Malaffi – Abu Dhabi Health Information Exchange

The implementation standardizes data across both public and private sectors, supporting 50,000 clinical users and enabling millions of health records to be securely and meaningfully shared.

EMEA

Health information exchange

Malaffi, managed by Abu Dhabi Health Data Services (ADHDS), is the first Health Information Exchange (HIE) in the Middle East and North Africa (MENA) region to implement SNOMED CT and LOINC at scale. The initiative was launched to address fragmentation across Abu Dhabi’s healthcare ecosystem—comprising 3,000+ facilities and 90+ electronic medical record (EMR) systems—and to improve the quality, consistency, and interoperability of clinical data. The implementation standardizes data across both public and private sectors, supporting 50,000 clinical users and enabling millions of health records to be securely and meaningfully shared. The project was developed under the Department of Health – Abu Dhabi’s digital transformation strategy and aims to create a sustainable, AI-ready healthcare data infrastructure that enhances clinical decision support, population health surveillance, and evidence-based policymaking.

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Description

The scope of the Malaffi implementation spans the entire Emirate of Abu Dhabi, encompassing both public and private healthcare providers. The system connects more than 3,000 healthcare facilities operating through over 90 different electronic medical record (EMR) systems, serving a network of approximately 50,000 clinicians and healthcare professionals.

This large-scale implementation covers multiple clinical domains, including diagnoses, allergies, chronic diseases, family history, social history, and diagnostic procedures. Within the social history domain, detailed coding has been applied to smoking, alcohol and substance use, educational attainment, and employment status—ensuring comprehensive and standardized documentation across the patient record. Specialized diagnostic areas, such as neurology procedures (including visual, somatosensory, and auditory evoked potentials), have also been standardized through structured SNOMED CT and LOINC coding.

In parallel, Malaffi has achieved extensive implementation of LOINC for laboratory results, increasing adoption from 12 percent to more than 85 percent by mid-2025. The initiative also integrates other international coding systems such as ICD-10 and CPT to ensure full interoperability across all clinical domains.

The implementation is governed by a strong regulatory foundation established by the Department of Health – Abu Dhabi, which issued formal guidance on coding standards in 2021. This directive, combined with the UAE’s membership in SNOMED International, created the enabling policy environment for large-scale standardization and interoperability. As a result, Malaffi has emerged as one of the most advanced health information exchanges globally, setting a new benchmark for data-driven, connected healthcare in the region.

Scope

SNOMED CT is at the core of Malaffi’s approach to achieving semantic interoperability across the Emirate’s diverse healthcare landscape. The terminology provides a unified clinical language that ensures the meaning of health information is preserved when exchanged between different systems, vendors, and care settings.

Rather than relying on retrospective mapping or translation of data, Malaffi implemented SNOMED CT directly at the source EMR level. This approach ensures that clinical data are coded accurately and consistently at the point of entry, maintaining the integrity of meaning throughout the data exchange process. To support this, the Malaffi team developed customized SNOMED CT reference sets tailored to Abu Dhabi’s clinical and regulatory context. These locally adapted refsets maintain alignment with international standards while reflecting the specific healthcare environment of the Emirate.

Through this model, SNOMED CT now underpins structured clinical documentation across key domains—ranging from diagnoses and chronic conditions to allergies, family history, social history, and diagnostic procedures. This consistency allows clinical information to be used effectively for patient care, analytics, and research.

Embedding SNOMED CT at the source level also created a sustainable and cost-effective model, eliminating the need for third-party terminology servers and reducing ongoing maintenance and licensing costs. The resulting data quality improvements have had a transformative impact: enhancing clinical decision support, enabling sophisticated AI-driven analytics, and strengthening Abu Dhabi’s public health surveillance capabilities.

By aligning with WHO-recommended standards and leveraging SNOMED International’s global framework, Malaffi has built a foundation for advanced digital health innovation. The initiative demonstrates how well-governed, standardized clinical data can enable predictive analytics, personalized medicine, and population health insights—positioning Abu Dhabi as a regional leader in the use of standardized terminology to power connected, intelligent healthcare.

How SNOMED CT will be used

The decision to adopt SNOMED CT as the primary clinical terminology for Malaffi stemmed from a comprehensive analysis conducted by the Abu Dhabi Health Data Services (ADHDS) team in 2021. This assessment revealed that while data exchange between healthcare providers was technically possible, the lack of semantic consistency—that is, the ability to preserve the meaning of clinical information across systems—was a critical barrier to achieving high-quality, interoperable health data.

SNOMED CT was selected because it provides a comprehensive, multilingual, and hierarchically structured terminology capable of representing clinical information with the precision and flexibility required at scale. Its rich concept model allows for both pre- and post-coordination, meaning that clinicians and systems can express complex clinical situations in a standardized way while retaining full clinical meaning.

In addition, SNOMED CT’s alignment with international standards—including HL7, ICD-10, and LOINC—made it ideal for integration within Malaffi’s broader interoperability strategy. Its compatibility with other terminologies enabled seamless linkage between diagnostic, procedural, and laboratory data, supporting unified data exchange across Abu Dhabi’s public and private healthcare sectors.

From a strategic standpoint, SNOMED CT also aligned with the UAE’s membership in SNOMED International and the Department of Health – Abu Dhabi’s 2021 coding guidance, both of which emphasized the need for a standardized clinical language to support digital transformation. This policy and governance framework provided a clear pathway for adoption and long-term sustainability.

Furthermore, SNOMED CT was chosen to support the Emirate’s AI and data analytics ambitions. High-quality, coded data form the foundation for artificial intelligence, machine learning, and predictive modeling. By standardizing data at the source EMR level using SNOMED CT, Malaffi ensured that clinical information would be accurate, structured, and ready for advanced analytical use without the need for costly data cleansing or transformation.

Ultimately, SNOMED CT was selected because it offers the combination of clinical depth, interoperability strength, and scalability required to support Abu Dhabi’s goal of building a unified, AI-ready health data environment. It not only addresses immediate interoperability challenges but also provides the terminology infrastructure needed for the future of data-driven, patient-centered care.