BACKGROUND:Hospitals have been struggling to effectively and meaningfully act on data from digital health technologies, compromising their ability to transform into learning health systems. However, the existing literature offers limited guidance on how to improve the effective use of these technologies. OBJECTIVE:The objectives of this article are to identify: (i) the factors that can hinder the effective use of digital health technologies; and (ii) the learning mechanisms that can improve the effective use of digital health technologies in hospital settings. METHOD:A qualitative study embedded in the Division of Surgery of an Australian public hospital was performed. Semi-structured interviews were conducted with 22 participants across different professions. The interviews were deductively analysed using an a priori developed classification framework. RESULTS:The effective use of digital health technologies is hindered by deficits in individual capabilities (n = 3), organisational capabilities (n = 6), and system capabilities (n = 4). To improve upon these capability deficits, targeted learning mechanisms can be used. CONCLUSION:Effective use of digital health technologies is influenced by individual, organisational, and technical system capabilities, which can be improved through comprehensive, proactive and reactive learning efforts. To improve the effective use of digital health technologies in support of broader transformation efforts, five recommendations for healthcare organisations were distilled.Implications for health information management practice:Healthcare organisations embarking on digital transformation should: (i) rethink traditional roles and responsibilities; (ii) redesign ill-suited workflows and models of care; (iii) create and embed robust data governance; (iv) optimise their digital health technologies on an ongoing basis; and (v) build a data-driven culture to foster clinical engagement.
BACKGROUND:Accurate and complete clinical documentation underpins the assignment of International Classification of Disease codes used for epidemiological research, hospital activity based funding, and health service planning. Objective: To develop, implement, and evaluate an education program for clinicians and clinical documentation specialists (CDS) to improve clinical documentation of stroke. METHOD:An education program to improve clinical documentation of stroke was developed by the Australia and New Zealand Stroke Coding Working Group. Eligible participants were clinicians (doctors, nurses, and allied health) and CDS in Australia or New Zealand. The education program comprised 4 modules in a pre-recorded 10-minute educational video. Surveys were administered before and after the educational video to evaluate knowledge (Kirkpatrick level 2) and obtain feedback. Quantitative data were summarised using descriptive statistics. Open-text responses to the feedback survey were analysed using inductive thematic analysis. RESULTS:Among 72 eligible participants, 41 (57%) completed the pre- and post-educational knowledge assessment surveys (n = 35 clinicians, n = 6 CDS). Compared with the pre-education survey, the median number of correct responses significantly increased in the post-education survey (pre: 3 [interquartile range (IQR) 2-4]; post: 5 [IQR 4-5]; p < 0.001). Most respondents (>90%) were satisfied with the education program, stating it provided "practical and transferrable knowledge" and that the education was "quick, clear, concise." CONCLUSION:Our education program was associated with an increased knowledge of appropriate clinical documentation of stroke. Ongoing monitoring of clinical coding and clinical documentation is required to ascertain whether the education of the participants translates to improved clinical coding of stroke.Implications for health information management practice:Accurate and complete clinical documentation directly impacts clinical coding, which in turn affects reimbursement, patient safety, and data quality for epidemiological research, resource allocation, and health policy decision-making.
BACKGROUND:In Australia, a patient's pathology results are held in separate provider systems, so clinicians often cannot see a full testing history during a consultation, which delays decisions and drives unnecessary repeat testing. Point-of-care access to a complete pathology record has been proposed as a remedy, but its feasibility and effect in routine outpatient practice remain poorly established. OBJECTIVE:To evaluate whether point-of-care access to a patient's complete pathology history is feasible and whether it improves the efficiency of outpatient care and lowers costs. METHOD:We piloted myPathology, a cloud-based application that integrates atomic pathology results from all major Western Australian providers from 2005 onward, across 4 outpatient settings over 12 months. Outcomes were assessed through clinician surveys, Medicare Benefits Schedule expenditure records, and an audit of tests normally performed once or rarely repeated. Changes in pathology spending were assessed using interrupted time series analysis. RESULTS:All 21 participating doctors (3 general practitioners and 18 specialist physicians) used the application throughout and reported more efficient workflows. Specialist physicians saved an average of 16 minutes and ordered 8.3 fewer tests per clinic session, and 13 of 18 (72%) reported starting treatment earlier. Outpatient pathology spending fell by about 66%, equivalent to AUD 201,685. The time series analysis showed an immediate fall in spending (change in level, β = -0.473, p < 0.05) and a continued downward trend (change in trend, β = -0.132, p < 0.01). Unnecessary repeats of once-only tests accounted for 37.8% of the cost of those tests (AUD 41,781). CONCLUSION:A comprehensive, longitudinal pathology platform was feasible to deploy and was associated with earlier treatment, more efficient workflows, lower costs, and less redundant testing. These benefits were more evident in specialist practice than in general practice, where the sample was small.Implications for health information management and practice:Interoperability delivers value only when clinicians can find, trust, and act on diagnostic data during a consultation. Storing results as discrete, searchable data points, rather than static documents, lets clinicians track trends and avoid duplicate tests. Delivering this at scale will require automated data exchange with providers and agreements that support clinically appropriate ordering.
BACKGROUND:Mobile applications for infectious disease surveillance can facilitate early outbreak detection and timely public health action. However, there is limited information about their real-world adoption, public trust, perceived usefulness, and preferred features. OBJECTIVE:To assess public trust, usability, and adoption of infectious disease surveillance mobile apps and to identify the features preferred by the public. METHOD:An anonymous, online, cross-sectional survey was conducted across Australia between May and August 2024. A total of 5177 adults aged ⩾ 18 years completed the survey. Descriptive statistics and multivariable logistic regression analyses examined sociodemographic correlates of mobile app use, trust, and perceived usefulness. RESULTS:Use of infectious disease surveillance apps was low, with only 151 participants (2.9%) reporting prior use. Despite this, 60% (3122/5177) perceived such apps as useful, and 15% (790/5177) reported trust in them. Younger adults were significantly more likely to use and trust these apps than older adults. Occupation in a health-related field (Odds ratio [OR] 1.89; 95% Confidence interval [CI] 1.21, 2.93), frontline responder status (OR 3.33; 95% CI 1.86, 5.96), postgraduate education (OR 2.04; 95% CI 1.20, 3.46), and speaking a non-English language at home (OR 2.72; 95% CI 1.50, 4.91) were significant predictors of app use. Men reported greater trust (OR 1.20; 95% CI 1.01, 1.43) but lower perceived usefulness (OR 0.81; 95% CI 0.72, 0.92) than women. Participants most frequently preferred features providing real-time updates and affected-area maps. CONCLUSION:Although most participants perceived mobile apps to be useful, their adoption was low. Use may vary over time and increase during pandemics, severe epidemics, or travel. Trust diverged from perceived usefulness in specific groups, and sustained use clustered with greater confidence. Design and implementation of infectious disease surveillance apps should emphasise high-value features, language equity, targeted onboarding for older adults, and strategies that convert trust into perceived usefulness.Implications for health information management practice:These findings highlight the need to integrate trust, usability, and accessibility into app design. Prioritising user-centred interfaces, language inclusivity, and transparent data practices in all facets of healthcare services, including the management of health information, can enhance engagement, improve data quality, and support effective digital disease surveillance. The health information manager's skillset would be essential for informing the successful integration of mobile health app data into electronic medical or health record systems.
Background: The development of the International Consortium for Health Outcomes Measurement (ICHOM) standard sets reflects an increasing awareness of the need for systematic, standardised data collection on patient outcomes. Objective: To describe the implementation of the ICHOM standardised set for hand and wrist conditions in the Department of Hand and Peripheral Nerve Surgery at a large, referral hospital in Sydney, Australia. Method: Patient outcomes were collected according to ICHOM-defined clinical pathways for three common procedures: carpal tunnel release, trigger digit release, and distal radius fixation. Response rates were recorded at preoperative and condition-specific follow-up time points over two 3-month periods before and after the employment of a dedicated research officer. Results: Sixty-three patients were treated prior to the implementation of a research officer and 87 patients after. Pre-operative data completion improved from 35% to 93%; 3-month patient reported outcome measures from 25% to 64%; and 3-month clinician reported outcome measures from 0% to 40%. Conclusion: The ICHOM standard set was successfully implemented in an Australian public hospital, aided significantly by the employment of a research officer. Implications for health information management practice: Standardised outcome measurement can be embedded in existing clinical workflows utilising a framework including dedicated personnel, robust information technology infrastructure, and patient engagement strategies. This template is broadly applicable across institutions and facilitates a shift towards value-based healthcare metrics.
BACKGROUND:The effectiveness of routine health information systems (RHIS) depends significantly on the self-efficacy and actual competence of healthcare professionals in managing health information. Despite investments in health information infrastructure, gaps between perceived abilities and actual skills continue to undermine system performance. OBJECTIVE:This study assessed self-efficacy levels and competence gaps among healthcare professionals in RHIS tasks within Cape Coast Metropolis, Ghana. METHOD:A mixed-methods cross-sectional study was conducted among 265 healthcare professionals across 13 health facilities in the Metropolis. Self-efficacy was measured using a validated 7-item scale with responses ranging from 0 to 100. Actual competence was assessed through standardised paper-and-pencil tests across five RHIS task domains: data quality checking (verifying accuracy, completeness, and timeliness), statistical calculations (computing rates and percentages), data plotting and visualisation, interpretation of findings, and information utilisation for decision-making. Knowledge of RHIS rationale and problem-solving skills were also evaluated. Descriptive statistics, correlation analysis, and gap analysis were performed on the data. RESULTS:The mean self-efficacy score was 36.8% (95% confidence interval (CI) ± 2.22), with perceived confidence in calculating RHIS tasks showing the highest level (39.8%) and using data to make decisions showing the lowest (33.8%). Actual competence averaged 19.9% (95% CI ± 10.6), with calculation skills achieving the highest performance (28.1%) and plotting skills the lowest (11.9%). Significant gaps existed between self-efficacy and actual competence across all domains, with the largest gap in plotting (26%) and smallest in calculations (11.7%). Only 25% demonstrated adequate knowledge of data collection rationale, and fewer than 4% demonstrated adequate problem-solving skills. CONCLUSION:Substantial gaps exist between healthcare professionals' perceived self-efficacy and actual competence in health information management tasks in Cape Coast Metropolis. Persistent overconfidence, weak problem-solving skills, and limited understanding of data collection rationale collectively undermine RHIS performance.Implications for health information management practice:Targeted, practical capacity-building programmes addressing both technical skills and self-assessment accuracy are urgently needed in Cape Coast Metropolis and comparable low- and middle-income countries settings.
Background: Work-integrated learning (WIL), a cornerstone of the university education of health information managers, combines academic theory with workplace-based practice to enhance graduate career-readiness. Recent curriculum changes to the two health information management (HIM) degrees at La Trobe University, Australia, involved the adoption of a project-based learning (PjBL) model, replacing an internship model, for final-year (capstone) professional practice placements. The University’s project placement documentation was comprehensively revised to inform and support healthcare industry supervisors in accommodating these changes. Objective: To investigate industry supervisors’ perceptions of the effectiveness of documentation re-developed to frame and support project-based, final-year professional practice (WIL) placements. Method: A cross-sectional study design utilised a self-administered, online survey of the industry supervisors of the 2024 final-year HIM placement students. The survey instrument captured feedback on the usability and user-friendliness of the first iteration of revised project documentation, including supervisor guidelines. Results: Most respondents (93%) reported that the guidelines clarified project-based placement requirements, with 80% rating them as useful or very useful. The revised proposal template was perceived to be user-friendly by 93% of industry supervisors. Suggested areas for improvement included clearer articulation of student skillsets, provision of examples of previous placement projects, and enhanced guidance and support for creating and writing learning outcomes (LOs) to frame project-based placements. Conclusion: Evidence-informed documentation supports authentic, inquiry-driven professional practice placements that successfully bridge theory and practice. The study produced recommendations for strengthening PjBL placement documentation to assist industry supervisors of final-year students, including refinements to (a) enhance usability of the new iteration of the placement documentation, (b) inform the development of robust LOs aligned with the national HIM professional competency standards, and (c) emphasise projects pitched to match student capabilities. Implications for health information management practice: This study has advanced our knowledge of WIL in HIM education by demonstrating how purposeful, stakeholder- and education theory-informed documentation can guide industry supervisors in applying a project-based approach. Embedding the PjBL model and principles into HIM placements provides opportunities for augmenting the development of profession-ready graduates, and for underpinning students’ acquisition of the transferable skills essential for navigating complex HIM environments.
Service context: Promoting value-based maternity care requires maternity services to actively monitor costs and outcomes, where value is defined as patient outcomes achieved relative to the costs of care, rather than focusing solely on cost minimisation. Aim: This study aimed to present an applied, real-world example of how linked health administrative and clinical cost data can be accessed, managed, and analysed, and to demonstrate their use in evaluating health service value within a value-based care framework. Practice innovation: To facilitate broader adoption, we provide fully annotated open-source code and detailed documentation to guide researchers in applying this approach to their own administrative perinatal data. Lessons learned: Reporting and analysing costs should be an ongoing part of service delivery and are enabled by the vast amounts of data that are routinely collected. Conclusion: This study demonstrates how to measure costs of the entire pregnancy journey using existing administrative data and to implement this as a system-wide, routine monitoring metric at the health system level or even whole of state/country level to support benchmarking. Implications for health information management practice: Better utilising existing routinely collected health data can support maternity services to implement cost measurement as a part of performance monitoring, identifying areas of service delivery that are high and low value, and evaluating any changes in service delivery into routine practice.
BACKGROUND:Increasing concern regarding long-term consequences of mild traumatic brain injury (mTBI; concussion) highlights the need for an accurate understanding of its epidemiological landscape. Current estimates of mTBI presentations to the emergency department (ED) are based on principal diagnosis International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) S06.0~ concussive injury codes assigned by the treating ED clinician in the ED information system (EDIS). While mTBI is defined by absence of loss of consciousness (LOC) or LOC < 30 minutes, the S06.0~ codes include injuries associated with longer periods of LOC. To ensure reliable case estimates to inform healthcare resource allocation, it is essential to understand which S06.0~ codes most accurately capture mTBI and to minimise its misclassification. OBJECTIVE:To identify the positive predictive value (PPV) of principal diagnosis S06.0~ codes for mTBI using information from EDIS and retrospective chart review. METHOD:All episodes of care assigned principal diagnosis S06.0~ codes between 1 July 2022 and 30 June 2023 at Royal Perth Hospital ED, Western Australia, were identified, and clinical diagnoses were confirmed via medical record review. PPVs were calculated for each S06.0~ code, and logistic regression models implemented (RStudio v4.3.3) to explore the influence of patient characteristics on coding. RESULTS:Code S06.00 was most frequently assigned (n = 279, 51.7%). No cases were coded S06.04 or S06.05 (>24 hours LOC). For mTBI, the adjusted PPV of S06.0~ was 71.8% (95% confidence interval (CI) 66.85-76.22), increasing to 74.0% (95% CI 68.72-78.69) after excluding S06.03 (LOC 30 minutes to 24 hours). The highest PPV was observed for the four-digit S06.0 code (adjusted PPV = 87.0%; 95% CI 74.36-93.90). PPVs were lower among patients arriving via emergency transport, older adults (>65 years), and cases without a documented injury mechanism. CONCLUSION:There are limitations to using ICD-10-AM concussive injury codes to identify mTBI in ED settings, and patient characteristics influence coding accuracy. Reconsideration of current ED coding practices, including exploration of alternative models that reduce clinician burden and improve coding accuracy, is recommended.Implications for health information management practice:The introduction of electronic medical records provides an opportunity to standardise documentation of key clinical findings and develop artificial intelligence tools to improve coding of mTBI.
Background: In Ghana, the adoption of health information governance (IG) by hospitals remains in its early stages, despite the establishment of a national legislative framework mandating the reporting of health record-related information by both private and government-assisted hospitals. Objective: To assess the level of awareness among hospital IG and clinical staff regarding the privacy and security components of IG in Kumasi, Ghana. Methods: A quantitative study was conducted from September to November 2021. An online survey was administered to a proportionately weighted sample of 330 eligible medical doctors and nurses from our purposively selected hospitals in Kumasi, each with ⩾50 beds. Results: A total of 307 valid responses were obtained, representing a 93% response rate. Over half of the respondents (53%) demonstrated limited awareness of IG programs designed to manage confidential health information. Approximately half (50.3%) reported having knowledge of data breach and privacy policies. The most cited barriers to IG implementation were cost (76.6%, n = 216) and implementation complexity (65.1%, n = 200). Nearly one-quarter of respondents (24.7%) reported having experienced a data breach, with loss of information (24.3%) identified as the most significant potential consequence for their health facilities. Conclusion: The hospitals studied lacked organisation-wide IG program, and overall awareness of health information security among staff was low. Implications for health information management practice: These findings highlight the urgent need for appropriate measures to (a) engage staff and other stakeholders in the development and implementation of IG programs, (b) raise awareness of health information security practices within Ghanaian hospitals and (c) establish formal education programs for professional health information managers.
BACKGROUND:Artificial intelligence (AI) transforms healthcare data collection, analysis, and application, making AI proficiency a growing necessity across health professions.ObjectiveThis study aimed to examine the influence of demographic factors on AI literacy among Health Information (HI) professionals, identify key knowledge gaps and inform workforce-aligned training recommendations. METHOD:This mixed-methods study analysed convenience-sampled survey data on AI literacy among HI professionals. Quantitative responses were examined with descriptive statistics, t-tests, Analysis of Variance (ANOVA), Spearman rank-order correlation, linear regression, geospatial analysis and a random forest to examine AI knowledge across demographic groups. The one qualitative open-ended response was analysed with latent Dirichlet allocation (LDA) topic modelling to identify themes.ResultsA total of 128 valid responses were analysed, including 22 participants who completed the technical knowledge section and 48 who responded to the open-ended question on AI education. Higher educational attainment and geographic location significantly predicted greater general AI literacy. However, no significant associations were found between AI literacy (general or technical) and age group, possession of non-health informatics credentials or prior AI experience. The cross-validated Random Forest models were assessed with and without oversampling. Accuracy was identical across both models (0.95), indicating that the overall prediction correctness of low versus high AI literacy was not affected by oversampling. The oversampled model had a superior ability to detect the minority class, making it more suitable for imbalanced classification tasks where recall is critical. CONCLUSION:This study identified several important knowledge gaps on the influence of demographic factors on AI literacy, which informs workforce-aligned training recommendations. These findings underscore the need for competency-based education to strengthen AI readiness within the health information workplace.Implications for health information management practice:The thematic analysis demonstrated the urgent need for AI knowledge, training and literacy for HI professionals and students. Themes from the LDA topic modelling informed the development of AI educational frameworks, structured into domains, subdomains and specific components of educational competencies. With multidisciplinary collaboration and further research, standardised AI core competencies for HI professionals could be created, validated by experts and adopted across educational programs to improve AI literacy in the HI field.
BACKGROUND:This study shares insights from clinical trialists who have conducted investigator-initiated trials that have linked trial data to administrative data, focusing on the challenges and facilitators of this approach. OBJECTIVE:To provide recommendations for evaluating the feasibility and suitability of using administrative data in clinical trials. METHOD:A convergent parallel mixed-methods study was conducted, surveying Australian clinical trialists and operations staff. Participants could opt-in to in-depth interviews. Survey data were analysed using descriptive statistics, while thematic analysis was applied to interview data, with findings integrated during interpretation. RESULTS:Four main themes and 10 sub-themes were identified as critical when evaluating the suitability of administrative data for clinical trials: (i) "trial management considerations" covers operational factors like budgeting, timelines and staffing; (ii) "assessing burdens vs. gains" encourages weighing up the research benefits with the additional operational and consent considerations; (iii) "data preparation and analysis" addresses the processes involved in preparing and analysing data for linkage between trial and administrative datasets; and (iv) "training and support" emphasises the need for researcher support when using linked data. CONCLUSION:Researchers should carefully evaluate the feasibility of using administrative data, considering costs, required skills, timelines and data accuracy. They must also be prepared for delays due to data request processes, participant consent requirements and the mandated use of data access platforms. Early planning can mitigate later complexities.Implications for health information management practice:This study highlights the value of health information managers in clinical research, particularly in managing electronic health records and clinical coding. Their expertise in these areas, as well as in data governance and system architecture, can support clinical trials that link to administrative data.
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: 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: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: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.
