Health information management : journal of the Health Information Management Association of Australia最新文献

Background: As the healthcare delivery landscape evolves and the impact of digital technologies and transformations becomes increasingly apparent, a competent healthcare management workforce, confident in the capabilities to employ and effectively incorporate digital health technologies into health service delivery is imperative.

Objectives: The article explores the perceptions and experiences of middle-level health managers in their responsibilities and competency requirements in the context of digital health transformation.

Method: Six focus groups were conducted with mid-level managers recruited across Australian public hospitals in 2023. Braun et al (2019) six-phase, reflexive thematic analysis approach was used to distinguish, explore, organise and advance insight into themes and categories of the factors emerging.

Results: Findings indicated that the digital competencies managers are required to demonstrate include a much broader and expanded competence across eight identified competencies.

Discussion: The unique attributes identified highlight and underscore the importance of enhancing digital competencies for health service managers to lead and manage in the digital health context.

Conclusion: This study reveals two key areas of health service manager competency enhancement required: (i) competence in digital data management and security, and (ii) the management of digital technologies in practice. These findings can inform institutional competency frameworks that guide the development of management capability in the digital health environment, as well as illuminate the system and human resource implications for organisations to facilitate the application of these competencies in the workplace and optimise the benefits of digital health.Implications for health information management practice:The competency enhancements detailed have implications for health service, digital and information management regarding institutional competency frameworks and systems requirements that may need to be upgraded, expanded or introduced, and the continuous professional development that may need to be provided within the institutional or accreditation authorities to health service managers and health information managers.

Background: The World Health Organization's (WHO's) International Statistical Classification of Diseases and Related Health Problems, Eleventh Revision (ICD-11) is a modern classification system that provides enhanced granularity and flexibility for capturing clinical and health system data. For Canada, transitioning from ICD-10-CA, the Canadian modification, to ICD-11 poses opportunities and challenges. To explore these opportunities and challenges more thoroughly, a backward crosswalk was developed to evaluate statistical continuity. This approach helped identify the benefits of ICD-11, while also highlighting potential implications for health systems, case mix, and national health indicator reporting.

Objective: To examine how bidirectional crosswalks between ICD-10-CA and ICD-11 can support Canada's transition to ICD-11; and demonstrate how these crosswalks can be utilised in a Canadian-specific use case.

Method: 14,652 ICD-11 Mortality and Morbidity Statistics (2022 release) codes were mapped to version 2022 ICD-10-CA codes. Each mapping was reviewed to determine the relationship between the ICD-11 and ICD-10-CA codes, categorising them as equivalent to, broader than or narrower than the source ICD-11 codes. The bidirectional crosswalks were applied to a Canadian use case to demonstrate level of specificity between ICD-10-CA and ICD-11 codes.

Results: 26% of the ICD-10-CA target codes were equivalent to a single ICD-11 code, 65% were broader, 9% were narrower and 0.03% had no applicable ICD-11 map. Findings from the Canadian use case showed that 55% of the ICD-11 target codes were equivalent to or narrower than their ICD-10-CA source codes in the forward crosswalk, and 57% of ICD-11 congenital anomaly concepts had greater specificity in the backward crosswalk.

Conclusion: The backward crosswalk assessment highlights the benefits of ICD-11's increased specificity, which has the potential to enhance healthcare data in Canada. However, these findings must be considered alongside the forward crosswalk analysis, which noted a loss in specificity.Implications for health information management practice:As demonstrated in a Canadian use case example, bidirectional crosswalks can be leveraged to better understand the impact of ICD-11 adoption.

Background: As digital technologies advance, vast amounts of routinely collected health data are increasingly available for quality improvement and research. However, concerns persist around the reuse of personal health information. Understanding public attitudes and knowledge is essential to building social licence and enabling ethical, large-scale data use.

Objective: This study explores key research themes in sharing health data for secondary use since 2020, highlighting major topics, emerging research frontiers and future directions for practice.

Method: An analysis of 95 publications from Web of Science, PubMed and Scopus was conducted using scientometric methods. Citation, co-citation and keyword co-occurrence analyses, along with strategic diagrams, were performed using VOSviewer to identify thematic clusters.

Results: Research has shifted from early exploratory studies to more multidisciplinary and technology-focused approaches. Key themes include digital tool adoption, integrated data systems and ethical data sharing solutions. The concept of consent has seen the most theoretical development, while public attitudes - particularly around ethical and sociocultural issues - remain underexplored but crucial.

Conclusion: Ethical governance, transparency and community engagement are central to advancing health data sharing. Building public trust and securing a social licence are foundational to success, especially as challenges around consent, data linkage and public perception remain.Implications for health information management practice:This analysis provides insight into public willingness to share health data for secondary data use and offers guidance for fostering a strong social licence while building public trust. Strengthening these trust and engagement frameworks is vital to achieving ethical data use and maximising the potential health system benefits of secondary data use.

Background: Digitalisation of health care has been ongoing for decades resulting in huge amounts of data that can be repurposed from clinical to administrative and strategic use. However, to be useful and meaningful, data require work by data professionals about whom we do not know much.

Objective: The aim of this study is to investigate the diversity and characteristics of data professionals working in Denmark's five regional healthcare organisations. To achieve the objective of knowing more about who the data professionals in health care are and what they do, we conducted a survey in the regions overseeing hospitals in Denmark. This supplements a previous study at a large, national healthcare data organisation, and together the two studies provide a comprehensive overview of who the data professionals in Denmark are.

Method: An explorative mixed-method approach combining a non-probability technique for design of an open survey with an unknown target population, and 10 semi-structured interviews was applied.

Results: We report on the educational backgrounds, work identities, competences, knowledge areas and how data professionals acquire their skills. There is a striking variety in all of these, though educational backgrounds in social or health sciences, work identities as data analysts and competences and knowledge areas around data and health care dominate. Skills and knowledge are primarily acquired through job experience and current employment.

Conclusion: Data work is conducted by many kinds of data professionals throughout the healthcare sector from specialised data units to data workers at hospital wards.Implications for health information management practice:Initiatives to become data-driven in health care needs to support skill acquisition as part of employment and be available for a broad group of people.

Background: This study extended prior research on employment outcomes of the 2012-2016 and 2017-2021 health information manager (HIM) graduate cohorts from La Trobe University, Australia.

Objectives: (1) To classify graduates' early-career pathways, applying two classificatory methodologies, employing (a) position title (methodology 1), (b) self-reported knowledge and skills-based duties (methodology 2); (2) To compare outcomes of the methodologies; (3) To examine graduates' duties within the profession's established knowledge-skills domains and inform understanding of HIMs' roles.

Method: Residual data from prior cross-sectional studies were applied to early-career graduate HIM positions, using the two classificatory methodologies.

Results: Methodology 1 showed a greater proportion of roles under the "Health Information Management" domain. Methodology 2 revealed more roles aligned with the "Health Information Systems" and "Data Management and Analytics" domains. A longitudinal increase in health systems implementation, system documentation development, database management, and IT system support and maintenance characterised increasing "technologisation" of HIM roles ("Health Information Systems" domain). Clinical coding of admitted episodes ("Health Classification" domain) was the most frequently reported skill utilised in HIMs' first post-graduation position, offset by a slight, longitudinal decrease in clinical coding engagement. The 2017-2021 cohorts progressively reduced involvement in clinical coding roles, instead utilising more skills in the "Health Information Systems" and "Data Management and Analytics" domains. Methodology 1 showed a cohort 1 to cohort 2 increase in graduates assigned to the "Data Management and Analytics" domain (10.2-16.6%); Methodology 2 showed a simultaneous decrease (22.0-15.1%), arguably influenced by artefactual differences. The apparent decline in the "Data Management and Analytics" domain should be viewed cautiously owing to HIMs' increasingly data-driven work.

Conclusion: Reliance on job title may oversimplify or misrepresent the scope of HIMs' duties. Choice of classificatory methodology significantly impacts upon the domain to which HIMs' roles are categorised. The findings support a skill- and duty-based methodology for accurate capture of evolving HIM roles.Implications for health information management practice:This research provides valuable insights into HIM workforce trends, and foundations for (a) longitudinal career pathway and skills mapping and (b) a visual tool (career map) to support the profession's visibility, promotion, and development of illustrative career trajectories.

Background: Haemophilia is a lifelong and chronic disease that has adverse consequences for the patient. The haemophilia registry is a key tool for managing this disease.

Objective: The present study aimed to design a minimum dataset for developing a registry system for haemophilia.

Method: This study was conducted in two stages. In the first stage, in order to conduct a scoping review, PubMed, Scopus and Web of Science databases were searched using relevant keywords up to 4 July 2025. The study selection process was based on the PRISMA guidelines, and finally, 40 articles were included. In the second stage, the data items retrieved from the studies were evaluated and consulted by 14 haematology specialists through a questionnaire. The minimum data items for haemophilia registry were confirmed based on the level of agreement of the participants (more than 75%), and descriptive statistics were used for data analysis, which was performed using the SPSS software (IBM Corp., Armonk, NY, USA).

Results: The initial minimum data items for the haemophilia registry system were extracted from 40 studies. These items included 77 items in 4 main categories: demographic data (21 items), laboratory data (32 items), clinical data (21 items) and adverse outcomes (3 items). Finally, these data items were validated by 14 haematology specialists. In the final dataset, 58 items, distributed across 4 categories, achieved an agreement of more than 75%, comprising 8 demographic items, 28 laboratory items, 17 clinical items and 3 adverse outcome items.

Conclusion: Registries record different data according to their purposes. The importance of this work lies in providing a minimum dataset for registering haemophilia patients in Iran, which can help improve the quality of care, facilitate future research and align with international registry systems for bleeding diseases. Therefore, the findings of this study provide a basis for designing, implementing and improving the haemophilia registry system in Iran.Implications for health information management practice:The findings of this study provide a strong foundation for designing and implementing a National Haemophilia Registry in Iran. This system will standardise and integrate data, prevent duplicate records and enhance treatment planning. It will also support epidemiological and clinical research with links to international databases, while improving patient care, follow-up and reducing complications. Overall, it can help align Iran with global standards for managing bleeding disorders.

Context: The Norwegian Health Archives Registry (NHAR) is a national initiative dedicated to digitising, centralising, and providing access to historical full-text patient health records (PHRs) for research purposes. Established in 2019, NHAR includes PHRs from the deceased population in Norway's specialist healthcare services, offering a unique long-term data source for future research. NHAR has now digitised 1.7 million paper-based PHRs, covering medical history dating back to 1875. The registry is now expanding to include digital-born PHRs.

Aim: This article describes NHAR's innovation potential as a health registry, its data management processes, and the integration of artificial intelligence (AI) tools to facilitate data management and research in compliance with strict health data regulations.

Practice innovation: NHAR's data value chain includes structured metadata acquisition, large-scale digitisation and secure data delivery for research. The workflow includes a custom optical character recognition (OCR) tool tailored to Norwegian medical terminology, concept-based search tools for unstructured clinical full text and robust strategies for long-term data management. A novel AI-based de-identification system automatically detects and masks personal identifiers in digitised PHRs.

Lessons learned: Despite these innovations, challenges persist in processing handwritten and historical PHRs due to OCR limitations and language-specific complexities. Key challenges include improving data quality, enhancing OCR accuracy and refining AI tools for information retrieval, data extraction and de-identification.

Conclusion: NHAR offers significant potential for interdisciplinary research across various medical fields.Implications for health information management practice:NHAR establishes a foundation for secure access to historical health data and introduces advanced data management strategies to facilitate future research.

Background: Clinical registries are essential in oncology for monitoring the quality of patient care and supporting research. However, maintaining these registries is resource-intensive and can burden clinical staff. Technologies such as artificial intelligence (AI) now offer the ability to automatically extract data from electronic medical records into registries, with the potential to lower costs and improve efficiency.

Objective: To examine the practical opportunities and challenges of automating oncology registries, using key lessons from the partial automation of the Australian Brain Cancer Registry (ABCR).The innovation:This analysis draws on the ABCR project experience, detailing the use of technologies ranging from discrete data extraction to advanced AI. It outlines the multidisciplinary approach required and discusses key factors relevant to registry automation.What can be learnt from this case?Successful registry automation relies on close collaboration between clinicians, researchers and programmers. Human oversight remains essential, particularly when the AI is uncertain about specific data points. Key factors for effective automation include clearly defined data elements, strong communication among stakeholders, robust safeguards for patient privacy and planning for long-term sustainability and interoperability of the registry. It is also important to avoid introducing bias by over-prioritising data that are easiest to extract automatically.

Conclusion: Automating cancer registries can reduce costs but requires thorough planning. The optimal approach may involve humans and machines working together.Implications for health information management practice:Giving early attention to data accuracy, patient privacy and the long-term sustainability of the registry is critical for long-term success.

Background: The regulatory and legal contexts of health information systems (HIS) play a crucial role in the generation and utilisation of health information. However, in many low- and middle-income countries, the regulatory framework for HIS remains underdeveloped, often hindering effective data management and system implementation. Objective: To evaluate how HIS policies, strategy and protocols guide the use of the health management information system (HMIS) for HIV and AIDS monitoring and evaluation (M&E) in Ethiopia. Method: An evaluation study using qualitative document analysis was adopted. Purposive criterion sampling was used to select government policy documents. Data extraction tool and management strategy were implemented. Atlas.Ti version 8 was used for content analysis. Results: Ten documents met the required criteria. The guidelines in the documents emphasised data quality assessment through data quality assurance techniques, data quality tracking of completeness and timeliness logbook and District Health Information System Version 2 (DHIS2) checks. Evidence from documents revealed broad guidance in terms of the implementation of M&E processes. Content showed clear definitions of information use at different levels and strategies to promote culture of data use. Conclusion: The study revealed that Ethiopia has an adequate regulatory framework for HIS to support the implementation of the HMIS in M&E of the HIV and AIDS program. However, the documents also acknowledge challenges with the implementation of these guidelines. Implications for health information management practice: This work highlights the importance of HIS and HMIS to the entire health system, and the vital role played by health information managers (HIMs) in managing the resources, people, protocols, and datasets essential for a functional information system, and for ensuring secure access to data for evidence-based decision-making.