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

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.

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 increased demand for health information managers (HIMs) to work at clinical registries. Objective: To explore and describe the (i) roles and responsibilities of HIMs and (ii) reported HIM workforce within Australian clinical registries. Method: Two cross-sectional surveys were undertaken with qualified HIMs and data custodians. Respondents were engaged through snowballing methods. Descriptive statistics were used to summarise quantitative data from both surveys. Inductive thematic analysis was used to summarise free-text responses. Results: Sixteen HIMs completed the survey (94% female; 50% aged <40 years; median 8 years post-graduation). The majority reported varied roles and responsibilities which spanned most of the domains of the profession, particularly tasks related to data and database management (81%), and data analysis and reporting (81%). Some HIMs are under-utilised, identifying that they would be more satisfied in their role if they could "use more of [their] health information management skills." From 27 responses to the data custodian survey, 12 employed HIMs and demonstrated a good understanding of their skills, which aligned with responses to the HIM survey. There was a gap identified in database management and analysis skills (n = 4) at clinical registries that do not employ HIMs. Conclusion: HIMs play an important role within clinical registries, especially related to data management, analysis and reporting. Ongoing advocacy is required to increase the understanding of HIMs' skills and to increase the responsibilities and number of HIMs working at clinical registries. Implications for health information management practice: HIMs are well-positioned to contribute to Australian clinical registries.

Background: The Australian Breast Device Registry (ABDR) monitors breast device safety by collecting procedure data from clinicians across Australian jurisdictions. Ensuring high case ascertainment, including implant insertion and revision, is essential. By linking with an administrative dataset, the ABDR can identify hospital and procedure-level data gaps to assess case ascertainment more effectively, supplementing previous efforts using breast device sales data. The aim of this project was to link ABDR data with the Victorian Admitted Episodes Dataset (VAED) to determine total and procedure-level case ascertainment and to provide feedback to participating sites regarding their data capture to support quality improvement. Method: The ABDR applied to the Centre for Victorian Data Linkage (CVDL) to administer the data linkage, employing a series of Australian Classification of Health Intervention (ACHI) procedure codes. Then, using this data, the ABDR produced site-specific case ascertainment reports. Results: The CVDL was able to match ABDR breast device-related procedures to the VAED dataset, demonstrating an overall 79% case ascertainment. Tissue expander removal and implant insertion procedures were most commonly captured (89%) and those least captured were tissue expander revision and removal or replacement procedures (59%). Customised site-specific reports were developed and distributed, comprising a series of benchmarked line graphs to track site procedure ascertainment over 6 years. Conclusion: Data linkage informed ABDR total and procedure-level case ascertainment in Victorian public and private hospitals. Reporting back to hospitals, their individual case ascertainment is integral to addressing gaps in case reporting and improving overall registry data capture and completeness. The registry proposes to complete data linkage annually in Victoria to monitor improvements in case reporting, and explore using data linkage in other health jurisdictions in the future. Implications for health information management practice: Data completeness is critical to data quality and use for clinical decision-making. Third-party data verification processes are a useful activity to enhance the quality of health service-contributed data to registries.

Background: Registries monitor treatment pathways and outcomes, driving healthcare improvements. However, registry outputs often target professionals, hindering consumer understanding. National strategies advocate for registries to engage in consumer co-design to develop accessible, consumer-friendly resources that empower the community to make informed decisions using registry outputs. This publication outlines the process undertaken to translate four registry outputs into consumer-friendly resources. Objective: To develop resources that support consumers to understand and use registry outputs to make informed healthcare decisions. Method: The Consumer-Friendly Information project employed a three-stage co-design approach over 12 months: establishment, consultation and resource development. A mix of lived experience and general consumers were recruited through diverse channels including consumer organisations and clinicians. Consumers were educated on registry outputs, identified key messages and contributed to resource creation through prototypes and iterative feedback. Audience-specific considerations and continuous communication between consumers, registries, and designers helped to balance scientific accuracy and accessibility. Results: Nine consumers participated in the project, collaborating in three subgroups to co-design six resources (two per registry). These included one infographic, one fact sheet, one animation, one video and two booklets. The resources were shaped by consumer needs and preferences. Conclusion: This study demonstrates the value of co-design in translating registry outputs, emphasising the need for careful planning, expectation management and communication between stakeholders to ensure consumer-friendly and evidence-based resources are developed. Implications for health information management practice: This practical "how-to guide" documenting the co-design process will support broader adoption across registries and health organisations.

Background: The Better Outcomes Registry and Network Ontario Information System (BIS) has captured data on births in Ontario since 2012. Data and information quality is a foundational pillar of Ontario's birth registry.

Objective: To evaluate data quality and reliability, we compared birth data in the BIS with like data elements in the Canadian Institute for Health Information-Discharge-Abstract-Database (CIHI-DAD) which captures administrative, clinical, and demographic data on all hospital discharges.

Methods: We used unique pregnancy identifiers to deterministically link maternal records in the BIS to the CIHI-DAD in the fiscal years 2016-2017 to 2020-2021. Percent agreement and Cohen Kappa Coefficients (simple or weighted) with 95% confidence intervals (CI) assessed agreement on selected elements in both databases. Sensitivity analyses explored the impact of the COVID-19 pandemic on data entry and quality processes.

Results: There was excellent percentage agreement (⩾90%) between the two databases for all maternal elements assessed. Fourteen out of the twenty elements assessed indicated substantial (κ = 0.61-0.80) or almost perfect agreement (κ = 0.81-0.99) on Kappa tests. Sensitivity analyses restricting the linked cohort to data entered before (2016/2017-2019/2020) and during (2020/2021) the COVID-19 pandemic demonstrated no significant changes in agreement across all elements.

Conclusion: Overall, the BIS and CIHI-DAD databases had high agreement on most maternal data elements; however, further examination is necessary to explore discrepancies identified.Implications for health information management practice:As the BIS is newer than the CIHI-DAD and uses a different method of data abstraction, routinely evaluating and enhancing data quality is crucial for providing accurate and valid evidence for health policy, surveillance, and 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: Clinical Quality Registries (CQRs) capture clinical practice data to monitor the performance of health services against agreed standards of care. Ensuring data timeliness, completeness and reliability are challenges for CQRs, as data are prospectively collected and time sensitive. The Australian Stroke Clinical Registry (AuSCR) commenced in 2009 and includes 67 hospitals voluntarily collecting data on patients with acute stroke (at December 2024). Objective: To describe the methods used to ensure data quality in a national CQR, using the AuSCR as a case study. Method: Methods from the AuSCR were described against The Australian Framework for CQRs (2024), focusing on three operating principles for data quality: "Data collection," "Data elements" and "Ensuring data quality." Results: The AuSCR meets these principles through: (1) an online data platform to import data from primary sources and perform logic checks; (2) provision of comprehensive training, a data dictionary and user manuals for contributors; (3) medical record audits; (4) bi-annual hospital data quality reports and near real-time dashboards including data discrepancies; (5) cross-referencing data against government admissions data. Our processes extend to patient-reported follow-up data collected within 90-180 days of admission. Conclusion: Managing health information in a national CQR involves multiple methods to ensure data quality and minimise clinician data entry time. The AuSCR is an exemplar program to guide the field. Implications for health information management practice: CQRs are rapidly adopting streamlined processes to collect, manage and validate data to maximise the quality of health information for clinical practice improvement.

Background: Cancer is typically a notifiable disease, with notifications captured in population-based cancer registries (PBCR) to inform public health cancer control. Despite the importance of PBCRs, a knowledge gap exists regarding the impact of novel and advanced data engineering technologies on PBCR health information, data quality and utility.

Objective: To examine the impact of electronic reporting, machine learning and automation on PBCR data quality and utility.

Method: A mixed-methods, participatory, performance story evaluation was conducted in 2022 to examine data quality (completeness, coverage, timeliness and efficiency) and utility (real-time dashboards and research) of health information collected between 2012 and 2021 by the New South Wales Cancer Registry (NSWCR), a PBCR in New South Wales, Australia.

Results: A two-fold increase in cancer notifications was observed between 2012 and 2021 (n = +171,841; 103% increase). Electronic data receipt increased by 63-percentage points between 2015 and 2021 (12% to 75%), and the number of services that provided electronic data also increased during this time. Timeliness of data receipt improved between 2012 and 2021, with 87% (n = 293,544) received on time in 2021. Manual requests, data extraction and processing times decreased from 4791 to 483 requests (2012-2021) and 921 to 63 days (2017-2021). Utility was enhanced, as supported by collaboration. Greater confidence in population-level quality improvement initiatives was reported, and an increase in research activity and functionality observed.

Conclusion: Electronic reporting, machine learning and automation can improve data quality, utility and cancer control capability, with collaboration remaining essential.Implications for health information management practice:Innovative technologies and collaboration can improve PBCRs and strengthen health care, policy, research and health system capability.

Background: Due to their low prevalence and wide distribution, rare kidney diseases pose challenges in diagnosis and management. Registries play a key role in organising data to address these issues.

Objectives: This study aims to compare the characteristics of existing rare kidney disease registries to gain insights to create new registries or develop available ones.

Method: This scoping review was conducted in 2024. The searches were performed in four electronic databases: PubMed, Web of Science, Scopus and ProQuest, as well as the Orphanet website. Various components of disease registries were extracted and compared narratively.

Results: Our review of 1,534 papers revealed 29 rare kidney disease registries, and an additional 8 registries were retrieved from the Orphanet website, established between 1989 and 2023 in various countries. The objectives of these registries were categorised into clinical (15 registries), research (29 registries), and epidemiological ones (11 registries), among these, 15 and 20 registries were international and national, respectively. Data collected included demographic, medical, and family history, paraclinical, therapeutic, genetic testing, comorbidities and treatment outcomes. Various strategies were employed for data quality assurance, including the use of data quality indicators, audits and adherence to established guidelines. Some registries used both prospective and retrospective data collection methods.

Conclusion: Establishing standardised registries for rare kidney diseases is essential to ensure data consistency and comparability. Due to legal and ethical challenges, and regional differences in disease prevalence, each country or institution may need to create its own registry. Adhering to common standards for data collection, collaboration and establishment of integrated frameworks facilitate comprehensive and comparable data analysis.Implication for health information management practice:This study suggests that rare kidney disease registries should adopt patient-centred designs with clearly defined objectives and standardised data elements to enable meaningful data collection and analysis. Registries must implement strong data quality assurance mechanisms and foster inter-institutional collaboration to support clinical decision-making, and drive targeted research.