The intersection of health information management and clinical registries

This Special Issue of the Health Information Management Journal (HIMJ) investigates “The Intersection between Health Information Management and Clinical Registries.” Health information management professionals are health sector specialists who “plan, develop, implement and manage health information systems, such as patient information systems, and clinical and administrative data, to meet the medical, legal, ethical, and administrative requirements of health care delivery” (Australian Government, 2025). They are specialists in the science of data management, regardless of how health information is collected, organised, stored, shared, analysed or reported, in multiple environments – paper, digital, electronic or web-based. The latter requires an increasing specialist knowledge of health informatics and digital health technologies. Health Information Managers have a crucial role in safeguarding the integrity, accuracy and confidentiality of these data. Underpinning data integrity and accuracy is the health information management professional’s expert knowledge of the language of medicine, the importance of complete, comprehensible clinical documentation and of clinical coding systems, terminologies and classifications. Their comprehensive knowledge of research methods ensures that data requested for research are handled ethically and for well-designed, methodologically sound analysis and reporting proposals, and that other data requests align with relevant national and state legislation and/or standards (Health Information Management Association of Australia (HIMAA), 2023). Clinical registries are structured repositories of standardised health data, which are essential to drive quality improvement, enhance clinical practice, support public health surveillance and advance research (Monash University, Public Health and Preventive Medicine, 2025).

This Special Issue explores how aspects of health information management are applied within clinical registries to advance the field of clinical registry science. Organised into six thematic sections, this Special Issue reflects the breadth and depth of current innovations and challenges in clinical registry development and use, which through this collection of review, research and professional practice articles, offers valuable insights for health information management professionals, researchers, practitioners and policymakers working to strengthen clinical registries. Together, these articles underscore the importance of health information management practices in building robust, responsive and high-quality health data systems, exploring aspects of registry science at different levels of maturity.

Data collection and standardisation: innovations in data collection methods and protocols for clinical registries

Internationally, there is growing momentum to ensure all data collected are standardised for all health systems using recognised nomenclatures, terminologies and standards such as the Systematized Nomenclature of Medicine – Clinical Terminology (SNOMED-CT), Health Level 7 International (HL7) Fast Healthcare Interoperability Resources (FHIR) (Riedel et al., 2024; Schulz and Martínez Costa, 2025), extending from common data models (Biederman et al., 2021), minimum datasets (Farajollahi et al., 2025), and core datasets for use in research and other targeted activities (Mann et al., 2026). Mahdian et al. (2026) discussed the structure, scope and operational challenges of rare kidney disease registries from a global perspective. These registries improved diagnosis, care coordination and research for low-prevalence conditions. This underscores the importance of standardised data collection and international collaboration, as fragmented efforts and inconsistent methodologies limit the broader impact of these registries. Saghaeiannejad Isfahani et al. (2026) reported the adaptation of an international registry software assessment tool for developing countries, in which the localisation process underscored the importance of context-specific tools to accurately evaluate registry functionality and support continuous improvement. Regarding the Australian and New Zealand Massive Transfusion Registry, Sparrow et al. (2026) detailed their methodology to harmonise data collection across healthcare systems. Implementing a standardised approach enables more accurate benchmarking, supports clinical decision-making and enhances the quality of transfusion care across diverse settings. Both Riley and Hui (2026) and Gjorgioski et al. (2026) suggested that coordinated national efforts are needed to standardise data collection. The case study by Morrison et al. (2026) described how the Australian Stroke Clinical Registry has operationalised the Framework for Clinical Quality Registries (Australian Commission on Safety and Quality in Health Care, 2024) for standardised data collection. Satheakeerthy et al. (2026) explored the automation of cancer registries using advanced technologies such as large language models, AI scribes and patient-driven data collection to improve scalability and efficiency. The findings highlight that the success of these innovations is heavily dependent on the quality and structure of data within electronic medical records (EMRs).

Data quality assurance: strategies for ensuring and enhancing the quality of data, including case ascertainment

‘Garbage in – garbage out’ has colloquially become a term for poor quality data entry leading to unreliable data output (Kilkenny and Robinson, 2018). The importance of assuring data quality has been widely recognised (Gisslander et al, 2024; Martos et al, 2024; Stausberg and Harkener, 2023). Ensuring data quality is a core competency for health information management professionals and essential for all registries.

In this Special Issue, Morrison et al. (2026) examined strategies used by the Australian Stroke Clinical Registry to enhance data quality, validation processes and stakeholder engagement. Their findings highlight that proactive data quality management is essential for ensuring the reliability and utility of registry outputs in clinical care and policymaking. Practical insights into the use of Australian Immunisation Register data were considered in Hull et al. (2026), who highlighted the value of this Register to support immunisation research and clinical decision-making. Their findings revealed challenges in data completeness and integration, which can limit the effectiveness in guiding safe and consistent vaccination practices. Dhinsa et al. (2026) assessed the agreement between the Better Outcomes Registry and Network Ontario Information System on births in Ontario with the Canadian Institute for Health Information-Discharge-Abstract-Database. The two databases showed strong agreement across most data elements, though further investigation is needed to understand and resolve the identified discrepancies (Dhinsa et al., 2026). These articles highlight that to effectively support health policy, surveillance and research, all registries must take steps to ensure the quality of health data.

Data coding and classification: developments in standardised coding systems and classification methodologies, including validation of clinical coding

Health information management professionals are responsible for the clinical coding of admitted patient records which is then used for case ascertainment at registries (Shepheard and Groom, 2020). Limited use of international coding systems among existing registries hamper interoperability and data exchange (Dendooven et al., 2020). Riley and Hui (2026) reported that Australia’s congenital anomaly registers are currently limited by fragmented data sources and inconsistent ascertainment methods, which hinder accurate national surveillance of paediatric genetic conditions. The reliance on administrative data and inconsistent coding practices leads to underreporting and misclassification of paediatric genetic conditions limiting the effectiveness of public health surveillance. Gjorgioski et al. (2026) explored how different data sources and case ascertainment methods influence the reported prevalence of paediatric monogenic and chromosomal conditions across international studies in a scoping review. Their findings revealed that reliance on ICD-coded data alone can lead to significant under-ascertainment, particularly in Australia where disease-specific registries are underutilised. Roman et al. (2026) surveyed cancer registries within the United Kingdom and Europe to assess the current use of automated clinical coding. Many cancer registries now employ computer-assisted algorithms to process pathology reports, and the authors suggest that further implementation of automated clinical coding could enhance data quality and accuracy, conserve resources and expand opportunities for research using registry data.

Governance, privacy and security: advances in safeguarding patient privacy and securing sensitive health information

Registry databases of health-related information operate within an overall governance and management structure; for example, the Australian Commission on Safety and Quality in Health Care (ACSQHC) has developed key documents regarding the governance, operation and technical requirements of clinical quality registries in Australia (Australian Commission on Safety and Quality in Health Care, 2025). Ali et al. (2020) reported that rare disease registries are most beneficial when they enable data sharing and promotion of research and audits, often internationally, for improving patient care. These authors claimed that given the recent expansion of rare disease registries, standards of good practice in relation to governance, infrastructure, documentation, training and audits, are even more essential, and focused on vital aspects of data access and data governance policies for rare disease registries, using the example of European Registries for Rare Endocrine Conditions. Helstad et al. (2026), in describing the establishment of the Norwegian Health Archives Registry in terms of size, coverage and archival processes, also presented novel methods to automate deidentification of medical records. The authors highlighted the diversity of skilled professionals who contribute to such an exercise, involving collaborations among clinical, health information, health technology cybersecurity experts involved. Saghaeiannejad Isfahani et al. (2026) confirmed that ensuring data security and privacy in information systems is a paramount concern for patients, in designing a localised assessment tool for disease registry software. Strategies to enhance governance structures were also examined by Morrison et al (2026).

Research methods, data analysis and reporting: novel approaches to analysing registry data and deriving actionable insights

Registry science analytics is progressing from descriptive reporting to understanding health outcomes. There were few articles in this Collection highlighting innovative methods for data analysis, but as recommended by Jacob-Brassad and de Mestral (2022), priority should be given to selecting analysis methods that are tailored to the type of study, the study design and its purpose. Groth et al. (2020) raised the need to also understand the strengths, limitations and differences between various data sources – administrative, hospital-based and population-based registries – to improve the quality and accuracy of research being undertaken on these datasets and to acknowledge these to support proper interpretation of research results.

Reporting techniques are also advancing with technology. Morrison et al. (2026) provided an example of a live dashboard displaying data quality within the Australian Stroke Clinical Registry for end-users. Eley et al. (2026) described the Consumer-Friendly Information project, which aimed to make clinical registry outputs more accessible and meaningful to consumers. Consumers co-designed registry fact sheets and reports, which improved the relevance, clarity and usability of registry data, thereby enhancing its impact on patient engagement and decision-making. Hansen et al. (2026) evaluated the accuracy of benchmarking and outlier detection methods across clinical registries of varying sizes, using simulated datasets to assess performance under different conditions. Their findings suggested that registries with low patient volumes and low-prevalence outcomes may struggle to achieve reliable benchmarking, potentially leading to misclassification of provider performance. Future directions should focus on establishing minimum data thresholds and promoting risk-adjusted models to ensure benchmarking efforts are both statistically robust and clinically meaningful.

Interoperability and linkage: solutions for achieving seamless interoperability between clinical registries and other health information systems

The benefits of record linkage in registries for epidemiological research were explored in a systematic review by Kataoka et al (2023). Registries are in various stages of achieving interoperability for sharing data between clinical registries and other health information systems. International validation studies in this area have resulted in the development of models that are feasible and reliable (Langhout et al, 2025). Three articles in this Special Issue provided solutions for achieving seamless interoperability between clinical registries and other health information systems. Stapleton et al. (2026) explored how emerging technologies – such as automated data capture, machine learning and electronic health record integration – are being applied within population-based cancer registries to enhance data quality and operational efficiency. Their findings suggested that while these innovations offer significant potential, their implementation is often hindered by infrastructure limitations, workforce capacity and interoperability challenges. Further, Hull et al (2026) advocated for improvements in data linkage between the Australian Immunisation Register and clinical systems to enhance data quality. Herbert et al. (2026) reported that integrating clinical registry data with administrative hospital datasets can substantially enhance the accuracy of identifying breast device procedures in Australia. Without such linkage, registries may underreport key procedures, limiting their effectiveness in monitoring device safety and informing clinical practice. To improve interoperability, future directions should focus on building technical capacity, national frameworks and scaling-up existing models, and developing national frameworks to guide the ethical and effective adoption of advanced technologies in clinical registries.

Future direction for clinical registries

Ahern et al. (2026) reported that Australian clinical registries play a critical role in monitoring health system performance, with most contributing to research, quality improvement and policy development across a wide range of clinical areas. However, variability in funding, data integration, and real-time reporting capabilities limits their full potential to drive system-wide improvements. Future efforts should focus on enhancing interoperability, expanding government support and embedding registries more deeply into national health data frameworks to maximise their impact. Burns et al. (2026) surveyed Health Information Managers (HIMs) and indentified that they play a vital yet under-recognised role in Australian clinical registries, particularly in data management, analysis and reporting. Their underutilisation leads to gaps in database expertise and missed opportunities for quality improvement and informed decision-making.

Conclusion

Throughout this Special Issue, we have showcased pioneering research and innovative perspectives that demonstrate the evolving relationship between health information management professional practices and the development, management and utilisation of clinical registries. Although few articles mentioned that health information management professionals had contributed to the activities of clinical registries, the six thematic sections align strongly with health information management professional core competencies. Future efforts should focus on advocating for broader integration of health information management professionals into registry teams and enhancing recognition of their specialised skills to strengthen the national health data infrastructure.