When Artificial Intelligence Strengthens Primary Healthcare: New Hope for the Future of Maternal and Child Health

Figure 1. From Paper to Intelligence: OCR & Predictive in Action - Summit’s community health promoters demonstrates the user interface of the OCR and predictive tools

The KONEKSI Project AI-in-Healthcare Program has been underway since 2024 as part of a collaborative effort to strengthen primary healthcare services in Indonesia through the use of artificial intelligence. This initiative secured competitive funding and received “Flourish Funding” from the Australian Government through the KONEKSI Grants Program after undergoing a rigorous selection and evaluation process emphasizing innovation, policy relevance, and potential for implementation impact. This support serves as a critical foundation for accelerating the development and pilot testing of AI-based solutions at the primary care level.

From 2–6 February 2026, a new milestone was marked when consortium partners from Australia and Jakarta conducted a strategic visit to one of the implementation sites to directly observe field progress and deepen technical discussions. The consortium, led by Summit Institute for Development (Summit Institute) and comprising Badan Riset dan Inovasi Nasional (BRIN), Universitas Mataram (UNRAM), and Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia, conducted a series of intensive activities combining strategic discussions, field validation, AI model development, and scientific publication strengthening. The program has four main implementation sites: West Lombok, East Lombok, Central Lombok, and Garut. This field visit focused on the service area of Narmada Primary Health Center in West Lombok as one representation of implementation at the primary care level.

Aligning Direction and Strategy for AI Development

The agenda began with presentations on project progress, including data infrastructure, the development of the Optical Character Recognition (OCR) system, and advancements in the predictive models under development. Discussions were open and strategic, ensuring that all partners shared a common understanding of achievements, challenges, and next steps.

One key session explored the potential of AI to drive broader transformation in primary healthcare services. The approach is not solely technology-oriented, but also focused on system strengthening and improving data-driven decision-making quality. This session continued with an in-depth discussion of three main AI development focuses: predicting the probability of healthcare-seeking behavior among pregnant women in utilizing Antenatal Care (ANC) services; analyzing combinations of interventions and services that influence health outcomes; and predicting health workforce performance to support service management strengthening.

From the Maternal and Child Health Handbook to a Predictive System: Field Validation

As part of the validation process, the consortium visited Jerangoan 1 Integrated Health Post (Posyandu) in Kramajaya Village, within the service area of Narmada Primary Health Center. At this site, the team directly observed how community health volunteers provide routine maternal and child services, including measurements of height, weight, head circumference, and mid–upper arm circumference, all of which are manually recorded in the Maternal and Child Health (MCH) Handbook.

The MCH Handbook forms the foundation of this system. Until now, data stored in handwritten form has been difficult to read and analyze quickly and comprehensively. Through OCR technology, paper records can be scanned and converted into structured digital data in a computer-readable format. The data are processed and stored in the backend system using the Fast Healthcare Interoperability Resources (FHIR) standard to enable interoperability with data from various health information systems. After undergoing verification and validation, the data serve as input for the predictive AI models currently under development.

The team also visited Narmada Primary Health Center to understand service workflows, review existing application systems, and discuss the integration of AI technology into ongoing systems. This approach ensures that the innovation developed does not stand alone, but is integrated into routine service practices.

Figure 2. Posyandu Visit: Advancing AI in Primary Healthcare (Posyandu Jerangoan 1, Krama Jaya Village) - A group photo captures cadre, village leadership, Puskesmas representatives, the District Health Office of Lombok Barat, SID, BRIN, and CSIRO.

Figure 3. Puskesmas Visit: Advancing Digital Transformation in Primary Care (Puskesmas Narmada, Lombok Barat)

From Paper Records to Prediction: Strengthening Decision-Making at the Frontline

This transformation begins with data. Maternal and child health service data have long been available in large quantities but remain disconnected and primarily used for reporting. Through digitalization, paper records become digital data. Subsequently, predictive artificial intelligence transforms these data into actionable recommendations.

The developed models function as decision-support systems, assisting health workers in detecting risks earlier, prioritizing follow-up actions, and estimating intervention needs more accurately. This approach encourages services to become more preventive and data-driven.

Voices from the Frontline of Primary Care

For health workers at Narmada Primary Health Center, this initiative is not merely a technology project. Dr. I Komang Sutrisna Budiyasa, Head of Narmada Primary Health Center, emphasized that AI-based approaches can serve as tools to strengthen clinical decision-making.

“OCR and Predictive AI are not meant to replace health workers, but to help bridge gaps and support us in making faster and more accurate decisions,” he stated.

Similarly, Rita Sosilo, Coordinator of the Maternal and Child Health Cluster, and Fri Noviani, District Health Office of West Lombok, highlighted the challenges of time-consuming manual documentation.

“We hope AI can help accelerate data processing so that services become more efficient and responsive,” she explained.

These testimonials demonstrate that the digital transformation being developed has tangible relevance for frontline users.

Towards Broader Scale

The visit concluded with discussions on strengthening scientific publications and exploring further collaboration. Implementation across West Lombok, East Lombok, Central Lombok, and Garut is designed to build a model that can be replicated nationally.

This transformation is not merely about digitalizing the Maternal and Child Health Handbook, but about ensuring that previously passive data can be leveraged to support evidence-based decision-making for better maternal and child health outcomes.