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

Background: Professional practice placements (work-integrated learning (WIL)) enable practical application of essential knowledge and skills, from the health information management university curricula, aligned to profession entry-level competencies.

Objectives: To (1) identify formal (explicit learning outcomes (ELOs)) and informal (derived learning outcomes (DLOs)) articulated in final-year, health information management WIL proposals, 2012-2021, at Australia's La Trobe University; and (2) map these to the contemporaneous, national Health Information Manager (HIM) profession entry-level competency (sub-)domains.

Method: A random sample (20%; n = 129) of 2012-2021 final-year placement proposals was interrogated using documentary analysis. ELOs and DLOs were extracted and categorised to the (sub-)domains of the Health Information Management Association of Australia's (2017) HIM Professional Competency Standards; between-group comparisons were made.

Results: Of the 129 proposals: 38 (29.5%) were absent ELOs; one had no project description; almost 74% were project-related. Predominant sub-domains in Domain A, "Generic professional skills": communication (in 63.7% of ELOs; 61.2% of DLOs); teamwork (40.7% ELOs; 50.8% DLOs). Predominant sub-domains in the eight profession-specific competency domains: "Health information services organisation and management" (Domain I; 60.5% ELOs; 100% DLOs); "Health information and records management" (Domain B; 54.9% ELOs; 56.3% DLOs); "Research methods" (Domain E; 45.1% ELOs; 44.5% DLOs). ELOs were most commonly aligned to four discipline-specific domains; DLOs were distributed across five.

Conclusion: Analysis of the knowledge-skills learning outcomes for final-year, student-HIM placements has generated recommendations to support agency supervisors in framing WIL project proposals.Implications for health information management practice:This research will support more robust WIL to complement student HIMs' academic education for competent, postgraduation practice.

Background: The introduction by the World Health Organization of specific International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) clinical codes for coronavirus disease 2019 (COVID-19) in early 2020 was key to standardising disease reporting and supporting global public health efforts. However, the concordance between these clinical codes and laboratory-confirmed COVID-19 cases based on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) results remains largely unexamined in Australia.

Objective: This study evaluated the concordance between ICD-10 Australian Modification (ICD-10-AM) code U07.1 (COVID-19, virus identified) and SARS-CoV-2 PCR test results in admitted patient records, to improve case identification.

Method: This retrospective study analysed routinely collected electronic medical record data from 13 public hospitals in New South Wales, Australia. Clinical coding of ICD-10-AM U07.1 was assessed using SARS-CoV-2 PCR results as the reference standard. Sensitivity, specificity, positive predictive value and negative predictive value were calculated. A mixed-effects logistic regression model was used to assess diagnostic concordance, adjusting for patient demographics.

Results: Among 25,724 admissions with a SARS-CoV-2 PCR test, 39.4% were confirmed COVID-19 cases based on positive SARS-CoV-2 PCR test results. The ICD-10-AM clinical coding of U07.1 demonstrated excellent accuracy, with a sensitivity of 91.5% (95% CI: 90.8-92.2%) and 94.1% (95% CI: 93.6-94.6%) compared to conventional and rapid PCR-confirmed cases, respectively.

Conclusion: The ICD-10-AM code U07.1 aligns well with SARS-CoV-2 PCR-confirmed cases, supporting its use as a reliable marker for COVID-19 in hospital data for surveillance and research purposes.Implications for health information management practice:Ongoing improvements in clinical coding practices are necessary to minimise misclassification and enhance accuracy for public health planning.

Background: ICD-11's digital architecture and granularity distinguish it from previous revisions and expand its applicability beyond mortality statistics and public health. The official ICD-11 version is updated annually. However, a separate online Maintenance Platform is continuously updated and hosts the Proposal Platform: a novel online tool that enables interested parties from all over the world to contribute to ICD-11 content. Anyone can register on the Platform to propose updates, such as adding new medical terms or improving existing descriptions, helping keep the classification relevant and inclusive. As a public, transparent system, users can view or comment on other users' proposals. Proposals are carefully reviewed by expert WHO committees through a transparent, multi-step process that ensures scientific accuracy and consistency. High-priority updates, like emerging health conditions, can be fast-tracked for quicker inclusion. Once a proposal is accepted, it becomes effective in the following update. A clear justification is provided for rejected proposals. Since ICD-11 came into effect, most suggestions from users have been successfully implemented.

Objective: This article describes the proposal submission process, the rigorous proposal review process, and the roles of the WHO reference groups and committees involved.

Conclusion: ICD-11 is a free, digital global health classification that anyone can help improve by submitting proposals through an open, transparent platform.Implications for health information management practice:This inclusive system empowers users worldwide to shape ICD-11 to reflect the evolving real-world medical and public health practice and emerging needs. This also prevents the need for country-specific modifications, ultimately improving the comparability of clinical data at the international level.

Background: Administrative health data are widely used for suicide attempt surveillance yet concerns remain about accuracy. The Victorian Emergency Minimum Dataset (VEMD) Human Intent Descriptor is an administrative coding system for classifying self-harm and suicidality in emergency department (ED) presentations. This study evaluates its accuracy in detecting suicide attempts by comparing it to clinician-applied Columbia Classification Algorithm of Suicide Assessment (C-CASA) ratings from medical records.

Method: This cross-sectional validation study examined 607 ED presentations referred to psychiatric triage across three hospitals in August 2020. C-CASA classifications were compared with corresponding VEMD Human Intent Descriptor data. Sensitivity, specificity, predictive values, likelihood ratios, and Cohen's kappa were calculated. Receiver operating characteristic (ROC) curves assessed overall discrimination.

Results: The VEMD descriptor demonstrated high specificity (99.0%) but low sensitivity (25.0%-27.3%), indicating many false negatives. The ROC analysis showed poor discriminatory ability (area under the curve = 0.62-0.63). Forty percent of missed cases were captured in ED diagnoses, highlighting gaps in coding accuracy.

Conclusion: While the VEMD descriptor reliably confirms suicide attempts, its poor sensitivity limits its utility for surveillance. Findings underscore the need for improved coding protocols and alternative detection strategies to enhance suicide attempt surveillance in ED settings.

Introduction: As health information systems (HIS) become a critical part of patient care, it is crucial to build an effective education strategy that facilitates the adoption and sustained use of these systems. The COVID-19 pandemic (2019-2023) has contributed to the rapid shift in virtual education and training for healthcare staff.

Objective: We sought to evaluate the efficacy and long-term sustainability of virtual training for using a HIS by examining opportunities and challenges.

Method: An exploratory, multimethods study was conducted with staff who had taken part in a virtual HIS training program as part of the clinical transformation journey at a large academic health science center in Canada. The study was guided by the Accelerating the Learning Cycle framework. Data were collected through pre- and post-training surveys, as well as semi-structured interviews. An iterative, inductive, constant comparative analysis approach, outlined by Braun and Clarke, was taken to thematically analyse the data.

Results: Of the 33 participants in this study, 13 were educational champions, and 20 were end-users. The pre- and post-training surveys yielded a total of 1479 responses in both groups. Three prominent themes emerged from this study: (1) fostering dynamic facilitation techniques to cultivate an inclusive culture and adapt to diverse learning needs; (2) integrating practical learning activities that contribute to knowledge retention; and (3) ensuring training resources are accessible and consistent for an optimal training experience.

Conclusion: As HIS continue to be part of the transformation of the healthcare ecosystem, education is vital in preparing healthcare providers to perform their clinical tasks and effectively use these technologies. Findings from this study can be used to inform the development of virtual training that is inclusive and addresses the needs of care providers.

Background: Advances in technology have increased the ease of reporting hospital incidents, resulting in large amounts of qualitative descriptive data. Health services have little experience analysing these data at scale to incorporate into routine reporting.

Objective: We aimed to explore the feasibility of applying a semi-automated content analysis (SACA) tool (Leximancer™) to qualitative descriptions of system-wide hospital incidents to provide insights into safety issues at all health service levels.

Method: Data from 1245 incidents reported across a network of hospitals in Australia were analysed using the SACA tool. Summaries were generated using a variety of techniques, including inductive and deductive approaches to extract key concepts in the data.

Results: The analysis was feasible and provided an actionable summary of the types of incidents reported in the data; the visual interface allowed users to explore the underlying text for a deeper understanding. Deductive analysis was utilised to explore specific areas of interest, and stratified analysis revealed more detailed concepts. The SACA tool was more efficient than manual processes; however, due to the context present in the incident descriptions, significant time, reading and subject matter expertise is still required to refine the analysis.

Conclusion: Semi-automated tools provide an opportunity for improving patient safety culture and practices by providing rapid content analysis of vast datasets that can be customised for specific organisational contexts and deployed at scale. Further research is required to assess usefulness with system users.

Implications: Qualitative data abound and system-wide analysis is essential to creating actionable insights.

Background: An increasing number of people are exploring their genetic predisposition to many diseases, allowing them to make healthcare decisions with improved knowledge. Objectives: The aim of this study was to identify factors that influence individuals to consider genetic testing utilising a modified health belief model (HBM). Method: The authors tested the modified HBM using a convenience sample of individuals from across the United States after a pilot study was used to test the validity and reliability of the constructs. Using SmartPLS, the researchers determined that the modified HBM explains the decision-making process used to determine what influences individuals to consider genetic testing. Results: Results suggested that perceived susceptibility, perceived benefits, cues to action, self-efficacy, e-health literacy and normative belief all play a role in an individual's decision to test their genetics. Conclusion: By conducting genetic testing, individuals may benefit from knowing they are predisposed to certain cancers and other diseases. Yet, research results have indicated that most individuals are unaware of resources available online that will help them in understanding genetic test results and associated diseases. Implications: Since healthcare literacy is an issue reported by these individuals, health information management professionals are well qualified to support them in e-health literacy by assisting them to evaluate the trustworthiness of available resources, and to educate them about privacy rights regarding access to and protection of their genetic information.

Background: The transition of systems to the International Statistical Classification of Diseases 11th Version (ICD-11) allows access to comprehensive data that accurately portray the complexity of morbidity and mortality data in Malaysia.

Objective: To demonstrate Malaysia's experience in implementing ICD-11, from data collection to downstream data use applications.Method and implementation:We describe improvements to existing data source systems and downstream data applications. For non-HIS and HIS (ICD-10) systems, data were manually entered into the health management information system equipped with ICD-11 or automatically mapped from ICD-10 to ICD-11. Following these system improvements, we collected and reported ICD-11 data from all hospitals nationwide, regardless of the individual systems' status in ICD-11 use.

Discussion: Lessons learnt related to legacy systems; ICD-11 releases and system updates; mapping; reporting; human resources and related applications.

Conclusion: With careful planning, standardisation of the collection and use of ICD-11 data can be accomplished with limited resources and in a complex environment with heterogeneous systems.

Implications: Use of ICD-11 data in downstream data applications improves data quality to answer specific business or research questions.

Background: Clinical coding is important for reimbursement, resource planning, administration and medical research. Objective: This study aimed to evaluate clinical coding accuracy and its influencing factors, especially the benefits of physician-clinical coder collaboration. Method: Twenty-four physicians and one senior clinical coder participated in the quality audit. The audit checklist, assessment criteria, training program and physician-clinical coder collaboration mechanism were clearly defined. The homepage filling standards, homepage filling guidelines and the guidelines of the International Classification of Diseases were used as the assessment criteria for evaluating accuracy. Results: A total of 323,320 medical records were reviewed. The average accuracy of homepage completion was 60.4% and poor-quality homepages accounted for 89.9% of coding errors. The average coding accuracy and correction percentage were 83.4% and 62.3%, respectively. After physician-clinical coder collaboration, the coding accuracy increased from 78.9% to 87.1% (χ² = 799.904, p < 0.001) and correction percentage increased from 52.0% to 73.0% (χ² = 1628.015, p < 0.001). Multivariate logistic regression revealed that complexity of medical records (odds ratio (OR) = 0.625), quality of homepages (OR = 20.445), month of physician-clinical coder collaboration (OR = 1.133-2.297), coder's major (OR = 1.616), coding experience (OR = 1.953), work engagement (OR = 1.290) and day of the week (OR = 1.054) were factors influencing coding accuracy. Conclusion: Physician-clinical coder collaboration effectively improved clinical coding accuracy and clinical coders benefited greatly. However, homepage quality was not improved. Furthermore, homepage quality and psychological factors influenced coding accuracy. Implications: Managers should implement regular standardised training for homepage completion, alongside ongoing improvements in coding practices and training.

Background: Effective documentation and coding in health care are crucial for patient care, safety, workflow improvement and accurate billing. Objectives: This quality improvement study aimed to enhance History and Physical (H&P) note documentation and charge capture processes to integrate coding and billing aspects, capture authentic work, preserve the H&P's integrity and align H&P-related revenue with actual performance. Method: A multidisciplinary team, including divisional leadership and specialists in documentation improvement, electronic health records, lean/six sigma methodology, a nocturnist and a senior-level physician coding auditor, initiated a quality improvement project. Educational efforts targeted approximately 50 hospitalists at a Departmental meeting in January 2023 (Department of Medicine, University of North Carolina School of Medicine), followed by the development and iterative testing of a standardised H&P note template in March 2023, officially disseminated to the entire Department in June 2023. Results: Despite limited impact from education alone, the implementation of an updated H&P template in May 2023 and department-wide distribution in June led to an immediate increase in average work relative value units (wRVU) per encounter, driven by enhanced capture of prolonged time codes and key medical decision-making phrases. The sustained correlation between template usage and increased wRVUs demonstrated a consistent, elevated plateau compared to the education phase. Conclusion: Collaboratively designed and user-informed note templates, balancing usability, efficiency and revenue-generating elements, proved more effective than education alone in integrating complex changes into clinical practice and enhancing coding and billing accuracy. Implications: Results of this study underscore the benefits of standardised documentation tools in enhancing both clinical and financial outcomes, suggesting that healthcare institutions could improve revenue capture, and documentation accuracy by adopting similar approaches.