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

Background: Automated clinical coding can use statistical or artificial intelligence-based technology to transform unstructured clinical data into clinical codes. These processes have the potential to enhance the quality and accuracy of data collections, save resources and accelerate research.

Objective: To evaluate the use of automated clinical coding in the United Kingdom (UK) and European cancer registries.

Method: An online electronic survey was formulated to evaluate the use and user opinion of automation within cancer registries. The survey was distributed to members of the United Kingdom and Ireland Association of Cancer Registry and the European cancer registries. Data analysis was performed using Microsoft Excel 2015^® version 15.13.3 in order to summarise the results.

Results: Twenty-three of the 117 cancer registries responded to the distributed survey; 15 (12.8%) cancer registries used automation within their registry, mainly in the form of natural language processing or machine learning. Most of the sampled registries (73.3%) used these technologies to automate data collection from pathology reports; 87% of respondents reported automation as efficient; and 26.1% reported improved data quality; 12 (52.1%) of cancer registries still manually checked all the automations; and 17 (74%) respondents believed that the algorithms for difficult tasks require further development.

Conclusion: Various computer-based algorithms have been used for automated clinical coding in the UK and European cancer registries in the past few decades; however, to date there are no published data to validate its use. Further research and development of these technologies is needed to ensure external validity and maximise the potential use within other cancer registries globally.Implications for health information management practice:It is clear that while automation can be advantageous in areas of clinical coding, the role of the "human" (HIMs and clinical coders) in coding and classifying registry data, and in overseeing the transition, will be required for some time yet.

Background: Robust population health surveillance is required to monitor trends in prevalence in congenital anomalies (CA) and to detect emerging threats to human development. All eight Australian states and territories are mandated to report CA data to national authorities. Objectives: (i) Compare Australian congenital anomaly registers (CARs) across jurisdictions; (ii) measure research utilisation of Australian CAR data. Method: We conducted a documentary analysis of publicly available online information on Australian CARs and performed a scoping review of peer-reviewed research published from 1980 to 2024 that utilised CAR data. Results: Five Australian states/territories possessed an established CAR; three practiced active surveillance, and three included mandatory reporting. Age of child inclusion criteria ranged from birth episode to 6 years. Most states/territories classified CAs according to the International Classification of Diseases 10th Revision Australian Modification (ICD-10-AM). There was inconsistency in scope, data sources, method of ascertainment, data linkage processes, data availability, reporting requirements and data quality. The scoping review identified 83 peer-reviewed publications using CAR data. The majority of publications originated from three states/territories and included key CAR staff as authors. Only one state/territory CAR consistently published research over the 44-year review period. Conclusion: There are major methodological inconsistencies among Australian CARs, undermining the interpretability and quality of nationally reported CA data. More standardisation and resourcing are required to maximise the research and policy value of Australian CARs. Implications for health information management practice: Urgent attention to data management practices, harmonisation across jurisdictions and resourcing are required to safeguard the sustainability and value of Australian CARs.

Background: The Australian Childhood Immunisation Register (ACIR), established in 1996, captures details of vaccinations given to children aged <7 years, expanded in 2016 to the whole-of-life Australian Immunisation Register (AIR). Objective: Overview of ACIR/AIR, how health information captured is managed and how AIR data facilitate insights into vaccination reporting trends. Method: The authors, with 58 years of collective experience in analysing and interpreting ACIR/AIR data, reviewed formal and grey literature relevant to ACIR/AIR and their operation and use. We analysed AIR data to document how data transmission to AIR and vaccination provider settings has evolved. Results: We describe policy and program changes instrumental to the ACIR-AIR expansion, AIR data fields, methodology for measuring population-level vaccination coverage, and ways data are used for: monitoring and evaluation of immunisation programs; public health surveillance; linked data analyses; vaccine effectiveness studies and other research. We show evidence of changing vaccination landscape including increasing trends in electronic data transmission (e.g. proportion of vaccinations given to children aged <10 years and notified to ACIR/AIR using practice management software increased from 56% in 2014 to 89% in 2023) and increase in vaccinations given in pharmacies (e.g. proportion of influenza vaccinations given to adults aged 20-64 years in pharmacies increased from 0.9% in 2017 to 26.9% in 2023). Conclusion: The AIR has been instrumental in monitoring and evaluating the reach and impact of Australia's publicly funded immunisation programs across the life course. Implications for health information management practice: Health information managers working with vaccination data contribute to the AIR through data management and upload to the AIR.

Background: Blood transfusion is a common medical intervention. For patients with acute critical bleeding, large volume "massive" transfusion (MT) is required, and is potentially life-saving. However, the evidence-base for transfusion practice, particularly for critical bleeding/MT management, is relatively weak, and has confounded the development of clinical best practice recommendations.

Aim: The aim was to address this evidence gap by building the Australian and New Zealand Massive Transfusion Registry (ANZ-MTR). We describe how data collection, standardisation and interoperability of data sourced from multiple electronic information systems are managed, and share the lessons learned.

Innovation: The ANZ-MTR is a database of routine electronic hospital admission information, laboratory test results, transfusion records and outcomes of adults (18 years and older) who have received a MT for any cause of acute critical bleeding, including trauma, major surgery, obstetric or gastrointestinal haemorrhage. Source data are provided by participating hospitals and are harmonised by the registry. Since its launch in 2011, the ANZ-MTR has captured over 9200 MT episodes from 29 hospitals.What can be learned from this case:Effective communication with all custodians of the source data has been fundamental to the success of the registry. A preeminent outcome of this success is the current expansion of the registry to become the National Transfusion Dataset, which will capture comprehensive data for all transfusions.Implications for health information management practice:The ANZ-MTR illustrates that complex and varied arrays of routinely collected clinical and hospital administrative data from multiple electronic information systems can be consolidated into a resource-rich clinical database.

Background: Clinical registries are long-term observational data collections relating to specific medical conditions, procedures, devices or health services. Objective: To assess current characteristics and outputs of clinical registries in Australia. Method: A cross-sectional survey design of Australian clinical registries listed on the Australian Commission on Safety and Quality in Health Care (ACSQHC) register as of 21 September 2023. Results: Of 107 clinical registries on the ACSQHC register that were contacted, 64 (60%) participated in the survey. Of these, 37 (58%) had been active for ⩾10 years, 38 (59%) were medical clinical registries and 35 (57%) received government funding. Clinical registry activities included research (92%), quality improvement (81%) and epidemiological monitoring (68%). Data were commonly patient-identifiable (64%) and collected by clinicians/staff (81%). A majority (55%) had real-time data available to contributing hospitals. Clinical registry outputs included providing data to researchers (97%), publications (83%), annual reports (69%) and site benchmarked reports (64%). Over half informed quality improvement activities (60%), monitored adherence to guidelines (59%) or informed policy or service planning (52%). Nearly half-supported clinical trials (49%), while one-fifth had integrated with government data frameworks. Conclusion: Australian clinical registries monitor health system performance across a breadth of clinical areas. A majority undertake regular public and hospital reporting and inform other quality improvement activities. Implications for health information management practice: Clinical registries interact with hospitals regarding their data collection and reporting activities. Health information management specialists have an important role in maximising registry data quality and therefore value to a wide variety of stakeholders.

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: 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.

Background: Genetic conditions significantly impact health and contribute to paediatric morbidity and mortality. Despite advancements, accurate estimation of the burden of genetic conditions remains complex. Objective: To determine how different data sources and ascertainment methods influence the prevalence of paediatric monogenic and chromosomal conditions in Australia and internationally. Method: Following Arksey and O'Malley's framework for scoping reviews, a systematic search of Medline, CINAHL, Scopus and Google Scholar identified peer-reviewed studies (2004-2024) including snowballing of references. Studies were included if they reported on at least one monogenic and/or chromosomal condition, involved children under 6 years of age, identified the data source, reported prevalence, and were conducted in Australia, New Zealand, Europe or North America. Data sources, type of case ascertainment and prevalence of genetic conditions were extracted from eligible studies. Descriptive analysis was used to summarise study characteristics, including year of publication, region, condition type, data sources and ascertainment methods. Results: Of 58 included studies, 57% originated in Europe, 5% in Australia and 78% were published post-2010. Overall, 36.2% examined monogenic disorders and 29.3% chromosomal. Registries were the most common data source (62.1%), with 78% using active case ascertainment. Main strategies included medical record abstraction (30%), genetic testing (27.5%) and International Classification of Diseases (ICD)-coded data (27.5%). In Australia, genetic testing and medical records yielded higher prevalence than ICD-coded data; internationally, disease-specific registries which use active ascertainment approaches reported greater prevalence than passive ascertainment approaches. Conclusion: Findings highlight how data source selection and ascertainment methods influence prevalence estimates, risking under-ascertainment when relying solely on ICD-coded data. In Australian studies, disease registries were not utilised, reflecting the need to address Australia's fragmented surveillance infrastructure by integrating Orphanet nomenclature of rare diseases (ORPHAcodes) with ICD-coded data and expanding registries. Implications for health information management practice: Strengthening national coordination, training in genetic coding, nomenclature and inheritance mechanisms, and broader workforce competency will improve prevalence estimates of genetic conditions.

Background: There is increasing interest in the public reporting of health provider benchmarking within clinical registries to identify underperforming sites (also known as outliers). As such, research into the optimal methods and ideal conditions for outlier detection is important. Objective: The aim of this study was to assess the accuracy of benchmarking and outlier classification methods for different values of clinical registry sizes and case volume minimums. Method: Clinical registry datasets were parametrically simulated varying the following parameters: number of sites, clinicians, patients and outcome events, case volume minimum and outcome prevalence. Two benchmarking models (unadjusted and risk-adjusted with logistic regression) and two outlier classification techniques (confidence intervals and control limits) were applied to each simulated dataset. The accuracy of outlier flagging was assessed using the receiver operator characteristic area under the curve (ROCAUC). Results: Risk-adjusted benchmarking performed better than unadjusted benchmarking across the registry sizes evaluated, providing up to a 20% increase in ROCAUC. The number of sites and clinicians had little effect on performance, while higher accuracy with increasing number of patients per site and outcome prevalence was observed. A threshold of 100 to 150 outcome events per site was needed to reach >80% ROCAUC. Conclusion: The use of low prevalence outcomes for benchmarking hospitals to detect outliers may be inappropriate, especially for clinical registries with low patient volumes. Implications for health information management practice: Clinical registries should consider their patient volumes and outcome prevalence before commencing benchmarking analyses to determine if acceptable accuracy can be achieved for their setting.