maintaining data sovereignty at each institution.
Once the data foundation and interoperability are established, AI-based analytics are utilized to support pregnancy risk prediction. The results of this analysis serve as a decision-support tool for healthcare workers, enabling earlier and more targeted interventions.
Towards Scale-Up: From Innovation to an Integrated System
Initial implementation shows that this approach is not only innovative but also feasible and relevant to field needs, particularly in supporting frontline healthcare workers in primary care settings. For the next stage of development, the scale-up strategy focuses on system integration, strengthening healthcare workforce capacity, and adapting to local contexts. Garut Regency has been designated as one of the pilot areas for this development, through an initiative toward an integrated digital health district. This initiative demonstrates how digital transformation can be implemented systemically at the regional level by connecting service delivery, data systems, and workforce capacity.
With a combination of policy, technology, and field implementation, this model opens opportunities for replication across various regions in Indonesia.
The Future of Smarter and More Inclusive Healthcare Services
Healthcare transformation is not merely about digitalization, but about how systems can strengthen the people within them. Technology acts as an enabler, while healthcare workers remain the key actors in delivering services.
Through strengthened human resources, more optimal use of data, and cross-sector collaboration, Indonesia is moving toward a more resilient, adaptive, and inclusive primary healthcare system.
In the future, such approaches are expected not only to improve service quality but also to ensure that every mother and child, wherever they are, has an equal opportunity to live a healthy life.
One of the challenges in achieving equitable healthcare services in Indonesia is that human resources are not yet fully optimized.
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